A Comprehensive Framework for the Modelling of Cartesian Force Output in Human Limbs - Sierotowicz, Marek; Lotti, Nicola; Rupp, Ruediger; Masia, Lorenzo; Castellini, Claudio (IEEE International Conference on Rehabilitation Robotics, 2022)
Neuromuscular functional electrical stimulation represents a valid technique for functional rehabilitation or, in the form of a neuroprosthesis, for the assistance of neurological patients. However, the selected stimulation of single muscles through surface electrodes remains challenging particularly for the upper extremity. In this paper, we present the MyoCeption, a comprehensive setup, which enables intuitive modeling of the user's musculoskeletal system, as well as proportional stimulation of the muscles with 16-bit resolution through up to 10 channels. The system can be used to provide open-loop force control, which, if coupled with an adequate body tracking system, can be used to implement an impedance control where the control loop is closed around the body posture. The system is completely self-contained and can be used in a wide array of scenarios, from rehabilitation to VR to teleoperation. Here, the MyoCeption's control environment has been experimentally validated through comparison with a third-party simulation suite. The results indicate that the musculoskeletal model used for the MyoCeption provides muscle geometries that are qualitatively similar to those computed in the baseline model.
CRIS-Team Scopus Importer:2022-10-28

Adaptive Filter for Biosignal-Driven Force Controls Preserves Predictive Powers of sEMG - Sierotowicz, Marek; Scheidl, Marc-Anton; Castellini, Claudio (2023 International Conference on Rehabilitation Robotics (ICORR), 2023)
Electromyographic controls based on machine learning rely on the stability and repeatability of signals related to muscular activity. However, such algorithms are prone to several issues, making them non-viable in certain applications with low tolerances for delays and signal instability, such as exoskeleton control or teleimpedance. These issues can become dramatic whenever, e.g., muscular activity is present not only when the user is trying to move but also for mere gravity compensation, which generally becomes more prominent the more proximal a muscle is. A substantial part of this instability is attributed to electromyography's inherent heteroscedasticity. In this study, we introduce and characterize an adaptive filter for sEMG features in such applications, which automatically adjusts its own cutoff frequency to suit the current movement intention. The adaptive filter is tested offline and online on a regression-based joint torque predictor. Both the offline and the online test show that the adaptive filter leads to more accurate prediction in terms of root mean square error when compared to the unfiltered prediction and higher responsiveness of the signal in terms of lag when compared to the output of a conventional low-pass filter.<br
Keywords: Torque; Machine learning algorithms; Exoskeletons; Dynamics; Adaptive filters; Low-pass filters; Muscles; Humans; Muscle; Skeletal; Electromyography; Exoskeleton Device; Movement; Algorithms

A direct spinal cord–computer interface enables the control of the paralysed hand in spinal cord injury - Souza de Oliveira, Daniela; Ponfick, Matthias; Braun, Dominik; Oßwald, Marius; Sierotowicz, Marek; Chatterjee, Satyaki; Weber, Douglas J.; Eskofier, Björn; Castellini, Claudio; Farina, Dario; Kinfe, Thomas Mehari; Del Vecchio, Alessandro (Brain, 2024)
The paralysis of the muscles controlling the hand dramatically limits the quality of life of individuals living with spinal cord injury (SCI). Here, with a non-invasive neural interface, we demonstrate that eight motor complete SCI individuals (C5-C6) are still able to task-modulate in real-time the activity of populations of spinal motor neurons with residual neural pathways. In all SCI participants tested, we identified groups of motor units under voluntary control that encoded various hand movements. The motor unit discharges were mapped into more than 10 degrees of freedom, ranging from grasping to individual hand-digit flexion and extension. We then mapped the neural dynamics into a real-time controlled virtual hand. The SCI participants were able to match the cue hand posture by proportionally controlling four degrees of freedom (opening and closing the hand and index flexion/extension). These results demonstrate that wearable muscle sensors provide access to spared motor neurons that are fully under voluntary control in complete cervical SCI individuals. This non-invasive neural interface allows the investigation of motor neuron changes after the injury and has the potential to promote movement restoration when integrated with assistive devices.<br
Keywords: spinal cord injury; motor neuron; motor unit; high density surface electromyography; neural interface

Deflection-Domain Passivity Control of Variable Stiffnesses Based on Potential Energy Reference - Panzirsch, Michael; Sierotowicz, Marek; Prakash, Revanth; Singh, Harsimran; Ott, Christian (IEEE Robotics and Automation Letters, 2022)
With emerging capabilities, robots will advance gradually into human environments in the near future. Thereby, safety and robustness is currently tackled through intrinsically soft robotics or variable impedances, mainly stiffnesses. In tele-operation, for instance, the control stiffness can be adapted to a measured arm impedance of the operator to stiffen the robot only when required for a manipulation task. Thus, humans or moving objects in the robot's environment are protected from hard collisions. Independent from its realization through hardware or software, the stability of the variation needs to be ensured through control strategies since energy is potentially introduced into the robotic system. This work presents a novel gradient-based passivity control concept for variable stiffnesses. In contrast to state-of-the-art methods, the approach is based on a potential energy storage reference and prevents phases of zero stiffness through deflection-domain control. I.e., according to the energy storage, the stiffness variation over the spring deflection is controlled to ensure passivity. Experiments confirm the functionality of the approach and its robustness against delayed communication and active environments.
CRIS-Team WoS Importer:2022-03-25

Empirical Time Complexity Analysis of Tractrix-Based Inverse Kinematics Algorithms - Sierotowicz, Marek; Aparna, A.; Chattaraj, Ritwik (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2024)
In soft robotics, hyper-redundant kinematic chains can be an effective way of modeling compliant objects and structures. Unfortunately, increasing the number of joints, the computational complexity of the calculations needed to solve the inverse kinematics problem for such systems increases sharply, especially when adopting traditional algorithms for IK solving, such as the Jacobian pseudo-inverse method. A computational procedure with a potentially lower complexity as a function of joint amount is one based on the tractrix curve. More recently, this same algorithm was formalized to function in three dimensions, but its time complexity was not adequately evaluated for cases where additional constraints are added to the kinematic solver, such as the chain being bound to a mechanical ground. Here we present the results of an empirical evaluation of the time requirements for convergence.
CRIS-Team Scopus Importer:2025-02-04

Force Myography Shows Higher Correlation to Force Output Compared to Average Rectified Value Electromyography Features - Sierotowicz, Marek; Castellini, Claudio; Anam, Khairul (Springer Science and Business Media Deutschland GmbH, 2025)
Compared to surface electromyography, force myography, or FMG, setups can be very cost-effective and robust, and they offer the advantage of not requiring skin contact. FMG as a modality is inherently correlated with muscular force, and should by extension be more directly correlated with measurable net force output in healthy subjects as well. In this study, we set out to prove this hypothesis by comparing the performance of a regression algorithm predicting isometric finger pressure based on FMG, as opposed to electromyography. Additionally, we demonstrated the effectiveness of a low-cost system, which concurrently with the robustness of FMG sensors, would make them an attractive solution to be used in resource-constrained settings, thus fostering a more widespread adoption of electronic prostheses in low- and middle income countries.
CRIS-Team Scopus Importer:2025-03-28

Omnidirectional endpoint force control through functional electrical stimulation - Sierotowicz, Marek; Castellini, Claudio (Biomedical Physics and Engineering Express, 2023)
Objective. In recent years, Functional Electrical Stimulation has found many applications both within and outside the medical field. However, most available wearable FES devices are not easily adaptable to different users, and most setups rely on task-specific control schemes. Approach. In this article, we present a peripheral stimulation prototype featuring a compressive jacket which allows to easily modify the electrode arrangement to better fit any body frame. Coupled with a suitable control system, this device can induce the output of arbitrary forces at the end-effector, which is the basis to facilitate universal, task-independent impedance control of the human limbs. Here, the device is validated by having it provide stimulation currents that should induce a desired force output. The forces exerted by the user as a result of stimulation are measured through a 6-axis force-torque sensor, and compared to the desired forces. Furthermore, here we present the offline analysis of a regression algorithm, trained on the data acquired during the aforementioned validation, which is able to reliably predict the force output based on the stimulation currents. Main results. Open-loop control of the output force is possible with correlation coefficients between commanded and measured force output direction up to 0.88. A twitch-based calibration procedure shows significant reduction of the RMS error in the online control. The regression algorithm trained offline is able to predict the force output given the injected stimulation with correlations up to 0.94, and average normalized errors of 0.12 RMS. Significance. A reliable force output control through FES is the first basis towards higher-level FES force controls. This could eventually provide full, general-purpose control of the human neuromuscular system, which would allow to induce any desired movement in the peri-personal space in individuals affected by e.g. spinal cord injury.
CRIS-Team Scopus Importer:2023-10-13
Keywords: Assistive robotics; FES; Force control; Impedance control of human limbs; Musculoskeletal model; Robot-inspired muscular control

