Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to disc...

Full description

Saved in:
Bibliographic Details
Main Authors: Betthauser, Joseph L., Krall, John T., Kaliki, Rahul R., Fifer, Matthew S., Thakor, Nitish V.
Format: Conference Proceeding
Language:English
Subjects:
Convolution
Data models
Electromyography
Microsoft Windows
Predictive models
Stability analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant (p
ISSN:1948-3554
DOI:10.1109/NER.2019.8717169