A consensus-based distributed voltage control for reactive power sharing in microgrids - Schiffer, Johannes; Seel, Thomas; Raisch, Jorg; Sezi, Tevfik (2014 European Control Conference, ECC 2014, 2014)
We propose a consensus-based distributed voltage control (DVC), which solves the problem of reactive power sharing in autonomous meshed inverter-based microgrids with inductive power lines. Opposed to other control strategies available thus far, the DVC does guarantee reactive power sharing in steady-state while only requiring distributed communication among inverters, i.e. no central computing nor communication unit is needed. Moreover, we provide a necessary and sufficient condition for local exponential stability. In addition, the performance of the proposed control is compared to the usual voltage droop control [1] in a simulation example based on the CIGRE benchmark medium voltage distribution network.
CRIS-Team Scopus Importer:2021-03-31

A Decoupling Servo Pressure Controller for Pneumatic Muscle Actuators - Martens, Mirco; Seel, Thomas; Boblan, Ivo (2018 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018, 2018)
A pressure servo controller for pneumatic muscle actuators (PMAs) is presented and tested in three test scenarios. Unlike most previous non-linear control approaches, the current contribution demonstrated that a linear controller design becomes feasible after a slight reformulation of the non-linear plant dynamics. The controller is designed by pole placement in the discrete-time domain. It is demonstrated experimentally that the controller solves the control task and compensates pneumatic cross-coupling effects between different PMAs connected to a shared kinematic structure with an additional disturbance cancellation. When the initial PMA volume is kept constant, a closed-loop cutoff frequency of about 6 Hz is achieved. Beyond that, a sine pressure trajectory with 1 Hz is tracked successfully at time-varying volume, and the pressure changes triggered by volume changes are significantly reduced. The controller is easy to implement and therefore suitable to run on low-cost embedded hardware.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Neumatic artificial muscles; Pneumatic muscle actuators; Pneumatic robots; Pressure control

Advanced technology for gait rehabilitation: An overview - Mikolajczyk, Tadeusz; Ciobanu, Ileana; Badea, Doina Ioana; Iliescu, Alina; Pizzamiglio, Sara; Schauer, Thomas; Seel, Thomas; Seiciu, Petre Lucian; Turner, Duncan L.; Berteanu, Mihai (Advances in Mechanical Engineering, 2018)
Most gait training systems are designed for acute and subacute neurological inpatients. Many systems are used for relearning gait movements (nonfunctional training) or gait cycle training (functional gait training). Each system presents its own advantages and disadvantages in terms of functional outcomes. However, training gait cycle movements is not sufficient for the rehabilitation of ambulation. There is a need for new solutions to overcome the limitations of existing systems in order to ensure individually tailored training conditions for each of the potential users, no matter the complexity of his or her condition. There is also a need for a new, integrative approach in gait rehabilitation, one that encompasses and addresses all aspects of physical as well as psychological aspects of ambulation in real-life multitasking situations. In this respect, a multidisciplinary multinational team performed an overview of the current technology for gait rehabilitation and reviewed the principles of ambulation training.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Ambulation; gait rehabilitation; mechatronics; rehabilitation medicine; rehabilitation technology; robotics

Alignment-Free, Self-Calibrating Elbow Angles Measurement Using Inertial Sensors - Mueller, Philipp; Begin, Marc-Andre; Schauer, Thomas; Seel, Thomas (IEEE Journal of Biomedical and Health Informatics, 2017)
Due to their relative ease of handling and low cost, inertial measurement unit (IMU)-based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis, and rehabilitation (e.g., Parkinson's disease monitoring or poststroke assessment). However, a major downside of current algorithms, recomposing human kinematics from IMU data, is that they require calibration motions and/or the careful alignment of the IMUs with respect to the body segments. In this article, we propose a new method, which is alignment-free and self-calibrating using arbitrary movements of the user and an initial zero reference arm pose. The proposed method utilizes real-time optimization to identify the two dominant axes of rotation of the elbow joint. The performance of the algorithm was assessed in an optical motion capture laboratory. The estimated IMU-based angles of a human subject were compared to the ones from a marker-based optical tracking system. The self-calibration converged in under 9.5 s on average and the rms errors with respect to the optical reference system were 2.7° for the flexion/extension and 3.8° for the pronation/supination angle. Our method can be particularly useful in the field of rehabilitation, where precise manual sensor-to-segment alignment as well as precise, predefined calibration movements are impractical.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Elbow tracking; Inertial measurement units (IMU); two degree of freedom (2DOF) joint; upper limb motion

Alignment-free, self-calibrating elbow angles measurement using inertial sensors - Muller, Philipp; Begin, Marc Andre; Schauer, Thomas; Seel, Thomas (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, 2016)
Due to their relative ease of handling and low-cost, inertial measurement unit (IMU) based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis and rehabilitation (e.g. Parkinson's disease monitoring or post-stroke assessment). However, a major downside of current algorithms recomposing human kinematics from IMU data is that they require calibration motions and/or the careful alignment of the IMUs respective to their body segment. In this article, we propose a new method, which is alignment free and self-calibrated using the arbitrary movements of the user and an initial zero reference arm pose. The proposed method utilizes real time optimization to identify the two dominant axes of rotation of the elbow joint. Using a two degree of freedom joint mimicking the human elbow, the performance of the algorithm was assessed by comparing the angles obtained from two IMUs to the ones obtained from a marker-based optical tracking system. The self-calibration proved to converge within seconds and the RMS errors with respect to the optical reference system were below 5°. Our method can be particularly useful in the field of telerehabilitation, where precise manual sensor to segment alignment as well as precise, predefined calibration movements are impractical.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Elbow tracking; inertial measurement units (IMU); two degree of freedom (2DOF) joint; upper limb motion

A Method for Lower Back Motion Assessment Using Wearable 6D Inertial Sensors - Molnar, Marco; Kok, Manon; Engel, Tilman; Kaplick, Hannes; Mayer, Frank; Seel, Thomas (2018 21st International Conference on Information Fusion, FUSION 2018, 2018)
Low back pain (LBP) is a leading cause of activity limitation. Objective assessment of the spinal motion plays a key role in diagnosis and treatment of LBP. We propose a method that facilitates clinical assessment of lower back motions by means of a wireless inertial sensor network. The sensor units are attached to the right and left side of the lumbar region, the pelvis and the thighs, respectively. Since magnetometers are known to be unreliable in indoor environments, we use only 3D accelerometer and 3D gyroscope readings. Compensation of integration drift in the horizontal plane is achieved by estimating the gyroscope biases from automatically detected initial rest phases. For the estimation of sensor orientations, both a smoothing algorithm and a filtering algorithm are presented. From these orientations, we determine three-dimensional joint angles between the thighs and the pelvis and between the pelvis and the lumbar region. We compare the orientations and joint angles to measurements of an optical motion tracking system that tracks each skin-mounted sensor by means of reflective markers. Eight subjects perform a neutral initial pose, then flexion/extension, lateral flexion, and rotation of the trunk. The root mean square deviation between inertial and optical angles is about one degree for angles in the frontal and sagittal plane and about two degrees for angles in the transverse plane (both values averaged over all trials). We choose five features that characterize the initial pose and the three motions. Interindividual differences of all features are found to be clearly larger than the observed measurement deviations. These results indicate that the proposed inertial sensor-based method is a promising tool for lower back motion assessment.
CRIS-Team Scopus Importer:2021-03-31
Keywords: avoid magnetometers; back motion assessment; drift correction; human motion analysis; Inertial measurement units; joint angle estimation; low back pain; validation against optical motion capture

A new approach for a Patient-Cooperative Upper Limb FES Support based on Vector Fields - Passon, Arne; Klewe, Tim; Seel, Thomas; Schauer, Thomas (IFAC-PapersOnLine, 2017)
Rehabilitation robotics and Functional Electrical Stimulation (FES) are becoming more important in the rehabilitation of stroke and spinal cord injured (SCI) patients. Patient-cooperative control strategies help to only compensate for deficits and to not support too much. The application of a cable-driven arm robotic system with constant force support is considered. FES of the biceps and triceps, as well as of the posterior and anterior part of the deltoid muscle allows us to control the flexion and extension of the elbow joint and of the shoulder motion in the transversal plane. In order to support these motions of the patient, we introduce a novel assist-as-needed FES support based on iterative learning vector fields. Using this new FES control scheme, we aim to facilitate the patient's timing and completion of the motion. The approach is evaluated in experimental trials with healthy subjects performing a breaststroke swimming motion. Starting from a patient-typical deviation of 50° the controller automatically adapts the support by adjusting the stimulation and thereby reduces the deviation to approximately 15° within less than ten strokes.
CRIS-Team Scopus Importer:2021-03-31
Keywords: assist-as-needed; biomedical systems; Functional Electrical Stimulation; learning control systems; multivariable control systems; neuroprosthetics; patient-cooperative; rehabilitation robotics; upper limb rehabilitation

An Inertial Sensor-based Trigger Algorithm for Functional Electrical Stimulation-Assisted Swimming in Paraplegics - Wiesener, Constantin; Seel, Thomas; Axelgaard, Jens; Horton, Rachel; Niedeggen, Andreas; Schauer, Thomas (IFAC-PapersOnLine, 2019)
Functional electrical stimulation (FES) is used to support gait in stroke patients and to induce cycling motions in paralyzed legs. In the current contribution, we present a method that, for the first time, enables FES-supported swimming in paraplegics. The proposed setup includes a waterproof stimulator, cables, and electrodes. In preliminary experiments, flexion and extension movements of the knee were generated in a completely paralyzed subject to support the propulsion. Furthermore, transcutaneous spinal cord stimulation (tSCS) is used to get a straight swimming position and to reduce spasticity of the lower extremities. The developed setting remained dry and safe during all sessions. The first trials revealed the need for a synchronization of the patient's arm movements with the artificially induced leg movements to prevent undesired rolling movements of the swimmer. To enable such a synchronized swimming, a trigger algorithm was developed that is based on the roll angle and angular velocity of the trunk. By experimental validation, it was demonstrated that a functional stimulation pattern can be generated during front crawl movements of the upper body. The new setup and methods are currently being tested during the STIMSWIM pilot study with paraplegics. The preliminary results of the first two subjects show an improvement of the swimming speed of approximately 15% for FES/tSCS assisted swimming compared to non-assisted swimming and a clear training effect over the first 7 sessions.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Assistive devices; Neurostimulation

A novel framework for a systematic integration of pneumatic-muscle-actuator-driven joints into robotic systems via a torque control interface - Martens, Mirco; Seel, Thomas; Zawatzki, Johannes; Boblan, Ivo (Actuators, 2018)
In this paper, two different torque control approaches for PMA-driven (PMA = Pneumatic muscle actuator) revolute joints are presented and tested. In previous work controllers for PMA-driven robots are typically customized for the use on a specific robotic system. In contrast, the proposed controllers define a general control interface for every robot that is actuated by PMA-driven joints. It will be shown that controlling the torque of a PMA-driven joint enables the use of standard robotic motion control frameworks, because the torque represents the natural input of the robotic equation of motion. Therefore, both proposed torque control approaches are interconnecting PMAs and their challenging characteristics on the one hand and “conventional” motion control strategies for robots on the other hand. After a detailed discussion of two different torque control approaches, we show that a torque controller handles all characteristics and dynamics of a PMA-driven joint internally, which implies that only its bandwidth and its static torque characteristic must be taken into account for the design of the outer motion control loop. This feature simplifies the integration of PMA-driven joints in robotic systems enormously, as will be demonstrated by a design of a cascade-structured, flatness-based motion controller for an exemplary robot with one degree of freedom.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Pneumatic artificial muscle (PAM); Pneumatic muscle actuator (PMA); Pneumatic robot; Pneumatic system; Pneumatic-muscle-actuator-driven joint

