Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement With Temporal Convolutional Networks.

ABSTRACT: Prediction of movement intentions from electromyographic (EMG) signals is typically performed with a pattern recognition approach, wherein a short dataframe of raw EMG is compressed into an instantaneous feature-encoding that is meaningful for classification. However, EMG signals are time-varying, implying that a frame-wise approach may not sufficiently incorporate temporal context into predictions, leading to erratic and unstable prediction behavior.

Objective

We demonstrate that sequential prediction models and, specifically, temporal convolutional networks are able to leverage useful temporal information from EMG to achieve superior predictive performance.

Methods

We compare this approach to other sequential and frame-wise models predicting 3 simultaneous hand and wrist de

SUBMITTER: Betthauser JL 

PROVIDER: S-EPMC10497232 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature