首页|Robust Machine Learning Mapping of sEMG Signals to Future Actuator Commands in Biomechatronic Devices
Robust Machine Learning Mapping of sEMG Signals to Future Actuator Commands in Biomechatronic Devices
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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 horizon.The input of the RNN consisted of interrupted sEMG signals(to mimic signal disconnections)and previous kinematic signals of the assistive system.The 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 dataset.This 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 system.Training with these interrupted input signals significantly improves the regression accuracy in the case of sEMG signal disconnection.This 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 robots.Future work should study the application to prosthesis control as well as the repeatability of the high-level controller with electrode shift.The low-level hierarchical controller that manages the human-robot interaction,the assistance/resistance strategy,and the actuator coordination should also be studied.
Myoelectric-based controlSurface electromyographyMachine learningMultibody system dynamicsExoskeletonBionic
Ali Nasr、Sydney Bell、Rachel L.Whittaker、Clark R.Dickerson、John McPhee
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Systems Design Engineering,University of Waterloo,Waterloo N2L 3G1,Canada
Kinesiology and Health Sciences,University of Waterloo,Waterloo N2L 3G3,Canada
Canada Research Chairs ProgramNatural Sciences and Engineering Research Council of Canada
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