A novel IMU extrinsic calibration method for mass production land vehicles - Rodrigo Marco, Vicent; Kalkkuhl, Jens; Raisch, Joerg; Seel, Thomas (Sensors, 2021)
Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the mis-alignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degrada-tion. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Automotive industry; Autonomous driving; Extrinsic calibration; Inertial sensors; Kalman filter; Motion estimation; Nonlinear systems; Odometry; Simultaneous state and parameter estimation; Systems and control engineering

A pneumatic-muscle-actuator-driven knee rehabilitation device for CAM therapy - Martens, Mirco; Zawatzki, Johannes; Seel, Thomas; Boblan, Ivo (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019)
In this paper a novel knee joint rehabilitation device made for controlled active motion (CAM) therapy is presented and tested. More precisely, the system is a redesign of an originally passive CAM device, called CAMOped. Instead of a break, the adjustable resistance, which is needed for CAM therapy, is now provided via a torque-controlled pneumatic-muscle-actuator-driven joint. These actuators are inherently compliant and can produce both a variable restistance and, by co-contraction, a variable stiffness. It will be shown that, by measuring the foot contact forces and using them as feedback information, the foot load can be adjusted very precisely up to 100 N. Furthermore, it will be demonstrated that, in contrast to passive systems, the presented active systems is capable of varying the resistance while the device is in use. This facilitates adapting the resistance to the patient's needs in real time and to use joint-angle-or foot-position-dependent resistance curves.
CRIS-Team Scopus Importer:2021-03-31

A tangible solution for hand motion tracking in clinical applications - Salchow-Hoemmen, Christina; Callies, Leonie; Laidig, Daniel; Valtin, Markus; Schauer, Thomas; Seel, Thomas (Sensors, 2019)
Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Dual quaternions; Functional electrical stimulation; Hand and finger kinematics; Hand tracking; Inertial measurement unit; Inertial sensor; Magnetic disturbances; Real-time motion tracking; Rehabilitation

A transferable force controller based on prescribed performance for contact establishment in robotic assembly tasks∗ - Halt, Lorenz; Pan, Fengjunjie; Tenbrock, Philipp; Pott, Andreas; Seel, Thomas (IEEE International Conference on Automation Science and Engineering, 2019)
In industrial robotics, controller parameters for force control must be adjusted to the specific robot that performs a task and they must be re-adjusted when the same task is to be performed by another robot. We address this challenge by proposing a transferable force controller for contact establishment between robot and surface. The controller is implemented based on task frame formalism. The proposed controller is based on prescribed performance control (PPC) and does not rely on a dynamic model of the environment. Due to the inherent robustness of PPC, it can be used to ensure similar performance for the same task across different robots and environments. The proposed controller is validated experimentally in a simple contact establishment task performed by three different robots (Universal Robots UR5, Franka Emika Panda, Denso Wave VS087) and three different board materials providing different stiffness (steel, aluminum, PVC). The PPC is found to yield an up to two orders of magnitude smaller variance of closed-loop settling time across all robots and materials than a conventional impedance controller.
CRIS-Team Scopus Importer:2021-03-31

Automatic anatomical calibration for IMU-based elbow angle measurement in disturbed magnetic fields - Laidig, Daniel; Müller, Philipp; Seel, Thomas (Current Directions in Biomedical Engineering, 2017)
Inertial Measurement Units (IMUs) are increasingly used for human motion analysis. However, two major challenges remain: First, one must know precisely in which orientation the sensor is attached to the respective body segment. This is commonly achieved by accurate manual placement of the sensors or by letting the subject perform tedious calibration movements. Second, standard methods for inertial motion analysis rely on a homogeneous magnetic field, which is rarely found in indoor environments. To address both challenges, we introduce an automatic calibration method for joints with two degrees of freedom such as the combined radioulnar and elbow joint. While the user performs arbitrary movements, the method automatically identifies the sensor-to-segment orientations by exploiting the kinematic constraints of the joint. Simultaneously, the method identifies and compensates the influence of magnetic disturbances on the sensor orientation quaternions and the joint angles. In experimental trials, we obtain angles that agree well with reference values from optical motion capture. We conclude that the proposed method overcomes mounting and calibration restrictions and improves measurement accuracy in indoor environments. It therefore improves the practical usability of IMUs for many medical applications.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Automatic anatomical calibration; Elbow joint; Kinematic constraints; Sensor-to-segment calibration

Automatic pairing of inertial sensors to lower limb segments – A plug-and-play approach - Graurock, David; Schauer, Thomas; Seel, Thomas (Current Directions in Biomedical Engineering, 2016)
Inertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Automatic calibration; Inertial sensor networks; Realtime gait analysis; Sensor-to-segment pairing; Wireless inertial measurement units

Autonomous Cycle Time Reduction of Robotic Tasks Using Iterative Learning Control - Halt, Lorenz; Meindl, Michael; Bayer, Victor; Kraus, Werner; Seel, Thomas (2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022)
CRIS-Team WoS Importer:2023-03-17

Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics - Meindl, Michael; Lehmann, Dustin; Seel, Thomas (Frontiers in Robotics and AI, 2022)
This work addresses the problem of reference tracking in autonomously learning robots with unknown, nonlinear dynamics. Existing solutions require model information or extensive parameter tuning, and have rarely been validated in real-world experiments. We propose a learning control scheme that learns to approximate the unknown dynamics by a Gaussian Process (GP), which is used to optimize and apply a feedforward control input on each trial. Unlike existing approaches, the proposed method neither requires knowledge of the system states and their dynamics nor knowledge of an effective feedback control structure. All algorithm parameters are chosen automatically, i.e. the learning method works plug and play. The proposed method is validated in extensive simulations and real-world experiments. In contrast to most existing work, we study learning dynamics for more than one motion task as well as the robustness of performance across a large range of learning parameters. The method’s plug and play applicability is demonstrated by experiments with a balancing robot, in which the proposed method rapidly learns to track the desired output. Due to its model-agnostic and plug and play properties, the proposed method is expected to have high potential for application to a large class of reference tracking problems in systems with unknown, nonlinear dynamics.
CRIS-Team Scopus Importer:2022-08-12
Keywords: autonomous systems; Gaussian processes (GP); iterative learning control; nonlinear systems; reinforcement learning; robot learning

BROAD – A Benchmark for Robust Inertial Orientation Estimation - Laidig, Daniel; Caruso, Marco; Cereatti, Andrea; Seel, Thomas (Data, 2021)
Inertial measurement units (IMUs) enable orientation, velocity, and position estimation in several application domains ranging from robotics and autonomous vehicles to human motion capture and rehabilitation engineering. Errors in orientation estimation greatly affect any of those motion parameters. The present work explains the main challenges in inertial orientation estimation (IOE) and presents an extensive benchmark dataset that includes 3D inertial and magnetic data with synchronized optical marker-based ground truth measurements, the Berlin Robust Orientation Estimation Assessment Dataset (BROAD). The BROAD dataset consists of 39 trials that are conducted at different speeds and include various types of movement. Thereof, 23 trials are performed in an undisturbed indoor environment, and 16 trials are recorded with deliberate magnetometer and accelerometer disturbances. We furthermore propose error metrics that allow for IOE accuracy evaluation while separating the heading and inclination portions of the error and introduce welldefined benchmark metrics. Based on the proposed benchmark, we perform an exemplary case study on two widely used openly available IOE algorithms. Due to the broad range of motion and disturbance scenarios, the proposed benchmark is expected to provide valuable insight and useful tools for the assessment, selection, and further development of inertial sensor fusion methods and IMU-based application systems.
CRIS-Team Scopus Importer:2021-07-30
Keywords: Attitude estimation; Benchmark dataset; Inertial measurement unit; Inertial sensor; Magnetic disturbances; Orientation estimation

Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks - Meindl, Michael; Molinari, Fabio; Lehmann, Dustin; Seel, Thomas (IEEE Transactions on Control Systems Technology, 2021)
Multi-agent systems (MASs) can autonomously learn to solve previously unknown tasks by means of each agent's individual intelligence as well as by collaborating and exploiting collective intelligence. This article considers a group of autonomous agents learning to track the same given reference trajectory in a possibly small number of trials. We propose a novel collective learning control method that combines iterative learning control (ILC) with a collective update strategy. We derive conditions for desirable convergence properties of such systems. We show that the proposed method allows the collective to combine the advantages of the agents' individual learning strategies and thereby overcomes trade-offs and limitations of single-agent ILC. This benefit is achieved by designing a heterogeneous collective, i.e., a different learning law is assigned to each agent. All theoretical results are confirmed in simulations and experiments with two-wheeled-inverted-pendulum robots (TWIPRs) that jointly learn to perform the desired maneuver.
CRIS-Team WoS Importer:2021-12-31

COMTEST Project: A Complete Modular Test Stand for Human and Humanoid Posture Control and Balance - Lippi, Vittorio; Mergner, Thomas; Seel, Thomas; Maurer, Christoph (IEEE-RAS International Conference on Humanoid Robots, 2019)
This work presents a system to benchmark humanoid posture control and balance performances under perturbed conditions. The specific benchmarking scenario consists, for example, of balancing upright stance while performing voluntary movements on moving surfaces. The system includes a motion platform used to provide the perturbation, an innovative body-Tracking system suitable for robots, humans and exoskeletons, control software and a set of predefined perturbations, a humanoid robot used to test algorithms, and analysis software providing state of the art data analysis used to provide quantitative measures of performance. In order to provide versatility, the design of the system is oriented to modularity: All its components can be replaced or extended according to experimental needs, adding additional perturbation profiles, new evaluation principles, and alternative tracking systems. It will be possible to use the system with different kinds of robots and exoskeletons as well as for human experiments aimed at gaining insights into human balance capabilities.
CRIS-Team Scopus Importer:2021-03-31

Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease (J Neuroeng Rehabil (2019) 16:77 DOI: 10.1186/s12984-019-0548-2) - Nguyen, An; Roth, Nils; Haji Ghassemi, Nooshin; Hannink, Julius; Seel, Thomas; Klucken, Jochen; Gaßner, Heiko; Eskofier, Björn (Journal of neuroEngineering and rehabilitation, 2019)
The original article [1] contained an error whereby Fig. 6 contained a minor shading glitch affecting its presentation. This has now been corrected.
CRIS-Team Scopus Importer:2019-08-06

Data-Driven Dynamic Input Transfer for Learning Control in Multi-Agent Systems with Heterogeneous Unknown Dynamics - Lehmann, Dustin; Drebinger, Philipp; Seel, Thomas; Raisch, Jörg (Proceedings of the IEEE Conference on Decision and Control, 2023)
Learning input signals that make a dynamic system respond with a desired output is often data intensive and time consuming. It is therefore natural to ask whether, in a heterogeneous multi-agent scenario, an input signal learned by one agent can be suitably adapted and transferred to make the other agents respond with the same desired output, despite exhibiting different dynamics. In this paper, we propose a novel method to achieve this by employing a dynamic input transfer map. The method does not require any a-priori knowledge of the individual agents' dynamics. Instead, a small amount of experimental data from the source and target systems are used to estimate the transfer map. We evaluate the proposed method and compare it to existing approaches using static input transfer maps by investigating two example scenarios: (i) a simulation scenario for muscle dynamics, (ii) an experimental setting with a group of two-wheeled inverted pendulum robots and a sim-to-real motion learning problem.
CRIS-Team Scopus Importer:2024-12-16

