Hybrid Machine Learning-Neuromusculoskeletal Modeling for Control of Lower Limb Prosthetics (original) (raw)

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 2020

Abstract

Objective: Current limitations in Electromyography (EMG)-driven Neuromusculoskeletal (NMS) modeling for control of wearable robotics are the requirement of both Motion Capture for both an indoor system and numerous EMG electrodes. These limitations make the technology unsuitable for amputees with only proximal muscles, who need optimal prosthetic device control during everyday activities. Therefore, we developed a novel Machine Learning (ML)driven NMS model able to predict lower limb joint torque only from wearable sensors than can be embedded in a prosthetic device. Methods: After the NMS model calibration of a single healthy subject (OpenSim® software and Calibrated EMGInformed Neuromusculoskeletal Modelling CEINMS Toolbox), an additional ML layer (Gaussian Mixture Regressors) was added to the model to replace the MoCap-derived dependent variables with estimations obtained only from wearable sensors. An on-line open-loop Forward Dynamic (FD) simulation of the knee joint is compute...

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