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Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

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

Computer Vision and Pattern Recognition · Submitted 8 Jan 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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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arXiv ID1901.02442 (version 1)
Pith IDWABLOJ7K, Pith's permanent record of this paper and its review
CitationJohn T. Krall, Joseph L. Betthauser, Matthew S. Fifer, Nitish V. Thakor, Rahul R. Kaliki. "Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks." arXiv:1901.02442 (2019). https://pith.science/paper/WABLOJ7K
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,653 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.98 · controlled comparison 0.44 · randomized assignment 0.02 · states quantitative result 0.94 · compares with prior results 0.96 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. background 0.99 0.08 0.03 asserted 0.67
s1 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. contribution 0.51 0.92 0.65 asserted 1.00
s2 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. method 0.70 0.96 0.06 asserted 1.00
s3 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. result 1.00 0.93 0.66 asserted 1.00

What kind of paper the classifier reports this is: empirical study 0.61 · evidence from observation or experiment 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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