Stable electromyographic sequence prediction during movement transitions using temporal convolutional networks

Joseph L. Betthauser, John T. Krall, Rahul R. Kaliki, Matthew S. Fifer, Nitish V. Thakor

Research output: Contribution to journalArticlepeer-review

Abstract

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< 0.001) performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Jan 8 2019

ASJC Scopus subject areas

  • General

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