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sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning

Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-ti...

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Autores principales: Zheng, Kaikui, Liu, Shuai, Yang, Jinxing, Al-Selwi, Metwalli, Li, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787629/
https://www.ncbi.nlm.nih.gov/pubmed/36560318
http://dx.doi.org/10.3390/s22249949
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author Zheng, Kaikui
Liu, Shuai
Yang, Jinxing
Al-Selwi, Metwalli
Li, Jun
author_facet Zheng, Kaikui
Liu, Shuai
Yang, Jinxing
Al-Selwi, Metwalli
Li, Jun
author_sort Zheng, Kaikui
collection PubMed
description Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a sliding window with long-term memory. Firstly, the key state modeled by GMM-HMMs is set as the condition. Then, the sliding window is used to dynamically look for the key state transition. The prediction results are given while finding the key state transition. To extend continuous multigesture action prediction, we use model pruning to improve reusability. Eight subjects participated in the experiment, and the results show that the average accuracy of continuous two-hand actions is 97% with a 70 ms time delay, which is better than LSTM (94.15%, 308 ms) and GRU (93.83%, 300 ms). In supplementary experiments with continuous four-hand actions, over 85% prediction accuracy is achieved with an average time delay of 90 ms.
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spelling pubmed-97876292022-12-24 sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning Zheng, Kaikui Liu, Shuai Yang, Jinxing Al-Selwi, Metwalli Li, Jun Sensors (Basel) Article Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a sliding window with long-term memory. Firstly, the key state modeled by GMM-HMMs is set as the condition. Then, the sliding window is used to dynamically look for the key state transition. The prediction results are given while finding the key state transition. To extend continuous multigesture action prediction, we use model pruning to improve reusability. Eight subjects participated in the experiment, and the results show that the average accuracy of continuous two-hand actions is 97% with a 70 ms time delay, which is better than LSTM (94.15%, 308 ms) and GRU (93.83%, 300 ms). In supplementary experiments with continuous four-hand actions, over 85% prediction accuracy is achieved with an average time delay of 90 ms. MDPI 2022-12-16 /pmc/articles/PMC9787629/ /pubmed/36560318 http://dx.doi.org/10.3390/s22249949 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zheng, Kaikui
Liu, Shuai
Yang, Jinxing
Al-Selwi, Metwalli
Li, Jun
sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning
title sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning
title_full sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning
title_fullStr sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning
title_full_unstemmed sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning
title_short sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning
title_sort semg-based continuous hand action prediction by using key state transition and model pruning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787629/
https://www.ncbi.nlm.nih.gov/pubmed/36560318
http://dx.doi.org/10.3390/s22249949
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