Dispelling Four Challenges in Inertial Motion Tracking with One Recurrent Inertial Graph-based Estimator (RING) - Bachhuber, Simon; Weygers, Ive; Seel, T. (IFAC-PapersOnLine, 2024)
In this paper, we extend the Recurrent Inertial Graph-based Estimator (RING), a novel neural-network-based solution for Inertial Motion Tracking (IMT), to generalize across a large range of sampling rates, and we demonstrate that it can overcome four real-world challenges: inhomogeneous magnetic fields, sensor-to-segment misalignment, sparse sensor setups, and nonrigid sensor attachment. RING can estimate the rotational state of a three-segment kinematic chain with double hinge joints from inertial data, and achieves an experimental mean-absolute-(tracking)-error of 8.10 ± 1.19 degrees if all four challenges are present simultaneously. The network is trained on simulated data yet evaluated on experimental data, highlighting its remarkable ability to zero-shot generalize from simulation to experiment. We conduct an ablation study to analyze the impact of each of the four challenges on RING's performance, we showcase its robustness to varying sampling rates, and we demonstrate that RING is capable of real-time operation. This research not only advances IMT technology by making it more accessible and versatile but also enhances its potential for new application domains including non-expert use of sparse IMT with nonrigid sensor attachments in unconstrained environments.
CRIS-Team Scopus Importer:2024-12-13
Keywords: Inertial Measurement Units; Magnetometer-free; Orientation Estimation; Recurrent Neural Networks; Sensor-to-Segment Alignment; Sparse Sensing

Gaussian process-based nonlinearity compensation for pneumatic soft actuators - Pawluchin, Alexander; Meindl, Michael; Weygers, Ive; Seel, Thomas; Boblan, Ivo (At-Automatisierungstechnik, 2024)
Highly compliant Pneumatic Soft Actuators (PSAs) have the potential to perform challenging tasks in a broad range of applications that require shape-adaptive capabilities. Achieving accurate tracking control for such actuators with complex geometries and material compositions typically involves many time-consuming and laborious engineering steps. In this work, we propose a data-driven learning-based control approach to address reference tracking tasks, incorporating self-adaptation in situ. We utilize a short interaction maneuver, recorded a priori, to collect the quasi-static data affected by severe hysteresis. Besides a linear feedback controller, we use two Gaussian process models to predict the feedforward control input to compensate for the nonlinearity in a one-shot learning setting. The proposed control approach demonstrates accurate tracking performance even under realistic varying configurations, such as alterations in mass and orientation, without any parameter tuning. Notably, training was achieved with only 25-50 s of experimental interaction, which emphasizes the plug-and-play capabilities in diverse real-world applications.
CRIS-Team Scopus Importer:2024-05-24
Keywords: feedforward control; Gaussian process; hysteresis modeling; pneumatic soft actuator; reference tracking; soft robotics

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

Plug-and-Play Sparse Inertial Motion Tracking With Sim-to-Real Transfer - Bachhuber, Simon; Lehmann, Dustin; Dorschky, Eva; Koelewijn, Anne; Seel, Thomas; Weygers, Ive (IEEE Sensors Letters, 2023)
Inertial measurement units (IMUs) are used for inertial motion tracking (IMT) in a growing number of applications as sensor fusion methods are being advanced in three directions: magnetometer-free IMT methods that eliminate the effect of magnetic disturbances; sparse IMT approaches that lead to reduced setup complexity; and automatic self-calibration of sensor-to-segment positions or orientations. In this letter, we propose an approach that combines all three achievements and, for the first time, enables plug-and-play, magnetometer-free, and sparse IMT. This is accomplished by training a recurrent neural-network-based observer (RNNo) on just-in-time generated simulated motion data of kinematic chains. We demonstrate that domain-specific training data augmentations lead to a trained RNNo which zero shot generalizes to previously unseen experimental data and, thus, overcomes the sim-to-real gap. The trained RNNo achieves a tracking error of < 4 degrees when estimating the relative pose of a three-segment kinematic chain with two hinge joints. The proposed method offers a novel simulation-data-driven approach for solving complex sparse sensing problems while assuring robust and plug-and-play generalizability to experimental data.
CRIS-Team Scopus Importer:2023-10-06
Keywords: inertial measurement units (IMUs); magnetometer-free; recurrent neural networks; sensor fusion; Sensor signal processing; sparse sensing

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

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

Validation of IMU against optical reference and development of open-source pipeline: proof of concept case report in a participant with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant - Ahmed, Kirstin; Taheri, Shayan; Weygers, Ive; Ortiz-Catalan, Max (Journal of neuroEngineering and rehabilitation, 2024)
Background: Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of "real" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in a case report with participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation walking over level ground. We hypothesis that by using this novel pipeline, we can validate IMU motion capture data, to a clinically acceptable degree. Results: Average RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant were 2.26° (IQR = 1.08°). Joint level average RMSE between the two systems from the TFA ranged from 1.66° to 3.82° and from 1.21° to 5.46° in the non-amputated participant. In plane average RMSE between the two systems from the TFA ranged from 2.17° (coronal) to 3.91° (sagittal) and from 1.96° (transverse) to 2.32° (sagittal) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant and resulted in ‘excellent’ similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40% (knee level) to 54.54% (pelvis level) and from 2.18% to 36.01% in the non-amputated participant. Conclusions: We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI and have proved our hypothesis.
CRIS-Team Scopus Importer:2024-08-09
Keywords: Gait analysis; IMU motion capture; Inertial measurement unit; Joint kinematics; Motion analysis; Motion capture validation; Orientation estimation algorithm; Osseointegration; Prosthetic gait; Transfemoral amputation gait

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