Deriving kinematic quantities from accelerometer readings for assessment of functional upper limb motions - Laidig, Daniel; Seel, Thomas (Current Directions in Biomedical Engineering, 2017)
Wearable accelerometers are lightweight, affordable, and allow for even smaller form factors than 9D inertial measurement units. They are therefore a promising tool for assessing the quality of movement of patients during daily life activities. While generic signal features such as signal power and frequency content are widely used, the derivation of kinematic (angular and spatial) quantities remains a challenge. We consider a chain of body segments, such as the arm, equipped with 3D accelerometers and propose a method for calculation of the inclination and relative height of the distal segment. For validation of the method against an optical motion capture system, we consider a setup with accelerometers on the forearm and the upper arm of a subject, who performs a sequence of drinking motions and pick-and-place motions. We obtain a root-mean-square deviation of about 2.5 cm for the wrist height relative to the shoulder and about 6° for the inclination angles of the forearm. We conclude that the proposed method yields measurements of kinematic quantities that are accurate enough for classification of functional versus non-functional motions or well-performed motions versus incomplete motions.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Accelerometer; Activities of daily living; Biomechanics; Inertial motion capture; Limb motion analysis; Rehabilitation

Design of a Networked Controller for a Two-Wheeled Inverted Pendulum Robot - Music, Zenit; Molinari, Fabio; Gallenmueller, Sebastian; Ayan, Onur; Zoppi, Samuele; Kellerer, Wolfgang; Carle, Georg; Seel, Thomas; Raisch, Joerg (IFAC-PapersOnLine, 2019)
The topic of this paper is to use an intuitive model-based approach to design a networked controller for a recent benchmark scenario. The benchmark problem is to remotely control a two-wheeled inverted pendulum robot via W-LAN communication. The robot has to keep a vertical upright position. Incorporating wireless communication in the control loop introduces multiple uncertainties and affects system performance and stability. The proposed networked control scheme employs model predictive techniques and deliberately extends delays in order to make them constant and deterministic. The performance of the resulting networked control system is evaluated experimentally with a predefined benchmarking experiment and is compared to local control involving no delays.
CRIS-Team Scopus Importer:2021-03-31

Design of an iterative learning control with a selective learning strategy for swinging up a pendulum - Beuchert, Jonas; Raisch, Jörg; Seel, Thomas (2018 European Control Conference, ECC 2018, 2018)
Swinging up a pendulum on a cart is a well-known demonstration example for trajectory tracking in a nonlinear system. The standard realtime feedback control approach fails if the plant output is not available in real time, e.g. due to large or variable measurement delays. However, the task can be solved in multiple trials by applying feedforward inputs that are improved from trial to trial by Iterative Learning Control (ILC). Our examination demonstrates that an ILC can be used for trajectory tracking close to the singularities and the unstable equilibrium of a non-linear system. Specifically, we present an ILC algorithm for pendulum swing-up by angle trajectory tracking. The controller design is based on a modified plant inversion approach that restricts the learning process to trajectory segments with small tracking errors and sufficient input sensitivity. We show that these restrictions lead to improved learning progress in contrast to conventional learning from the complete trajectory. Controller performance is evaluated in an experimental testbed. The ILC starts from a zero-input trajectory and learns to swing up the pendulum within six iterations. Robustness is analyzed experimentally, and the performance is compared to literature results. The convergence is at least two orders of magnitude faster than the one achieved by other methods that avoid feedback and do not rely on a suitable initial input trajectory.
CRIS-Team Scopus Importer:2021-03-31

Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease - Nguyen, An; Roth, Nils; Haji Ghassemi, Nooshin; Hannink, Julius; Seel, Thomas; Klucken, Jochen; Gaßner, Heiko; Eskofier, Björn (Journal of neuroEngineering and rehabilitation, 2019)
BackgroundGait symptoms and balance impairment are characteristic indicators for the progression in Parkinson's disease (PD). Current gait assessments mostly focus on straight strides with assumed constant velocity, while acceleration/deceleration and turning strides are often ignored. This is either due to the set up of typical clinical assessments or technical limitations in capture volume. Wearable inertial measurement units are a promising and unobtrusive technology to overcome these limitations. Other gait phases such as initiation, termination, transitioning (between straight walking and turning) and turning might be relevant as well for the evaluation of gait and balance impairments in PD.MethodIn a cohort of 119 PD patients, we applied unsupervised algorithms to find different gait clusters which potentially include the clinically relevant information from distinct gait phases in the standardized 4x10 m gait test. To clinically validate our approach, we determined the discriminative power in each gait cluster to classify between impaired and unimpaired PD patients and compared it to baseline (analyzing all straight strides).ResultsAs a main result, analyzing only one of the gait clusters constant, non-constant or turning led in each case to a better classification performance in comparison to the baseline (increase of area under the curve (AUC) up to 19% relative to baseline). Furthermore, gait parameters (for turning, constant and non-constant gait) that best predict motor impairment in PD were identified.ConclusionsWe conclude that a more detailed analysis in terms of different gait clusters of standardized gait tests such as the 4x10 m walk may give more insights about the clinically relevant motor impairment in PD patients.
CRIS-Team WoS Importer:2019-07-12

Development of Adapted Guitar to Improve Motor Function after Stroke: Feasibility Study in Young Adults - Dechenaud, Marcelline; Laidig, Daniel; Seel, Thomas; Gilbert, Hunter B.; Kuznetsov, Nikita A. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019)
Recent research indicates that music-supported therapies may offer unique benefits for rehabilitation of motor function after stroke. We designed an adapted guitar and training task aimed to improve coordination between rhythmic and discrete movements because individuals recovering from stroke have greater difficulty performing discrete vs. rhythmic movements. In this paper, we report a feasibility study on training to play this adapted guitar in healthy young adults. Subjects (N = 10) practiced two rhythmic strumming patterns over three consecutive days using their non-dominant hand guided by an audiovisual metronome (60 bpm). They were also instructed to press a foot pedal while maintaining the strumming movement. Elbow and wrist kinematics were estimated using wireless inertial measurement units. Results showed positive mean asynchrony between strum onsets and metronome onsets, and a decrease in the standard deviation of mean asynchrony over practice. In early practice, participants slowed the strumming movement when they pressed the foot pedal, but this interference decreased on days two and three. Smoothness of the elbow movement during the strum phase, which includes the contact with the strings, increased over practice, while smoothness of the return phase did not change over practice. The predominant joint coordination pattern used for the strum phase consisted of elbow extension coupled with elbow pronation, wrist extension, and ulnar deviation. We discuss how these results fit into current music-based rehabilitation literature and outline directions for future applications of this music-supported intervention.
CRIS-Team Scopus Importer:2021-03-31

Development of a home-based wrist range-of-motion training system for children with cerebral palsy - Mittag, Christina; Leiss, Regina; Lorenz, Katharina; Seel, Thomas (At-Automatisierungstechnik, 2020)
This paper presents the proof-of-concept of a home-based gamified wrist rehabilitation training system for children with cerebral palsy (CCP). We describe the user-centered design process of this system, which is composed of a wrist-worn inertial measurement unit (IMU) and a tangible device with an embedded IMU. The system employs a quaternion-based algorithm for automatic real-time estimation of the range of motion (RoM) covered by adduction/abduction and flexion/extension motions of the wrist. Experimental validation shows that the RoM can be determined with sufficient accuracy to control a game and that the algorithm is applicable in CCP. A serious game, which uses the presented algorithm and enables feedback as well as motivating stimuli, is implemented and evaluated by physiotherapists.
CRIS-Team Scopus Importer:2021-03-31
Keywords: home-based motor training; human-technology interaction; inertial sensor fusion; intelligent biomedical systems; motion state estimation; rehabilitation engineering; serious game design; user-centered design

Development of a novel functional electrical stimulation system for hand rehabilitation using feedback control - Ruppel, Mirjana; Seel, Thomas; Dogramadzi, Sanja (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2016)
Worldwide stroke is a leading cause for disability and the demand for rehabilitation devices and everyday life assistance is still high. To address this demand, we consider a combination of Functional Electrical Stimulation (FES) of the forearm muscles and Iterative Learning Control (ILC) for hand rehabilitation. We implement an ILC scheme to control the extension and flexion of the paretic fingers via two FES channels and propose a piecewise linear parametrization of the domain of admissible stimulation intensities.
CRIS-Team Scopus Importer:2021-03-31

Distributed Bio-inspired Humanoid Posture Control - Lippi, Vittorio; Molinari, Fabio; Seel, Thomas (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019)
This paper presents an innovative distributed bio-inspired posture control strategy for a humanoid, employing a balance control system DEC (Disturbance Estimation and Compensation). Its inherently modular structure could potentially lead to conflicts among modules, as already shown in literature. A distributed control strategy is presented here, whose underlying idea is to let only one module at a time perform balancing, whilst the other joints are controlled to be at a fixed position. Modules agree, in a distributed fashion, on which module to enable, by iterating a max-consensus protocol. Simulations performed with a triple inverted pendulum model show that this approach limits the conflicts among modules while achieving the desired posture and allows for saving energy while performing the task. This comes at the cost of a higher rise time.
CRIS-Team Scopus Importer:2021-03-31

Eliminating the Effect of Magnetic Disturbances on the Inclination Estimates of Inertial Sensors - Seel, Thomas; Ruppin, Stefan (IFAC-PapersOnLine, 2017)
Inertial measurement units (IMUs) are a promising tool for realtime analysis of human motion in many application domains. We consider the standard sensor fusion problem in IMU orientation estimation. We highlight the malicious effects that inhomogeneous magnetic fields, which are often found in indoor environments, can have on the inclination portions (roll and pitch) of the orientation estimate. We then propose a novel method that uses an analytical solution of the sensor fusion problem and purely horizontal magnetometer-based corrections. The method assures that magnetic field measurements affect only the heading (yaw) component of the orientation estimate. Furthermore, we parametrize the algorithm such that the user can choose the time constant and aggressiveness with which the algorithm balances between gyroscope drift compensation and rejection of disturbances caused by inhomogeneous magnetic fields or by velocity changes.
CRIS-Team Scopus Importer:2021-03-31
Keywords: alternating magnetic fields; biomedical engineering; human motion analysis; inertial measurement units; magnetic disturbance; motion estimation; orientation estimation; quaternion; sensor fusion

Entwurf einer lernenden Kaskadenregelung für ein nichtinvasives kontinuierliches Blutdruckmesssystem - Seel, Thomas; Schneider, Sarah; Affeld, Klaus; Schauer, Thomas (At-Automatisierungstechnik, 2015)
For a novel blood pressure measurement system, we design a cascaded controller that consists of two classic feedback loops and an iterative learning algorithm.We show that the bandwidth of the feedback loops is limited by the measurement noise of the ultrasound sensor and that this restriction can be circumvented by exploiting the repetitive nature of the disturbance that is induced by the oscillating arterial pressure.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biomedical engineering; Blood pressure measurement; Cascade control; Iterative learning control; System identification

Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks - Laidig, Daniel; Trimpe, Sebastian; Seel, Thomas (Current Directions in Biomedical Engineering, 2016)
We examine the usefulness of event-based sampling approaches for reducing communication in inertial-sensor-based analysis of human motion. To this end we consider realtime measurement of the knee joint angle during walking, employing a recently developed sensor fusion algorithm. We simulate the effects of different event-based sampling methods on a large set of experimental data with ground truth obtained from an external motion capture system. This results in a reduced wireless communication load at the cost of a slightly increased error in the calculated angles. The proposed methods are compared in terms of best balance of these two aspects. We show that the transmitted data can be reduced by 66% while maintaining the same level of accuracy.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Adaptive sampling; Event-based sampling; Human motion analysis; IMU; Inertial sensor networks; Send-on-area; Send-on-delta

Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors - Laidig, Daniel; Schauer, Thomas; Seel, Thomas (IEEE International Conference on Rehabilitation Robotics, 2017)
Inertial Measurement Units (IMUs) have become a widely used tool for rehabilitation and other application domains in which human motion is analyzed using an ambulatory or wearable setup. Since the magnetic field is inhomogeneous in indoor environments and in the proximity of ferromagnetic material, standard orientation estimation and joint angle calculation algorithms often lead to inaccurate or even completely wrong results. One approach to circumvent this is to exploit the kinematic constraint that is induced by mechanical hinge joints and also by approximate hinge joints such as the knee joint and the finger (interphalangeal) joints of the human body. We propose a quaternion-based method for joint angle measurement for approximate hinge joints moving through inhomogeneous magnetic fields. The method exploits the kinematic constraint to compensate the error that the magnetic disturbances induce in the IMU orientation estimates. This is achieved by realtime estimation and correction of the relative heading (azimuth) error that is caused by the disturbance. Since the kinematic constraint does not allow heading correction when the joint axis is vertical, we extend the proposed method such that it improves accuracy and robustness when the joint is close to that singularity. We evaluate the method by simulations of a quick hand motion and study the effect of inaccurate sensor-to-segment (anatomical) calibration and joint constraint relaxations. As a main result, the proposed method is found to reduce the root-mean-square error of the joint angle from 25.8° to 2.6° in the presence of large magnetic disturbances.
CRIS-Team Scopus Importer:2021-03-31

Feedback control of foot eversion in the adaptive peroneal stimulator - Seel, Thomas; Laidig, Daniel; Valtin, Markus; Werner, Cordula; Raisch, Jorg; Schauer, Thomas (2014 22nd Mediterranean Conference on Control and Automation, MED 2014, 2014)
The limited ability to dorsiflex the foot, known as drop foot, can be treated by functional electrical stimulation. Therein, undesired foot eversion/inversion is a common problem which is usually corrected by tedious manual repositioning of the electrodes. We address this issue by presenting a feedback-control solution featuring three major contributions: (1) an algorithm for inertial sensor-based foot-to-ground angle measurement with periodic drift correction; (2) a three-electrode setup that allows distribution of an overall stimulation intensity to the tibialis anterior muscle and to the superficial peroneal nerve that innervates the fibularis longus muscle, thus decoupling dorsiflexion and eversion control; (3) a run-to-run controller and an iterative learning controller, both of which use step-by-step learning to achieve desired eversion foot-to-ground angles. Experiments with a chronic drop foot patient demonstrate compensation of undesired eversion/inversion within at most two steps, while dorsiflexion angle trajectories are not affected.
CRIS-Team Scopus Importer:2021-03-31

Gait training by FES - Schauer, Thomas; Seel, Thomas (Advanced Technologies for the Rehabilitation of Gait and Balance Disorders, 2018)
Functional electrical stimulation has been applied for more than half a century to restore and support gait in patients after stroke or after spinal cord injury. Most prevalent are assistive systems for the correction of drop foot in stroke patients using either surface or implanted stimulation technology. For therapeutical use in clinical environments, multi-channel FES systems are often employed in combination with robotic devices or partial body weight support during walking on a treadmill. The restoration of gait in spinal cord injured people is also an ongoing research topic. New implantable stimulation systems and hybrid approaches that combine powered exoskeletons and FES are under investigation. Inertial sensor technology, electromyographic sensing, and advanced feedback control are predicted to be key technologies of future FES systems that allow a more patient and situation-specific gait support.
CRIS-Team Scopus Importer:2021-03-31
Keywords: FES Cycle; Postural Rehabilitation; Postural Balance; Virtual Reality Rehabilitation Systems; Wearable Robots; Gait Training Exoskeleton; Gait-adaptability Training; Gait Analysis; Locomotion Control; Cognitive Factors in Rehabilitation

Hierarchical Event-Triggered Learning for Cyclically Excited Systems With Application to Wireless Sensor Networks - Beuchert, Jonas; Solowjow, Friedrich; Raisch, Joerg; Trimpe, Sebastian; Seel, Thomas (IEEE Control Systems Letters, 2020)
Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and event-triggered learning (ETL) methods reduce network communication by sending information only when it cannot be adequately predicted based on previously transmitted data. This letter proposes an ETL approach for nonlinear discrete-time systems with cyclic excitation. The method automatically recognizes cyclic patterns in data-even when they change repeatedly-and reduces communication load whenever the current data can be accurately predicted from previous cycles. Nonetheless, a bounded error between original and received signal is guaranteed. The cyclic excitation model, which is used for predictions, is updated hierarchically, i.e., a full model update is only performed if updating a small number of model parameters is not sufficient. A nonparametric statistical test enforces that model updates happen only if the cyclic excitation changed with high probability. The effectiveness of the proposed methods is demonstrated using the application example of wireless real-time pitch angle measurements of a human foot in a feedback-controlled neuroprosthesis. The experimental results show that communication load can be reduced by 70% while the root-mean-square error between measured and received angle is less than 1°.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Sensor networks; statistical learning

Hybrid Inertial-Robotic Motion Tracking for Posture Biofeedback in Upper Limb Rehabilitation - Passon, Arne; Schauer, Thomas; Seel, Thomas (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2018)
Rehabilitation robotics and neuromuscular stimulation have become widespread technologies for rehabilitation training of stroke and spinal cord injured patients. In this context, real-time tracking of the performed motion facilitates real-time control of the motion support and biofeedback about undesired compensatory motions. We consider a cable-driven robotic system for upper limb rehabilitation and extend it by two wearable inertial sensors. By sensor fusion of the robotic and inertial measurements, we obtain accurate estimates of the forearm and upper arm orientation and position, which cannot be obtained by either of both measurement systems alone. A real-time biofeedback is introduced to prevent undesired compensatory motions of the trunk and shoulder. The proposed methods are evaluated with respect to an optical reference system in a series of experimental trials with and without compensatory motions. Using only the robotic sensors yields average measurement errors of up to 24 cm for the shoulder position and 19° for the elbow angle. In contrast, the proposed hybrid sensor fusion achieves accuracies better than 6 cm and 4°, respectively.
CRIS-Team Scopus Importer:2021-03-31

IMU-based Assessment of Ankle Inversion Kinematics and Orthosis Migration - Betz, Johannes; Klingspor, Christoph; Seel, Thomas (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019)
Ankle orthoses are used to prevent injuries during very fast inversion motions of the ankle. In order to provide proper protection, they must fit well and migrate as little as possible. Inertial measurement units (IMUs) have become a useful tool for accurate motion analysis and are frequently used for gait analysis. In the present paper, we examine orthosis migration and inversion kinematics of human ankles protected and unprotected by an orthosis. We present two test benches that were developed for these purposes, as well as a set of inertial sensor fusion methods that are used to determine kinematic parameters from the sensor readings. To avoid the common but restrictive assumption of a homogeneous magnetic field, we determine all motion parameters without the use of magnetometer readings. We conduct a measurement series to compare the proposed IMU-based method to alternative camera-based and goniometer-based methods. Using two different ankle orthosis prototypes, we demonstrate that the proposed IMU-based methods facilitate accurate assessment of orthosis migration and ankle inversion kinematics.
CRIS-Team Scopus Importer:2021-03-31

IMU-based joint angle measurement for gait analysis - Seel, Thomas; Raisch, Joerg; Schauer, Thomas (Sensors, 2014)
This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°. © 2014 by the authors; licensee MDPI, Basel, Switzerland.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Avoid magnetometers; Exploit kinematic constraints; Gait analysis; Gyroscopes and accelerometers; Inertial measurement units; Joint angle measurement; Joint axis and position identification; Sensor-to-segment mounting; Validation against optical gait analysis; Validation on prosthetic and human leg

Inertial-Sensor-Controlled Functional Electrical Stimulation for Swimming in Paraplegics: Enabling a Novel Hybrid Exercise Modality - Wiesener, Constantin; Seel, Thomas; Spieker, Lotta; Niedeggen, Andreas; Schauer, Thomas (IEEE Control Systems Magazine, 2020)
Here, we present initial results on an inertial sensor controlled neuroprosthetic system that supports people with spinal cord injuries during swimming exercises. This novel electrical stimulation system allows paraplegics to perform swimming exercises using their own legs.
CRIS-Team Scopus Importer:2021-03-31

Inertial sensors—applications and challenges in a nutshell - Seel, Thomas; Kok, Manon; Mcginnis, Ryan S. (Sensors, 2020)
This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Accelerometers; Body area networks; Error modeling and calibration; Gyroscopes; Inertial motion tracking; Inertial sensors in health care and sports engineering; Inertial sensors in robotics and manufacturing; Inertial sensors in vehicle motion estimation and control; Localization and mapping; Machine learning applied to inertial sensor data; Magnetometers; Sensor fusion

In vitro validation of a non-invasive continuous blood pressure measurement method for the perioperative application - Geus, Paul; Laidig, Daniel; Borchers, Patrick; Seel, Thomas; Stettin, Johanna; Kertzscher, Ulrich (Current Directions in Biomedical Engineering, 2021)
Hypotensive periods during surgery lead to an increase in postoperative complications. To further avoid the adverse events associated with the invasive measurement of the blood pressure, a perioperative, continuous, non-invasive blood pressure measurement method was developed. The precision of the method was tested for different mean pressure values and different thicknesses of the tissue model using a specifically designed and validated simulator that has the characteristics of the lower arm. The mean pressure difference between the pressure determined by the proposed method and simulated blood pressure from the simulator was 3.93 mmHg (±4.75 mmHg) for the systole and 4.89 mmHg (±6.10 mmHg) for the diastole. The results showed that the method can be a promising alternative to invasive blood pressure measurement methods.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Blood Pressure Measurement; Iterative Learning Control; Non-invasive; Perioperative

In-vitro validation of inertial-sensor-to-bone alignment - Weygers, Ive; Kok, Manon; Seel, Thomas; Shah, Darshan; Taylan, Orçun; Scheys, Lennart; Hallez, Hans; Claeys, Kurt (Journal of Biomechanics, 2021)
A major shortcoming in kinematic estimation using skin-attached inertial sensors is the alignment of sensor-embedded and segment-embedded coordinate systems. Only a correct alignment results in clinically relevant kinematics. Model-based inertial-sensor-to-bone alignment methods relate inertial sensor measurements with a model of the joint. Therefore, they do not rely on properly executed calibration movements or a correct sensor placement. However, it is unknown how accurate such model-based methods align the sensor axes and the underlying segment-embedded axes, as defined by clinical definitions. Also, validation of the alignment models is challenging, since an optical motion capture ground truth can be prone to disturbances from soft tissue movement, orientation estimation and manual palpation errors. We present an anatomical tibiofemoral ground truth on an unloaded cadaveric measurement set-up that intrinsically overcomes these disturbances. Additionally, we validate existing model-based alignment strategies. Modeling the degrees of freedom leads to the identification of rotation axes. However, there is no reason why these axes would align with the segment-embedded axes. Relative inertial-sensor orientation information and rich arbitrary movements showed to aid in identifying the underlying joint axes. The first dominant sagittal rotation axis aligned sufficiently well with the underlying segment-embedded reference. The estimated axes that relate to secondary kinematics tend to deviate from the underlying segment-embedded axes as much as their expected range of motion around the axes. In order to interpret the secondary kinematics, the alignment model should more closely match the biomechanics of the joint.
CRIS-Team Scopus Importer:2021-10-22
Keywords: Human movement analysis; IMU; Joint kinematics; Lower limb; Sensor-to-segment alignment

Iterative learning cascade control of continuous noninvasive blood pressure measurement - Seel, Thomas; Schauer, Thomas; Weber, Sarah; Affeld, Klaus (Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2013)
A noninvasive continuous blood pressure measurement technique that has been developed lately requires precise control of the blood flow through a superficial artery. The flow is measured using ultrasound and influenced via manipulating the pressure inside an inflatable air balloon which is placed over the artery. This contribution is concerned with the design and evaluation of a learning cascaded control structure for such measurement devices. Two feedback control loops are designed in discrete time via pole placement and then combined with an iterative learning control. The latter exploits the repetitive nature of the disturbance that is induced by the oscillating arterial pressure. Experimental results indicate that the proposed controller structure yields considerably smaller setpoint deviations than previous approaches. © 2013 IEEE.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biomedical systems; Blood pressure measurement; Cascade control; Feedback control; Iterative learning control

