{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T05:54:44Z","timestamp":1722318884271},"reference-count":46,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T00:00:00Z","timestamp":1537833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB1300301"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671417"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 \u00b1 1.29 and 8.67 \u00b1 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 \u00b1 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.<\/jats:p>","DOI":"10.3390\/s18103226","type":"journal-article","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T15:12:26Z","timestamp":1537888346000},"page":"3226","source":"Crossref","is-referenced-by-count":59,"title":["Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation"],"prefix":"10.3390","volume":"18","author":[{"given":"Lingfeng","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6166-0390","authenticated-orcid":false,"given":"Shuai","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1533-4340","authenticated-orcid":false,"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}]},{"given":"Xun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/S1672-6529(16)60377-3","article-title":"Design and myoelectric control of an anthropomorphic prosthetic hand","volume":"14","author":"Wang","year":"2017","journal-title":"J. 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