Human Joint Torque Modelling With MMG and EMG During Lower Limb Human-Exoskeleton Interaction

نویسندگان

چکیده

Human-robot cooperation is vital for optimising powered assist of lower limb exoskeletons (LLEs). Robotic capacity to intelligently adapt human force, however, demands a fusion data from exoskeleton and user state smooth human-robot synergy. Muscle activity, mapped through electromyography (EMG) or mechanomyography (MMG) widely acknowledged as usable sensor input that precedes the onset joint torque. However, competing complementary information between such physiological feedback yet be exploited, even assessed, predictive LLE control. We investigate benefits EMG MMG sensing modalities means calculating torque assist-as-needed (AAN) Three biomechanically agnostic machine learning approaches, linear regression, polynomial neural networks, are implemented prediction during human-exoskeleton interaction experiments. Results demonstrate predicts with slightly accuracy than isometric interaction. Performance comparable dynamic exercise. Neural network models achieve best performance both (94.8±0.7% 97.6±0.8% (Mean ± SD)) at expense training time implementation complexity. This investigation represents first LLEs their comparison EMG. provide our implementations future investigations (https://github.com/cic12/ieee_appx).

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ژورنال

عنوان ژورنال: IEEE robotics and automation letters

سال: 2021

ISSN: ['2377-3766']

DOI: https://doi.org/10.1109/lra.2021.3097832