Iterative Learning Control and System Identification of the Antagonistic Knee Muscle Complex During Gait Using Functional Electrical Stimulation - Mueller, Philipp; Balligand, Cecile; Seel, Thomas; Schauer, Thomas (IFAC-PapersOnLine, 2017)
Functional Electrical Stimulation (FES) can be used to support the gait of stroke patients. By measuring joint angles and adjusting the stimulation intensities automatically to the current need of the patient, setup times can be reduced and time-variant effects like muscle fatigue can be compensated. This was achieved in recent publications by using Iterative Learning Control (ILC) on the ankle complex. In this paper we consider FES of the antagonistic knee muscle complex (quadriceps and hamstring muscles) that controls knee flexion/extension. We used a coactivation strategy in order to map the two stimulation channels to a single control input. A large class of dynamic models was obtained by system identification based on data from two experiments: one with standing subjects and one with subjects walking on a treadmill while being stimulated during different time segments of the gait cycle. Time delays, system poles, and in particular the system gains were found to vary largely. Furthermore, large differences were observed between muscle dynamics in standing pose and during walking. We designed an iterative learning controller that is stable for almost all models. In experiments with eight healthy subjects walking on a treadmill, the ILC was found to reduce deviations from a reference trajectory to about five degrees within two strides.
CRIS-Team Scopus Importer:2021-03-31
Keywords: adaptive; functional electrical stimulation (FES); gait; iterative learning control (ILC); knee angle; multichannel; neuroprosthesis; stroke rehabilitation; system identification

Iterative learning control for variable pass length systems - Seel, Thomas; Schauer, Thomas; Raisch, Jörg (IFAC Proceedings Volumes (IFAC-PapersOnline), 2011)
Monotonic convergence of linear iterative learning control (ILC) systems with changing pass length is considered. The maximum pass length (MPL) error is introduced as a useful concept for convergence analysis of this class of systems. Using the lifted-system framework, a both necessary and sufficient monotonic convergence criterion is found for the 1-norm of the MPL error. Further findings on 2-norm and ∞-norm convergence are added. Finally, an example system is given, i.e. the control of an electrical stimulation system for gait assistance, and simulation results are provided. © 2011 IFAC.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biomedical systems; Convergence analysis; Discrete-time; Iterative learning control; Lifted system framework; Linear systems; Monotonic convergence; Variable pass length

Iterative learning control of a drop foot neuroprosthesis - Generating physiological foot motion in paretic gait by automatic feedback control - Seel, Thomas; Werner, Cordula; Raisch, Joerg; Schauer, Thomas (Control Engineering Practice, 2016)
Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift the foot and leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We highlight the role of feedback control for addressing the challenges that result from the large individuality and time-variance of muscle response dynamics. Unlike many previous approaches, we do not reduce the control problem to the scalar case. Instead, the entire pitch angle trajectory of the paretic foot is measured by means of a 6D Inertial Measurement Unit (IMU) and controlled by an Iterative Learning Control (ILC) scheme for variable-pass-length systems. While previously suggested controllers were often validated for the strongly simplified case of sitting or lying subjects, we demonstrate the effectiveness of the proposed approach in experimental trials with walking drop foot patients. Our results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion. Starting from such conservative intensity profiles, the proposed learning controller automatically achieves a desired foot motion within one or two strides and keeps adjusting the stimulation to compensate time-variant muscle dynamics and disturbances.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biomedical engineering application; Functional electrical stimulation; Inertial measurement unit; Iterative learning control; Motor impairment; Realtime motion analysis

Iterative learning control of drop foot stimulation with array electrodes for selective muscle activation - Valtin, M.; Seel, Thomas; Raisch, J.; Schauer, T. (IFAC Proceedings Volumes (IFAC-PapersOnline), 2014)
Disorders of the central nervous system like stroke often lead to a paresis of the muscles responsible for dorsiflexion and eversion of the foot during swing phase. Functional electrical stimulation (FES) is commonly used to improve the foot movement. A precise placement of two single surface electrodes on the shank as well as a manual tuning of heel-switch triggered stimulation is required when using standard drop-foot stimulators. In this work, the use of automatically tuned array electrodes for selective nerve/muscle stimulation and the application of iterative learning control for adjusting stimulation intensities are investigated with the aim to obtain an optimal foot movement. The stimulation effect is observed by means of two inertial measurement units mounted on the foot and shank, respectively. Two array electrodes are placed over the areas covering the peroneal nerve and the muscle tibialis anterior. A fast identification procedure finds three suitable sets of array elements, forming so-called virtual electrodes, in both arrays. Two stimulation channels are established over the three virtual electrodes. Both channels produce dorsiflexion, but one promotes eversion and the other inversion. A decoupling matrix is applied to the stimulation intensities of both channels in order to obtain independent control over dorsiflexion and eversion. Finally, two independent iterative learning controllers are employed to achieve desired angle profiles. The proposed stimulation and control scheme is initially tested on a healthy person sitting on a table with the shank and foot free to swing. A tracking RMS error of less than one degree is achieved within six walking steps.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Array electrodes; Biomedical system; Functional electrical stimulation; Iterative learning control; MIMO decoupling; Virtual electrodes

Iterative learning control with variable pass length applied to trajectory tracking on a crane with output constraints - Guth, Mickaël; Seel, Thomas; Raisch, Jörg (Proceedings of the IEEE Conference on Decision and Control, 2013)
A typical application of Iterative Learning Control (ILC), namely trajectory tracking on a lab-scale gantry crane, is considered. However, the load is only allowed to move in the close proximity of the reference trajectory. Since these output constraints lead to disrupted trials, the pass length in this ILC system is not constant. In this contribution, we present new results on convergence and new combinations of methods for this class of ILC systems and apply them to the given application. Simulation and experimental results are provided which demonstrate that both maximum pass length and small tracking error can be achieved in very few iterations even in the presence of tight constraints and model uncertainties. © 2013 IEEE.
CRIS-Team Scopus Importer:2021-03-31

Iterative Learning Vector Field for FES-Supported Cyclic Upper Limb Movements in Combination with Robotic Weight Compensation - Passon, Arne; Seel, Thomas; Massmann, Jonas; Schauer, Thomas; Freernan, Chris (IEEE International Conference on Intelligent Robots and Systems, 2018)
Robotics and Functional Electrical Stimulation (FES) are well-established technologies for the rehabilitation of stroke and spinal cord injured (SCI) patients. We propose a hybrid solution that combines feedback-controlled FES of biceps and triceps as well as posterior and anterior deltoid with a cable-driven robotic system to support repetitive arm movements, like 'breaststroke swimming' exercises. The robotic system partially compensates the arm weight by controlling the cable tension forces, and the FES promotes motion in the transversal plane. To adjust the FES support to the needs of the individual patients we use an iterative learning vector field (ILVF) which encodes the stimulation intensities that are applied to guide the patient along a pre-specified reference trajectory in the joint angle space. In contrast to previous iterative learning control approaches, the ILVF allows the patient to perform the motion at self-selected cadence. The proposed learning algorithm explicitly takes the dynamics of the artificially activated muscles into account and assures smooth stimulation intensity profiles. The control algorithm is tested in simulations using a complex neuro-musculoskeletal model. For 'breaststroke' motions, the initial RMS error of purely volitional movements is reduced from 38° to 10° within 21 cycles by the adaptive FES support. After 50 iterations of the ILVF, the algorithm converges to a steady state RMS error of 4°. Changes in the patient's muscle activity and cadence were well tolerated by the control system and did not cause a noticable increase in the steady state RMS error.
CRIS-Team Scopus Importer:2021-03-31

Iteratively Learning Electromyography (EMG)-based Functional Electrical Stimulation (FES) for Stroke Rehabilitation - Lupu, O.; Madaschi, M.; Seel, Thomas; Cologni, A.; Previdi, F.; Schauer, Thomas (Biomedizinische Technik, 2012)
CRIS-Team Scopus Importer:2021-03-31

Iterativ Lernende Regelung mit variabler Zykluslänge für FES-basierte Kompensation einer Fußheberschwäche - Seel, Thomas; Schauer, Thomas; Raisch, Joerg (At-Automatisierungstechnik, 2013)
Functional electrical stimulation can be used to support the dorsiflexion of the foot during the swing phase of gait. Iterative learning control is employed to generate natural foot angle trajectories. In this article we consider early ground contact due to insufficient stimulation and study the resulting coupled dynamics of the error and the pass length. First, adequate theoretical concepts are discussed, which are then used for controller design. Finally, the achievable convergence properties are analyzed experimentally. © Oldenbourg Wissenschaftsverlag.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biomedical engineering; Drop foot syndrome; Functional electrical stimulation; Iterative learning control; Monotonic convergence; Variable pass length

Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints - Seel, Thomas; Schauer, Thomas; Raisch, Jorg (Proceedings of the IEEE International Conference on Control Applications, 2012)
We consider 6d inertial measurement units (IMU) attached to rigid bodies, e.g. human limb segments or links of a robotic manipulator, which are connected by hinge joints and spheroidal joints. Novel methods for joint axis estimation and joint position estimation are presented that exploit the kinematic constraints induced by these two types of joints. The presented methods do not require any knowledge about the sensor units' positions or orientations and do not include integration, i.e. they are insensitive to measurement bias. By means of a three-links simulation model, the estimation algorithms are validated and convergence is analyzed. Finally, the algorithms are tested using experimental data from IMU-based human gait analysis. © 2012 IEEE.
CRIS-Team Scopus Importer:2021-03-31

Joint Axis Estimation for Fast and Slow Movements Using Weighted Gyroscope and Acceleration Constraints - Olsson, Fredrik; Seel, Thomas; Lehmann, Dustin; Halvorsen, Kjartan (FUSION 2019 - 22nd International Conference on Information Fusion, 2019)
Sensor-to-segment calibration is a crucial step in inertial motion tracking. When two segments are connected by a hinge joint, for example in human knee and finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. There exist methods that identify these coordinates by solving an optimization problem that is based on kinematic joint constraints, which involve either the measured accelerations or the measured angular rates. In the current paper we demonstrate that using only one of these constraints leads to inaccurate estimates at either fast or slow motions. We propose a novel method based on a cost function that combines both constraints. The restrictive assumption of a homogeneous magnetic field is avoided by using only accelerometer and gyroscope readings. To combine the advantages of both sensor types, the residual weights are adjusted automatically based on the estimated signal variances and a nonlinear weighting of the acceleration norm difference. The method is evaluated using real data from nine different motions of an upper limb exoskeleton. Results show that, unlike previous approaches, the proposed method yields accurate joint axis estimation after only five seconds for all fast and slow motions.
CRIS-Team Scopus Importer:2021-03-31
Keywords: anatomic calibration; human movement analysis; inertial sensors; kinematic modeling

Magnetometer-free Realtime Inertial Motion Tracking by Exploitation of Kinematic Constraints in 2-DoF Joints - Laidig, Daniel; Lehmann, Dustin; Begin, Marc Andre; Seel, Thomas (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019)
Inertial Measurement Units (IMUs) are used to track the motion of kinematic chains in a wide variety of robotic and biomedical applications. However, inertial motion tracking is severely limited by the fact that magnetic fields are inhomogeneous in indoor environments and near electronic devices. Methods that use only accelerations and angular rates for orientation estimation yield no absolute heading information and suffer from heading drift. To overcome this limitation, we propose a novel method that exploits an orientation-based kinematic constraint in joints with two degrees of freedom (DoF), such as cardan joints, saddle joints, the human wrists, elbow or ankles. The method determines the relative heading of the joint segments in real time by minimization of a nonlinear cost function. A filter for singularity treatment ensures accurate tracking during motion phases for which the cost function minimum is ambiguous. We experimentally validate the method in metacarpophalangeal (MCP) joints between the palm and the fingers. Accurate relative orientation tracking is achieved continuously despite several singular motion phases and even though the heading components of the 6D orientations drift by more than 360 degrees within ten minutes. The proposed method overcomes a major limitation of inertial motion tracking and thereby facilitates the use of this technology in robotic and biomechanical applications.
CRIS-Team Scopus Importer:2021-03-31

