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

doi:10.1109/NER.2019.8717169

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

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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 language	English (US)
Title of host publication	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Publisher	IEEE Computer Society
Pages	1046-1049
Number of pages	4
ISBN (Electronic)	9781538679210
DOIs	https://doi.org/10.1109/NER.2019.8717169
State	Published - May 16 2019
Event	9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States Duration: Mar 20 2019 → Mar 23 2019

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2019-March
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Conference

Conference	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Country/Territory	United States
City	San Francisco
Period	3/20/19 → 3/23/19

ASJC Scopus subject areas

Artificial Intelligence
Mechanical Engineering

Access to Document

10.1109/NER.2019.8717169

Cite this

Betthauser, J. L., Krall, J. T., Kaliki, R. R., Fifer, M. S., & Thakor, N. V. (2019). Stable Electromyographic Sequence Prediction during Movement Transitions using Temporal Convolutional Networks. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 (pp. 1046-1049). Article 8717169 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/NER.2019.8717169

Stable Electromyographic Sequence Prediction during Movement Transitions using Temporal Convolutional Networks. / Betthauser, Joseph L.; Krall, John T.; Kaliki, Rahul R. et al.
9th International IEEE EMBS Conference on Neural Engineering, NER 2019. IEEE Computer Society, 2019. p. 1046-1049 8717169 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Betthauser, JL, Krall, JT, Kaliki, RR, Fifer, MS & Thakor, NV 2019, Stable Electromyographic Sequence Prediction during Movement Transitions using Temporal Convolutional Networks. in 9th International IEEE EMBS Conference on Neural Engineering, NER 2019., 8717169, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2019-March, IEEE Computer Society, pp. 1046-1049, 9th International IEEE EMBS Conference on Neural Engineering, NER 2019, San Francisco, United States, 3/20/19. https://doi.org/10.1109/NER.2019.8717169

Betthauser JL, Krall JT, Kaliki RR, Fifer MS, Thakor NV. Stable Electromyographic Sequence Prediction during Movement Transitions using Temporal Convolutional Networks. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019. IEEE Computer Society. 2019. p. 1046-1049. 8717169. (International IEEE/EMBS Conference on Neural Engineering, NER). doi: 10.1109/NER.2019.8717169

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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.",

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AB - 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.

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Stable Electromyographic Sequence Prediction during Movement Transitions using Temporal Convolutional Networks

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