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robust Machine Learning Mapping of sEMG Signals to Future Actuator Commands in Biomechatronic Devices

Journal of Bionic Engineering 2024年第1期21卷 270-287页

作者： Ali Nasr Sydney Bell rachel L.Whittaker clark r.dickerson John McPhee Systems Design Engineering University of WaterlooWaterloo N2L 3G1Canada Kinesiology and Health Sciences University of WaterlooWaterloo N2L3G3Canada

A machine learning model for regression of interrupted Surface Electromyography(sEMG)signals to future control-oriented signals(e.g.,robot’s joint angle and assistive torque)of an active biomechatronic device for high-level myoelectric-based hierarchical control is proposed.A recurrent Neural Network(rNN)was trained using output data,initially obtained from offline optimization of the biomechatronic(human–robot)device and shifted by the prediction *** input of the rNN consisted of interrupted sEMG signals(to mimic signal disconnections)and previous kinematic signals of the assistive *** rNN with a 0.1-s prediction horizon could predict the control-oriented joint angle and assistive torque with 92%and 86.5%regression accuracy,respectively,for the test *** proposed approach permits a fast,predictive,and direct estimation of control-oriented signals instead of an iterative process that optimizes assistive torque in the inverse dynamic simulation of a multibody human–robot *** with these interrupted input signals significantly improves the regression accuracy in the case of sEMG signal *** robust Predictive Control-oriented Machine Learning(robust-MuscleNET)model can support volitional high-level myoelectric-based control of biomechatronic devices,such as exoskeletons,prostheses,and assistive/resistive *** work should study the application to prosthesis control as well as the repeatability of the high-level controller with electrode *** low-level hierarchical controller that manages the human–robot interaction,the assistance/resistance strategy,and the actuator coordination should also be studied.

关键词： Myoelectric-based control-Surface electromyography Machine learning Multibody system dynamics Exoskeleton Bionic

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