Modular finger and hand motion capturing system based on inertial and magnetic sensors - Valtin, Markus; Salchow, Christina; Seel, Thomas; Laidig, Daniel; Schauer, Thomas (Current Directions in Biomedical Engineering, 2017)
The assessment of hand posture and kinematics is increasingly important in various fields. This includes the rehabilitation of stroke survivors with restricted hand function. This paper presents a modular, ambulatory measurement system for the assement of the remaining hand function and for closed-loop controlled therapy. The device is based on inertial sensors and utilizes up to five interchangeable sensor strips to achieve modularity and to simplify the sensor attachment. We introduce the modular hardware design and describe algorithms used to calculate the joint angles. Measurements with two experimental setups demonstrate the feasibility and the potential of such a tracking device.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Finger kinematics; Hand; Human motion analysis; Inertial measurement units; Joint angle estimation

Monotonic convergence of iterative learning control systems with variable pass length - Seel, Thomas; Schauer, Thomas; Raisch, Joerg (International Journal of Control, 2017)
A growing number of researchers consider iterative learning control (ILC) a promising tool for numerous control problems in biomedical application systems. We will briefly discuss why classical ILC theory is technically too restrictive for some of these applications. Subsequently, we will extend the classical ILC design in the lifted systems framework to the class of repetitive trajectory tracking tasks with variable pass length. We will analyse the closed-loop dynamics for two standard learning laws, and we will discuss in which sense the tracking error can be reduced by which controller design strategies. Necessary and sufficient conditions for monotonic convergence will be derived. We then summarise all results in a set of practical controller design guidelines. Finally, a simulation study is presented, which demonstrates the usefulness of these guidelines and illustrates the special dynamics that occur in variable pass length learning.
CRIS-Team Scopus Importer:2021-03-31
Keywords: biomedical applications; Iterative learning control; lifted system framework; monotonic convergence; non-uniform trial duration

Motion Estimation for Tethered Airfoils with Tether Sag - Hendrik Freter, Jan; Seel, Thomas; Elfert, Christoph; Gohlich, Dietmar (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2020)
In this contribution a motion estimation approach for the autonomous flight of tethered airfoils is presented. Accurate motion data are essential for the airborne wind energy sector to optimize the harvested wind energy and for the manufacturer of tethered airfoils to optimize the kite design based on measurement data. We propose an estimation based on tether angle measurements from the ground unit and inertial sensor data from the airfoil. In contrast to existing approaches, we account for the issue of tether sag, which renders tether angle measurements temporarily inaccurate. We formulate a Kalman Filter which adaptively shifts the fusion weight to the measurement with the higher certainty. The proposed estimation method is evaluated in simulations, and a proof of concept is given on experimental data, for which the proposed method yields a three times smaller estimation error than a fixed-weight solution.
CRIS-Team Scopus Importer:2021-03-31

Multi-modal sensor fusion for highly accurate vehicle motion state estimation - Marco, Vicent Rodrigo; Kalkkuhl, Jens; Raisch, Joerg; Scholte, Wouter J.; Nijmeijer, Henk; Seel, Thomas (Control Engineering Practice, 2020)
In the context of autonomous driving in urban environments accurate and reliable information about the vehicle motion is crucial. This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Moreover, in order to enhance the estimation accuracy, the scheme simultaneously estimates the gyroscope and accelerometer biases. The approach relies on a state-affine representation of a kinematic model with an additional measurement equation based on a single-track model. The sensor fusion scheme is built upon a recently proposed adaptive estimator, which allows a direct consideration of model uncertainties and sensor noise. In order to provide accurate estimates during collision avoidance manoeuvres, a measurement covariance adaptation is introduced, which reduces the influence of the single-track model when its information is superfluous. A validation using experimental data demonstrates the effectiveness of the method during both regular urban drives and collision avoidance manoeuvres.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Automotive industry; Autonomous driving; Collision avoidance; Inertial sensors; Kalman filter; Motion estimation; Non-linear systems; Observability; Odometry; Simultaneous state and parameter estimation; Systems and control engineering

Multivariable control of foot motion during gait by peroneal nerve stimulation via two skin electrodes - Seel, Thomas; Valtin, Markus; Werner, Cordula; Schauer, Thomas (IFAC-PapersOnLine, 2015)
Foot drop appears, for example, in stroke patients and is characterized by the limited ability to lift (the lateral and/or medial edge of) the foot, which leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We introduce a novel stimulation pulse waveform modification technique that allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus almost independently via two single surface electrodes without violating the zero net current requirement. A piecewise linear controller output mapping is applied in order to cope with the nonlinearities in patients' stimulation intensity tolerance. The pitch and roll angle of the foot are estimated by means of an Inertial Measurement Unit (IMU) and controlled via a decentralized Iterative Learning Control (ILC) scheme. We demonstrate the effectiveness of our approach in experimental trials with stroke patients walking on a treadmill. Starting from conventional stimulation parameters, the controller automatically achieves physiological foot pitch and roll angle trajectories within only two strides.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biomedical systems; Drop foot syndrome; Functional electrical stimulation; Iterative learning control; Multivariable control systems; Neuroprosthetics; Pulse waveform modification; Rehabilitation engineering; Validation by experiments

Neural networks versus conventional filters for inertial-sensor-based attitude estimation - Weber, Daniel; Guhmann, Clemens; Seel, Thomas (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020, 2020)
Inertial measurement units are commonly used to estimate the attitude of moving objects. Numerous nonlinear filter approaches have been proposed for solving the inherent sensor fusion problem. However, when a large range of different dynamic and static rotational and translational motions is considered, the attainable accuracy is limited by the need for situation-dependent adjustment of accelerometer and gyroscope fusion weights. We investigate to what extent these limitations can be overcome by means of artificial neural networks and how much domain-specific optimization of the neural network model is required to outperform the conventional filter solution. A diverse set of motion recordings with a marker-based optical ground truth is used for performance evaluation and comparison. The proposed neural networks are found to outperform the conventional filter across all motions only if domain-specific optimizations are introduced. We conclude that they are a promising tool for inertial-sensor-based real-time attitude estimation, but both expert knowledge and rich datasets are required to achieve top performance.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Attitude determination; Convolutional neural networks; Inertial sensors; Neural networks; Nonlinear filters; Performance evaluation; Recurrent neural networks; Sensor fusion

Neural ODEs for Data-driven Automatic Self-Design of Finite-time Output Feedback Control for Unknown Nonlinear Dynamics - Bachhuber, Simon; Weygers, Ive; Seel, Thomas (IEEE Control Systems Letters, 2023)
Many application fields, e.g., robotic surgery, autonomous piloting, and wearable robotics greatly benefit from advances in robotics and automation. A common task is to control an unknown nonlinear system such that its output tracks a desired reference signal for a finite duration of time. A learning control method that automatically and efficiently designs output feedback controllers for this task would greatly boost practicality over time-consuming and labour-intensive manual system identification and controller design methods. In this contribution we propose Automatic Neural Ordinary Differential Equation Control (ANODEC), a data-efficient automatic design of output feedback controllers for finite-time reference tracking in systems with unknown nonlinear dynamics. In a two-step approach, ANODEC first identifies a neural ODE model of the system dynamics from input-output data of the system dynamics and then exploits this data-driven model to learn a neural ODE feedback controller, while requiring no knowledge of the actual system state or its dimensionality. In-silico validation shows that ANODEC is able to —automatically— design competitive controllers that outperform two controller baselines, and achieves an on average ≈ 30% / 17% lower median RMSE. This is demonstrated in four different nonlinear systems using multiple, qualitatively different and even out-of-training-distribution reference signals.
CRIS-Team Scopus Importer:2023-07-21
Keywords: Adaptive control; Data models; Mathematical models; Neural networks; Nonlinear dynamical systems; System dynamics; Task analysis

Nonlinear observer with observability-based parameter adaptation for vehicle motion estimation - Marco, Vicent Rodrigo; Kalkkuhl, Jens; Seel, Thomas (IFAC-PapersOnLine, 2018)
In the framework of self-driving cars and driver-assistance systems the demand for reliable information about the vehicle ego-motion is increasing. This paper describes an estimation scheme, based on a nonlinear observer design, that provides velocity and attitude angle estimates. The approach relies on a state-affine representation of a kinematic model bolstered by a dynamic model-based measurement equation. By means of a thorough observability analysis, global exponential convergence is theoretically guaranteed. Additionally, in order to minimize the errors introduced by the dynamic model limitations, an observer tuning rule is proposed. The adaptation of the tuning parameters is built upon an online observability assessment of the system without the support of the dynamic model. Experimental results show that the presented approach reliably estimates the motion states.
CRIS-Team Scopus Importer:2021-03-31
Keywords: automobile industry; Motion estimation; nonlinear systems; observability

Observability of the relative motion from inertial data in kinematic chains - Kok, Manon; Eckhoff, Karsten; Weygers, Ive; Seel, Thomas (Control Engineering Practice, 2022)
Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applications. In recent years, it has been shown that, by placing inertial sensors on segments that are connected by rotational joints, the motion of that kinematic chain can be tracked accurately. These methods specifically avoid using magnetometer measurements, which are known to be unreliable since the magnetic field at the different sensor locations is typically different. They rely on the assumption that the motion of the kinematic chain is sufficiently rich to assure observability of the relative pose. However, a formal investigation of this crucial requirement has not yet been presented, and no specific conditions for observability have so far been given. In this work, we present an observability analysis and show that the relative pose of the body segments is indeed observable under a very mild condition on the motion. We support our results by simulation studies, in which we employ a state estimator that neither uses magnetometer measurements nor additional sensors and does not impose assumptions on the accelerometer to measure only the direction of gravity, nor on the range of motion or degrees of freedom of the joints. We investigate the effect of the amount of excitation and of stationary periods in the data on the accuracy of the estimates. We then use experimental data from two mechanical joints as well as from a human gait experiment to validate the observability criterion in practice and to show that small excitation levels are sufficient for obtaining accurate estimates even in the presence of time periods during which the motion is not observable.
CRIS-Team Scopus Importer:2022-06-03
Keywords: Inertial sensors; Kinematic chains; Motion estimation; Observability

Online Monitoring of Muscle Activity During Walking for Bio-feedback and for Observing the Effects of Transcutaneous Electrical Stimulation - Bunt, Nathan D.; Moreno, Juan C.; Muller, Philipp; Seel, Thomas; Schauer, Thomas (Converging Clinical and Engineering Research on Neurorehabilitation II, 2017)
This contribution describes a method for real-time analysis of muscle activity over the gait cycle while simultaneously applying Functional Electrical Stimulation (FES) to the assessed muscles. Inertial sensors at the foot are used for real-time gait phase detection in order to synchronize the stimulation with the gait. An EMG analysis has been performed to cancel out stimulation artifacts, to filter the data and to extract the voluntary EMG activity. This corrected EMG signal has been rectified and low-pass filtered to produce an envelope that was parameterized as a function of the Gait Cycle Percentage (GCP). The volitional EMG activity profile has been averaged over five strides and can be presented as a moving average over the exercise duration. Initial evaluation with healthy subjects showed that this procedure is feasible to detect the expected volitional muscle activity profiles also during active electrical stimulation.
CRIS-Team Scopus Importer:2021-03-31

