{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T12:32:00Z","timestamp":1772886720561,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wheelchair sports are recognized as an international sport, and research and support are being promoted to increase the competitiveness of wheelchair sports. For example, an electromyogram can observe muscle activity. However, it is generally used under controlled conditions due to the complexity of preparing the measurement equipment and the movement restrictions imposed by cables and measurement equipment. It is difficult to perform measurements in actual competition environments. Therefore, in this study, we developed a method to estimate myoelectric potential that can be used in competitive environments and does not limit physical movement. We developed a deep learning model that outputs surface myoelectric potentials by inputting camera images of wheelchair movements and the measured values of inertial sensors installed on wheelchairs. For seven subjects, we estimated the myoelectric potential during chair work, which is important in wheelchair sports. As a result of creating an in-subject model and comparing the estimated myoelectric potential with the myoelectric potential measured by an electromyogram, we confirmed a correlation (correlation coefficient 0.5 or greater at a significance level of 0.1%). Since this method can estimate the myoelectric potential without limiting the movement of the body, it is considered that it can be applied to the performance evaluation of wheelchair sports.<\/jats:p>","DOI":"10.3390\/s22041615","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1615","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Learning-Based Myoelectric Potential Estimation Method for Wheelchair Operation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8513-0204","authenticated-orcid":false,"given":"Shimpei","family":"Aihara","sequence":"first","affiliation":[{"name":"Department of Sport Science, Japan Institute of Sports Sciences, 3-15-1 Nishigaoka, Kita-ku, Tokyo 115-0056, Japan"},{"name":"School of Creative Science and Engineering, Waseda University, Wasedamachi-27, Shinjuku-ku, Tokyo 169-8050, Japan"}]},{"given":"Ryusei","family":"Shibata","sequence":"additional","affiliation":[{"name":"Graduate School of Information and Management Systems Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata 940-2188, Japan"}]},{"given":"Ryosuke","family":"Mizukami","sequence":"additional","affiliation":[{"name":"Graduate School of Information and Management Systems Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata 940-2188, Japan"}]},{"given":"Takara","family":"Sakai","sequence":"additional","affiliation":[{"name":"Graduate School of Information and Management Systems Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata 940-2188, Japan"}]},{"given":"Akira","family":"Shionoya","sequence":"additional","affiliation":[{"name":"Graduate School of Information and Management Systems Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata 940-2188, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"van der Slikke, R.M.A., Berger, M.A.M., Bregman, D.J.J., and Veeger, D.H.E.J. 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