Original paper

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

Volume: 67, Issue: 6, Pages: 1707 - 1717
Published: Jun 1, 2020
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...
Paper Details
Title
Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement With Temporal Convolutional Networks
Published Date
Jun 1, 2020
Volume
67
Issue
6
Pages
1707 - 1717
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