On motions that allow for identification of hinge joint axes from kinematic constraints and 6D IMU data - Nowka, Danny; Kok, Manon; Seel, Thomas (2019 18th European Control Conference, ECC 2019, 2019)
In inertial motion tracking of kinematic chains, inertial measurement units (IMUs) are attached to each segment in order to track their motion in three-dimensional space. Determining the relations between the functional axes of a joint and the local coordinate system of the attached sensor is a crucial requirement. For the case of hinge joints, methods have been proposed that exploit kinematic constraints to automatically identify the local hinge joint axis coordinates from the raw data of almost arbitrary motions. However, to current date, it remains unclear which joint motions are sufficiently rich for the joint axis to become identifiable. We consider a commonly used gyroscope-based kinematic constraint and present a novel accelerometer-based kinematic constraint. We study conditions of identifiability by analyzing the nonlinear constraint equations and present practical conditions for the minimum excitation that is required. Among other results, we prove that planar motions and subsequent motions of both ends of the joint are sufficient as long as the joint axis does not remain perfectly horizontal. Theoretical results are validated in experimental studies of a human upper limb wearing an exoskeleton. Despite the typical IMU-related measurement inaccuracies and although the human elbow joint is only an approximate hinge joint, the cost function defined by the kinematic constraints exhibits a distinct global minimum at the true joint axis coordinates if the motion fulfills the proposed requirements.
CRIS-Team Scopus Importer:2021-03-31

Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: An event-triggered learning approach - Beuchert, Jonas; Solowjow, Friedrich; Trimpe, Sebastian; Seel, Thomas (Sensors, 2020)
Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Bandwidth limitations; Body area networks; Communication networks; Data transmission protocols; Event-triggered state estimation; Gaussian processes; Inertial measurement units; Motion tracking; Physiological signals

Realtime EMG analysis for transcutaneous electrical stimulation assisted gait training in stroke patients - Schauer, Thomas; Seel, Thomas; Bunt, N. D.; Mueller, P.; Moreno, J. C. (IFAC-PapersOnLine, 2016)
This contribution describes a method for realtime analysis of muscle activity during application of Functional Electrical Stimulation (FES) to the assessed muscles. Inertial sensors at the foot are used for realtime gait phase detection in order to synchronize the stimulation with the gait. After detecting and muting stimulation artifacts and after extraction of Inputer-Pulse Intervals (IPIs), a non-causal high-pass filter is applied to a section of the IPI to extract the voluntary EMG activity. The filter suppresses FES-evoked EMG activity (M-wave) and electrode discharging artifacts. The initial filter states are chosen by an optimization procedure to minimize undesired filter transients. The obtained filtered EMG signal is then rectified and averaged to produce a scalar measure of the volitional EMG activity over the last IPI. The volitional EMG activity during four different detected gait phases is calculated after every completed step and displayed to the stroke patients for biofeedback or to the therapist in order to adjust the FES. The system has been initially evaluated with healthy subjects walking on a treadmill. It was demonstrated that different walking styles of an individual can be distinguished by the EMG analysis also during active FES support.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biofeedback; Filter Design; Functional Electrical Stimulation; Gait; Optimization; Rehabilitation; Signal Processing

Real-time implementation and evaluation of magnetometerless tracking system for human and humanoid posture control benchmarking based on inertial sensors - Lippi, Vittorio; Brands, Kai Günter; Seel, Thomas (ICINCO 2020 - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics, 2020)
This work describes a tracking system designed for humanoid robots, exoskeletons and humans oriented to posture control and balance experiments. The system aims to provide a tool that allows for repeatability of balance experiments across different robotics platforms and control algorithms with the ultimate aim of providing a standardized framework for performance benchmarking. To make the system suitable for different geometries and materials, it relies just on inertial sensors. The system is evaluated with a marker-based optical tracking, performing a trial of a typical posture control and balance experiment. In particular the frequency response function of the body segments respect to the support surface tilt is evaluated.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Benchmarking; Humanoid posture control; Magnetometerless; Tracking system

Real-time motion onset recognition for robot-assisted gait rehabilitation - Hassani, Roushanak Haji; Bannwart, Mathias; Bolliger, Marc; Seel, Thomas; Brunner, Reinald; Rauter, Georg (Journal of neuroEngineering and rehabilitation, 2022)
Background: Many patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users’ movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8 classes in total). Methods: To predict the onsets of the gait-related tasks, three Inertial Measurement Units (IMUs) were attached to the sternum and middle of outer thighs of 19 controls without neurological movement disorders and two individuals with incomplete Spinal Cord Injury (iSCI). The data of IMUs obtained from different gait tasks was sent synchronously to a real-time data acquisition system through a custom-made Bluetooth-EtherCAT gateway. In the first step, data was applied offline for training five different classifiers. The best classifier was chosen based on F1-score results of a Leave-One-Participant-Out Cross-Validation (LOPOCV), which is an unbiased way of testing. In a final step, the chosen classifier was tested in real time with an additional control participant to demonstrate feasibility for real-time classification. Results: Testing five different classifiers, the best performance was obtained in a single-layer neural network with 25 neurons. The F1-score of 86.83 % ± 6.2 % and 92.01 % are achieved on testing using LOPOCV and test data (30 % , participants = 20), respectively. Furthermore, the results from the implemented real-time classifier were compared with the offline classifier and revealed nearly identical performance (difference = 0.08 %). Conclusions: A neural network classifier was trained for identifying the onset of gait-related tasks in real time. Test data showed convincing performance for offline and real-time classification. This demonstrates the feasibility and potential for implementing real-time onset recognition in rehabilitation devices in future.
CRIS-Team Scopus Importer:2022-02-11
Keywords: Body weight support; EtherCAT; Inertial measurement unit; Real-time activity recognition; Sliding window; Wireless EtherCAT interface

Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint - Weygers, Ive; Kok, Manon; Seel, Thomas; Shah, Darshan; Taylan, Orcun; Scheys, Lennart; Hallez, Hans; Claeys, Kurt (Scientific Data, 2021)
Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.
CRIS-Team Scopus Importer:2021-08-27

Regularized adaptive Kalman filter for non-persistently excited systems - Rodrigo Marco, Vicent; Kalkkuhl, Jens C.; Raisch, Jörg; Seel, Thomas (Automatica, 2022)
Most available methods for joint state-parameter estimation in discrete-time stochastic time-varying systems suffer from severe performance degradation if the system is not persistently excited. We revisit and extend a recently proposed adaptive Kalman filter in order to enhance its robustness against periods of poor excitation. A Levenberg–Marquardt-like regularization algorithm is integrated in the filter to preclude the estimator from becoming unreliable due to wind-up effects. It is proven that, under uniform complete observability–controllability conditions and a persistent excitation condition, the expectations of the state and parameter estimation errors tend to zero exponentially fast. Furthermore, their stability (in the sense of Lyapunov) is guaranteed even if the persistent excitation condition is not satisfied. The effectiveness of the algorithm is demonstrated using real sensor data from a vehicle motion estimation application. Unlike the original algorithm, the proposed regularized adaptive Kalman filter provides accurate and reliable estimates of the states and parameters despite periods of poor excitation.
CRIS-Team Scopus Importer:2022-02-11
Keywords: Adaptive observer design; Automotive vehicles; Discrete-time stochastic systems; Joint state and parameter estimation; Motion estimation; Non-persistently excited systems

RIANN—A Robust Neural Network Outperforms Attitude Estimation Filters - Weber, Daniel; Gühmann, Clemens; Seel, Thomas (AI, 2021)
Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a ready-to-use, neural network-based, parameter-free, real-time-capable inertial attitude estimator, which generalizes well across different motion dynamics, environments, and sampling rates, without the need for application-specific adaptations. We gather six publicly available datasets of which we exploit two datasets for the method development and the training, and we use four datasets for evaluation of the trained estimator in three different test scenarios with varying practical relevance. Results show that RIANN outperforms state-of-the-art attitude estimation filters in the sense that it generalizes much better across a variety of motions and conditions in different applications, with different sensor hardware and different sampling frequencies. This is true even if the filters are tuned on each individual test dataset, whereas RIANN was trained on completely separate data and has never seen any of these test datasets. RIANN can be applied directly without adaptations or training and is therefore expected to enable plug-and-play solutions in numerous applications, especially when accuracy is crucial but no ground-truth data is available for tuning or when motion and disturbance characteristics are uncertain. We made RIANN publicly available.
CRIS-Team Scopus Importer:2024-12-16
Keywords: attitude estimation; inertial sensors; information fusion; neural networks; nonlinear filters; performance evaluation; recurrent neural networks

RNN-based Observability Analysis for Magnetometer-Free Sparse Inertial Motion Tracking - Bachhuber, Simon; Weber, Daniel; Weygers, Ive; Seel, Thomas (2022 25th International Conference on Information Fusion, FUSION 2022, 2022)
Inertial measurement units are widely used for motion tracking of kinematic chains in numerous applications. While magnetometer-free sensor fusion enables reliably high accuracy in indoor environments and near magnetic disturbances, the use of sparse sensor setups would yield additional advantages in cost, effort, and usability. However, it is unclear which sparse sensor setups can be used to track which motions of which kinematic chains, since observability of the underlying nonlinear dynamics is barely understood to date. We propose a method that utilizes recurrent neural networks (RNNs) and automatically generated training data to assess the observability of the relative pose of kinematic chains in sparse inertial motion tracking (IMT) systems. We apply this method to a range of double-hinge-joint systems that perform fully-exciting random motion. Results show how the degree of observability depends on the kinematic structure and that RNN-based observers can achieve small tracking errors in a large range of sparse and magnetometer-free setups. The proposed methods enable systematic assessment of observability properties in complex nonlinear dynamics and represent a key step toward enabling reliably accurate and non-restrictive IMT solutions.
CRIS-Team Scopus Importer:2022-09-09

Robust plug-and-play joint axis estimation using inertial sensors - Olsson, Fredrik; Kok, Manon; Seel, Thomas; Halvorsen, Kjartan (Sensors, 2020)
Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of 2° were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Gyroscopes and accelerometers; Inertial measurement units; Joint axis identification; Kinematic constraints; Sensor-to-segment calibration; Validation on mechanical joint

Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints - Laidig, Daniel; Weygers, Ive; Seel, Thomas (Sensors, 2022)
Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.
CRIS-Team WoS Importer:2023-01-13

Sparse Magnetometer-Free Real-Time Inertial Hand Motion Tracking - Grapentin, Aaron; Lehmann, Dustin; Zhupa, Ardjola; Seel, Thomas (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2020)
Hand motion tracking is a key technology in several applications including ergonomic workplace assessment, human-machine interaction and neurological rehabilitation. Recent technological solutions are based on inertial measurement units (IMUs). They are less obtrusive than exoskeleton-based solutions and overcome the line-of-sight restrictions of optical systems. The number of sensors is crucial for usability, unobtrusiveness, and hardware cost. In this paper, we present a real-time capable, sparse motion tracking solution for hand motion tracking that requires only five IMUs, one on each of the distal finger segments and one on the back of the hand, in contrast to recently proposed full-setup solution with 16 IMUs. The method only uses gyroscope and accelerometer readings and avoids magnetometer readings, which enables unrestricted use in indoor environments, near ferromagnetic materials and electronic devices. We use a moving horizon estimation (MHE) approach that exploits kinematic constraints to track motions and performs long-term stable heading estimation. The proposed method is validated experimentally using a recently developed sensor system. It is found that the proposed method yields qualitatively good agreement of the estimated and the actual hand motion and that the estimates are long-term stable. The root-mean-square deviation between the fingertip position estimates of the sparse and the full setup are found to be in the range of 1 cm. The method is hence highly suitable for unobtrusive and non-restrictive motion tracking in a range of applications.
CRIS-Team Scopus Importer:2021-03-31

Special issue: AUTOMED 2021: Automation in Medical Technology - Rauter, Georg; Seel, Thomas; Rostalski, Philipp (At-Automatisierungstechnik, 2022)
CRIS-Team WoS Importer:2022-12-02