Robot-Inspired Human Impedance Control Through Functional Electrical Stimulation - Sierotowicz, Marek; Castellini, Claudio (2023 International Conference on Rehabilitation Robotics (ICORR), 2023)
Functional Electrical Stimulation is an effective tool to foster rehabilitation of neurological patients suffering from impaired motor functions. It can also serve as an assistive device to compensate for compromised motor functions in the chronic phase occurring after a disease or trauma. In all cases, the dominant paradigm in FES applications is that of aiding specialized, task-specific movements, such as reaching or grasping. Usually this is achieved by targeting specific muscle groups which are associated to the targeted motion by experts. A general purpose, FES-based control theory capable of enabling neurological patients to achieve a wide range of positional goals in their peri-personal space is still missing. In this paper, we present an early analysis of the performance achievable through a muscular impedance control loop employing FES to actuate force and movement. The control is evaluated in a test where the user's upper limb is moved by means of an exonerve to a series of target positions on a plane without providing visual feedback nor requiring volitional effort. The results allow to characterize the performance of such a setup over time and to assess how well can it generalize over different target positions in the user's peri-personal space. The current study population also allows to evaluate the effects of user's experience with FES systems on the overall performance during the test. The results indicate that the proposed control loop can generalize well over different arm poses.<br
Keywords: Visualization; Sociology; Aerospace electronics; Muscles; Iron; User experience; Impedance; Humans; Electric Impedance; Upper Extremity; Electric Stimulation; Electric Stimulation Therapy; Movement

Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography - Sierotowicz, Marek; Brusamento, Donato; Schirrmeister, Benjamin; Connan, Mathilde; Bornmann, Jonas; Gonzalez-Vargas, Jose; Castellini, Claudio (Frontiers in Robotics and AI, 2022)
Repetitive or tiring tasks and movements during manual work can lead to serious musculoskeletal disorders and, consequently, to monetary damage for both the worker and the employer. Among the most common of these tasks is overhead working while operating a heavy tool, such as drilling, painting, and decorating. In such scenarios, it is desirable to provide adaptive support in order to take some of the load off the shoulder joint as needed. However, even to this day, hardly any viable approaches have been tested, which could enable the user to control such assistive devices naturally and in real time. Here, we present and assess the adaptive Paexo Shoulder exoskeleton, an unobtrusive device explicitly designed for this kind of industrial scenario, which can provide a variable amount of support to the shoulders and arms of a user engaged in overhead work. The adaptive Paexo Shoulder exoskeleton is controlled through machine learning applied to force myography. The controller is able to determine the lifted mass and provide the required support in real time. Twelve subjects joined a user study comparing the Paexo driven through this adaptive control to the Paexo locked in a fixed level of support. The results showed that the machine learning algorithm can successfully adapt the level of assistance to the lifted mass. Specifically, adaptive assistance can sensibly reduce the muscle activity’s sensitivity to the lifted mass, with an observed relative reduction of up to 31% of the muscular activity observed when lifting 2 kg normalized by the baseline when lifting no mass.
CRIS-Team Scopus Importer:2022-10-07
Keywords: adaptive support; exoskeletons; force myography; human–machine interaction; machine learning

Wearable Assistive Robotics and Its Interaction with Muscular Recruitment Strategies - Sierotowicz, Marek; Brusamento, Donato; Missiroli, Francesco; Bornmann, Jonas; Gonzalez-Vargas, Jose; Masia, Lorenzo; Castellini, Claudio (Springer Science and Business Media Deutschland GmbH, 2025)
Understanding the interaction between wearable robotics and muscular recruitment is crucial for efficient workload sharing. This paper examines previous experiments involving three assistive technologies: a tendon-driven glove, a compliant exoskeleton for overhead work, and a functional electrical stimulation prototype. The findings highlight the importance of transparent control interfaces and biomechanical optimization in enhancing the usability and effectiveness of wearable assistive devices.
CRIS-Team Scopus Importer:2025-03-28