Title: Optimal EMG Sensor Placement for Robotic Prosthetics with Sequential Adaptive Functional Estimation
Abstract: Robotic hand prostheses are capable of translating multiple forearm electromyography (EMG) signals into finger and wrist movement through a control strategy. Training the control strategy involves an analysis of concurrent, longitudinal movement and EMG data collected across many forearm muscles, producing highly correlated EMG signals. To improve the prosthetic’s prediction accuracy and stability, we want to identify a control strategy that requires as few EMG signals as possible. We develop a control strategy based on a novel EMG-based functional linear model that accounts for the underlying biomechanics of hand movement, leading to natural, continuous movement of the prosthetic. The model is made parsimonious and interpretable through our proposed Sequential Adaptive Functional Estimation (SAFE) procedure motivated by the adaptive and relaxed group LASSO techniques. SAFE is shown to identify clinically important EMG signals with negligible false positive rates for an able-bodied subject.
This webinar acknowledges the winners of the 2021 SPES (ASA Section on Physical and Engineering Sciences) Award.
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