Stop repeating yourself: Exploitation of repetitive signal patterns to reduce communication load in sensor networks - Zhang, Tianyi; Laidig, Daniel; Seel, Thomas (2019 18th European Control Conference, ECC 2019, 2019)
We consider communication in systems where (1) it is desirable to reduce communication load and (2) the signals to be transmitted contain approximately periodic patterns. We propose methods that extract the periodic pattern from the signal in real time and reduce communication between sender and receiver to one sample per cycle period. The proposed methods allow for variable cycle duration and immediately return to full communication if the signal no longer contains the extracted pattern. During reduced communication the receiver provides an immediate real-time signal based on extrapolation and concatenation of the extracted pattern. At the end of each cycle, the receiver additionally reconstructs the signal segment of that cycle by means of time warping, which yields an output that is more accurate but is provided only cyclewise. As a specific example application we consider transmission of quaternion signals from wireless wearable inertial sensors attached to the lower body of a walking human. We quantify how repetitive these signals are and how much they change when the subject simulates pathological gait. We then determine the error between the true signal and the extrapolated and reconstructed signals. The proposed method is found to reduce communication load by up to 80% while remaining sensitive to deviations from the natural variability of human gait.
CRIS-Team Scopus Importer:2021-03-31

Supporting front crawl swimming in paraplegics using electrical stimulation: A feasibility study - Wiesener, Constantin; Spieker, Lotta; Axelgaard, Jens; Horton, Rachel; Niedeggen, Andreas; Wenger, Nikolaus; Seel, Thomas; Schauer, Thomas (Journal of neuroEngineering and rehabilitation, 2020)
Background: Participation in physical and therapeutic activities is usually severely restricted after a spinal cord injury (SCI). Reasons for this are the associated loss of voluntary motor function, inefficient temperature regulation of the affected extremities, and early muscle fatigue. Hydrotherapy or swim training offer an inherent weight relief, reduce spasticity and improve coordination, muscle strength and fitness. Methods: We present a new hybrid exercise modality that combines functional electrical stimulation (FES) of the knee extensors and transcutaneous spinal cord stimulation (tSCS) with paraplegic front crawl swimming. tSCS is used to stimulate the afferent fibers of the L2-S2 posterior roots for spasticity reduction. By activating the tSCS, the trunk musculature is recruited at a motor level. This shall improve trunk stability and straighten the upper body. Within this feasibility study, two complete SCI subjects (both Asia scale A, lesion level Th5/6), who have been proficient front crawl swimmers, conducted a 10-week swim training with stimulation support. In an additional assessment swim session nine months after the training, the knee extension, hip extension, and trunk roll angles where measured using waterproof inertial measurement units (IMUs) and compared for different swimming conditions (no stimulation, tSCS, FES, FES plus tSCS). Results: For both subjects, a training effect over the 10-week swim training was observed in terms of measured lap times (16 m pool) for all swimming conditions. Swimming supported by FES reduced lap times by 15.4% and 8.7% on average for Subject A and Subject B, respectively. Adding tSCS support yielded even greater mean decreases of 19.3% and 20.9% for Subjects A and B, respectively. Additionally, both subjects individually reported that swimming with tSCS for 30-45 minutes eliminated spasticity in the lower extremities for up to 4 hours beyond the duration of the session. Comparing the median as well as the interquartile range of all different settings, the IMU-based motion analysis revealed that FES as well as FES+tSCS improve knee extension in both subjects, while hip extension was only increased in one subject. Trunk roll angles were similar for all swimming conditions. tSCS had no influence on the knee and hip joint angles. Both subjects reported that stimulation-assisted swimming is comfortable, enjoyable, and they would like to use such a device for recreational training and rehabilitation in the future. Conclusions: Stimulation-assisted swimming seems to be a promising new form of hybrid exercise for SCI people. It is safe to use with reusable silicone electrodes and can be performed independently by experienced paraplegic swimmers except for transfer to water. The study results indicate that swimming speed can be increased by the proposed methods and spasticity can be reduced by prolonged swim sessions with tSCS and FES. The combination of stimulation with hydrotherapy might be a promising therapy for neurologic rehabilitation in incomplete SCI, stroke or multiples sclerosis patients. Therefore, further studies shall incorporate other neurologic disorders and investigate the potential benefits of FES and tSCS therapy in the water for gait and balance.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Exercise; Functional electrical stimulation (FES); Spinal cord injury (SCI); Swimming; Transcutaneous spinal cord stimulation (tSCS)

The adaptive drop foot stimulator – Multivariable learning control of foot pitch and roll motion in paretic gait - Seel, Thomas; Werner, Cordula; Schauer, Thomas (Medical Engineering & Physics, 2016)
Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients’ stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Biomedical systems; Drop foot syndrome; Foot motion assessment; Functional electrical stimulation; Gait phase detection; Inertial sensor; Iterative learning control; Multivariable control systems; Neuroprosthetics; Rehabilitation engineering; Validation by experiments

Using a single inertial sensor to control exergames for children with cerebral palsy - Mittag, Christina; Waldheim, Vivian; Krause, Axel; Seel, Thomas (Current Directions in Biomedical Engineering, 2022)
Introduction: Children with cerebral palsy (CCP) benefit from intensive arm training. Exergames that can be played at home offer the possibility to increase the frequency of therapy but require reliable and accurate real-time motion tracking via easy-to-use sensors in unsupervised settings and magnetically disturbed environments. Method: We propose an inertial-sensor-based method with a single sensor on the wrist for real-time tracking of the inclination of the forearm. The control parameter of the game was validated with an optical marker-based ground truth system. Results: First experiments with a therapist performing training movements in a healthy and simulated spastic manner show that the forearm inclination well captures the motion dynamics. The accuracy of the inertial-sensor-based measurement is validated with respect to the reference system in three healthy subjects. Orientation offsets between the inertial sensor and the forearm marker set in the range of 2° 6° and dynamic measurement errors about 3.1° were obtained. Conclusion: This work demonstrates that the proposed method is suitable for real-time control of exergames of CCP. The validation with an optical reference system showed that the forearm inclination can be used as for feedback and therapeutic progress monitoring.
CRIS-Team Scopus Importer:2022-09-30
Keywords: Cerebral palsy; Exergame; Forearm inclination; Inertial sensor; Motion capture validation; Rehabilitation

Validation of Non-Restrictive Inertial Gait Analysis of Individuals with Incomplete Spinal Cord Injury in Clinical Settings - Hassani, Roushanak Haji; Willi, Romina; Rauter, Georg; Bolliger, Marc; Seel, Thomas (Sensors, 2022)
Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate the analysis of walking patterns in clinical settings and daily life activities. However, most inertial gait analysis methods are unpractical in clinical settings due to the necessity of precise sensor placement, the need for well-performed calibration movements and poses, and due to distorted magnetometer data in indoor environments as well as nearby ferromagnetic material and electronic devices. To address these limitations, recent literature has proposed methods for self-calibrating magnetometer-free inertial motion tracking, and acceptable performance has been achieved in mechanical joints and in individuals without neurological disorders. However, the performance of such methods has not been validated in clinical settings for individuals with neurological disorders, specifically individuals with incomplete Spinal Cord Injury (iSCI). In the present study, we used recently proposed inertial motion-tracking methods, which avoid magnetometer data and leverage kinematic constraints for anatomical calibration. We used these methods to determine the range of motion of the Flexion/Extension (F/E) hip and Abduction/Adduction (A/A) angles, the F/E knee angles, and the Dorsi/Plantar (D/P) flexion ankle joint angles during walking. Data (IMU and OMC) of five individuals with no neurological disorders (control group) and five participants with iSCI walking for two minutes on a treadmill in a self-paced mode were analyzed. For validation purposes, the OMC system was considered as a reference. The mean absolute difference (MAD) between calculated range of motion of joint angles was 5.00 degrees, 5.02 degrees, 5.26 degrees, and 3.72 degrees for hip F/E, hip A/A, knee F/E, and ankle D/P flexion angles, respectively. In addition, relative stance, swing, double support phases, and cadence were calculated and validated. The MAD for the relative gait phases (stance, swing, and double support) was 1.7%, and the average cadence error was 0.09 steps/min. The MAD values for RoM and relative gait phases can be considered as clinically acceptable. Therefore, we conclude that the proposed methodology is promising, enabling non-restrictive inertial gait analysis in clinical settings.
CRIS-Team WoS Importer:2022-06-24

Voltage Stability and Reactive Power Sharing in Inverter-Based Microgrids with Consensus-Based Distributed Voltage Control - Schiffer, Johannes; Seel, Thomas; Raisch, Joerg; Sezi, Tevfik (IEEE Transactions on Control Systems Technology, 2016)
We propose a consensus-based distributed voltage control (DVC) that solves the problem of reactive power sharing in autonomous inverter-based microgrids with dominantly inductive power lines and arbitrary electrical topology. Opposed to other control strategies available thus far, the control presented here does guarantee a desired reactive power distribution in steady state while only requiring distributed communication among inverters, i.e., no central computing nor communication unit is needed. For inductive impedance loads and under the assumption of small phase angle differences between the output voltages of the inverters, we prove that the choice of the control parameters uniquely determines the corresponding equilibrium point of the closed-loop voltage and reactive power dynamics. In addition, for the case of uniform time constants of the power measurement filters, a necessary and sufficient condition for local exponential stability of that equilibrium point is given. The compatibility of the DVC with the usual frequency droop control for inverters is shown and the performance of the proposed DVC is compared with the usual voltage droop control via simulation of a microgrid based on the Conseil International des Grands Réseaux Electriques (CIGRE) benchmark medium voltage distribution network.
CRIS-Team Scopus Importer:2021-03-31
Keywords: Consensus algorithms; distributed cooperative control; droop control; inverters; microgrid control; microgrid stability; multiagent systems; power sharing; secondary control; smart grid applications; voltage stability

VQF: A Milestone in Accuracy and Versatility of 6D and 9D Inertial Orientation Estimation - Laidig, Daniel; Weygers, Ive; Bachhuber, Simon; Seel, Thomas (2022 25th International Conference on Information Fusion, FUSION 2022, 2022)
We present a versatile quaternion-based inertial orientation estimation filter (VQF). Inclination drift from gyroscope strapdown integration is corrected from specific force measurements that are low-pass filtered in an almost-inertial frame to effectively compensate for instantaneous accelerations and decelerations. Heading drift is corrected via a scalar heading offset. The resulting decoupled state representation enables simultaneous 6D and 9D orientation estimation. We systematically evaluated the method on a rich orientation estimation benchmark dataset and show that the proposed method clearly outperforms three of the currently most commonly adopted and accurate inertial orientation estimation filters. The filter is available as open-source software, and its parameters are tuned to work well for a wide range of movements and application scenarios. The fundamentally different filtering approach with a decoupled state representation and novel inclination correction resulted in an unprecedented level of accuracy, with 41% lower orientation estimation errors and a twice-as-high inclination accuracy compared to existing state-of-the-art methods. This facilitates new and exciting high-precision applications in the field of inertial motion tracking.
CRIS-Team Scopus Importer:2022-09-09

VQF: Highly accurate IMU orientation estimation with bias estimation and magnetic disturbance rejection - Laidig, Daniel; Seel, Thomas (Information Fusion, 2023)
The miniaturization of MEMS-based inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. The fundamental sensor fusion task of orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position and velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all eight literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. This improved accuracy with respect to the state of the art is observed not only in average but also for each of several different motion characteristics, as well as for gyroscope bias estimation. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method.
CRIS-Team Scopus Importer:2022-11-04
Keywords: AHRS; Attitude estimation; Gyroscope bias estimation; IMU; Inertial measurement unit; Inertial sensor; Magnetic disturbances; Orientation estimation; Sensor fusion