Hybrid Models for High-precision sEMG-based Joint Torque / Movement Prediciton for Wearable Robotics

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To enable the development of biosignal-driven robotic body support systems (e.g., exoskeletons) which continuously adapt to the behaviour of the user, fundamental challenges have to be resolved. The goal of the proposed project is to improve the prediction accuracy of EMG-driven muscle-joint models by introducing hybrid extensions to the model topology. The first challenges in current EMG-driven, model-based approaches are prediction inaccuracies with respect to torque which occur mainly in static-postures, slow joint rotations (mechanical drift at low EMG-activity) and transients to dynamic limb movements. To compensate for these inaccuracies, configurable sliding friction models and adaptive models of the joint geometry will be added to the established muscle-joint-setup. A further improvement will be achieved in an iterative optimization process through explorative adding of grey box models in parallel to the submodels. A second challenge in the prediction of joint movements is the fact that EMG-driven muscle models produce solely forces as output parameters. These forces cannot directly be assigned to joint torques and noisy joint torques cannot be used to determine the absolute joint angle. Hence, the mere observation of EMGs does not provide information on the current angle of the joint, which is connected to the muscles. However, with the help of EMG-arrays, the absolute position of muscle innervation zones (IZ), muscle-tendon intersections and thus also of muscle fiber lengths can be estimated (e.g., with beamforming algorithms). Using this information in combination with the submodels of muscles, tendons, and joints, a valid estimate of muscle and tendon lengths as well as the joint angle will be achieved. Based on EMG-measurements, this allows the elimination of position drift without explicit measurement of the joint angle. The resulting EMG-driven muscle-joint models also support a solution for a third challenge which is the latency reduction between the torques generated by an orthotic device/wearable robot and the biological arm (movement intention). Since the extended EMG-driven muscle-joint model predicts the joint torque in the next instant of time, a new control approach will be developed which uses this torque prediction and a model of the orthotic device to predict the phase shift. Based on this prediction, the controller can influence the torque generation in order to adapt the phase shift and hence the latency. Influences on the level of support are still subject to an additional support-level-controller. The real world applicability of the models will be evaluated based on a minimally actuated orthosis which will be built during the project. For parameterization and evaluation, movement- and EMG-data will be used which will be recorded in three experimental campaigns.