Dataset Information

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

ABSTRACT: 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, implying that a frame-wise approach may not sufficiently incorporate temporal context into predictions, leading to erratic and unstable prediction behavior.

Objective

We demonstrate that sequential prediction models and, specifically, temporal convolutional networks are able to leverage useful temporal information from EMG to achieve superior predictive performance.

Methods

We compare this approach to other sequential and frame-wise models predicting 3 simultaneous hand and wrist degrees-of-freedom from 2 amputee and 13 non-amputee human subjects in a minimally constrained experiment. We also compare these models on the publicly available Ninapro and CapgMyo amputee and non-amputee datasets.

Results

Temporal convolutional networks yield predictions that are more accurate and stable than frame-wise models, especially during inter-class transitions, with an average response delay of 4.6 ms and simpler feature-encoding. Their performance can be further improved with adaptive reinforcement training.

Significance

Sequential models that incorporate temporal information from EMG achieve superior movement prediction performance and these models allow for novel types of interactive training.

Conclusions

Addressing EMG decoding as a sequential modeling problem will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems.

SUBMITTER: Betthauser JL

PROVIDER: S-EPMC10497232 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Betthauser Joseph L JL Krall John T JT Bannowsky Shain G SG Levay Gyorgy G Kaliki Rahul R RR Fifer Matthew S MS Thakor Nitish V NV

IEEE transactions on bio-medical engineering 20190923 6

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, implying that a frame-wise approach may not sufficiently incorporate temporal context into predictions, leading to erratic and unstable prediction behavior.<h4>Objective</h4>We demonstrate that sequent ...[more]

PMID: 31545709

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Project description:MotivationIn addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-residue contacts, such as solvent accessibility, predicted secondary structure, and scores from other contact prediction methods. It is unclear how much of this information is needed to achieve state-of-the-art results. Here, we show that using deep neural network models, simple alignment statistics contain sufficient information to achieve state-of-the-art precision. Our prediction method, DeepCov, uses fully convolutional neural networks operating on amino-acid pair frequency or covariance data derived directly from sequence alignments, without using global statistical methods such as sparse inverse covariance or pseudolikelihood estimation.ResultsComparisons against CCMpred and MetaPSICOV2 show that using pairwise covariance data calculated from raw alignments as input allows us to match or exceed the performance of both of these methods. Almost all of the achieved precision is obtained when considering relatively local windows (around 15 residues) around any member of a given residue pairing; larger window sizes have comparable performance. Assessment on a set of shallow sequence alignments (fewer than 160 effective sequences) indicates that the new method is substantially more precise than CCMpred and MetaPSICOV2 in this regime, suggesting that improved precision is attainable on smaller sequence families. Overall, the performance of DeepCov is competitive with the state of the art, and our results demonstrate that global models, which employ features from all parts of the input alignment when predicting individual contacts, are not strictly needed in order to attain precise contact predictions.Availability and implementationDeepCov is freely available at https://github.com/psipred/DeepCov.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

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

Objective

Methods

Results

Significance

Conclusions

Publications

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

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