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

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 6; pp. 1707 - 1717
• Main Authors: Betthauser, Joseph L., Krall, John T., Bannowsky, Shain G., Levay, Gyorgy, Kaliki, Rahul R., Fifer, Matthew S., Thakor, Nitish V.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amputation; amputee; Amputees; Artificial Limbs; Biomedical monitoring; Computational modeling; Control systems; Convolution; Decoding; ED-TCN; Electromyographic (EMG); Electromyography; Hand; Humans; latency; Movement; Networks; Neural networks; Pattern recognition; Performance prediction; Prediction models; Predictive models; Prostheses; Prosthetics; Recurrent neural networks; reinforcement; Reinforcement learning; Reproducibility of Results; sequence; Signal classification; stability; Stability analysis; temporal convolutional network (TCN); Training; Wrist

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, i...