{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T18:51:16Z","timestamp":1769194276776,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T00:00:00Z","timestamp":1556150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Municipal Science and Technology Program","award":["Z181100003118007"],"award-info":[{"award-number":["Z181100003118007"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91648207, 61673068"],"award-info":[{"award-number":["91648207, 61673068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accuracy and robustness of a classification. However, the features learned through deep learning are incomprehensible, and this issue has precluded the use of deep learning in clinical applications where model comprehension is required. In this paper, a generative flow model (GFM), which is a recent flourishing branch of deep learning, is used with a SoftMax classifier for hand-gesture classification. The proposed approach achieves     63.86 \u00b1 5.12 %     accuracy in classifying 53 different hand gestures from the NinaPro database 5. The distribution of all 53 hand gestures is modelled by the GFM, and each dimension of the feature learned by the GFM is comprehensible using the reverse flow of the GFM. Moreover, the feature appears to be related to muscle synergy to some extent.<\/jats:p>","DOI":"10.3390\/s19081952","type":"journal-article","created":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T10:44:06Z","timestamp":1556189046000},"page":"1952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["sEMG-Based Hand-Gesture Classification Using a Generative Flow Model"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2694-044X","authenticated-orcid":false,"given":"Wentao","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China"},{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Huaxin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Innovation Centre for Intelligent Robots and Systems, Beijing 100081, China"}]},{"given":"Rongyu","family":"Tang","sequence":"additional","affiliation":[{"name":"Beijing Innovation Centre for Intelligent Robots and Systems, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8134-1162","authenticated-orcid":false,"given":"Yiran","family":"Lang","sequence":"additional","affiliation":[{"name":"Beijing Innovation Centre for Intelligent Robots and Systems, Beijing 100081, China"}]},{"given":"Jiping","family":"He","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Qiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China"},{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/10798587.2015.1008735","article-title":"Simple and Computationally Efficient Movement Classification Approach for EMG-controlled Prosthetic Hand: ANFIS vs. Artificial Neural Network","volume":"21","author":"Fariman","year":"2015","journal-title":"Intell. 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