{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:07:02Z","timestamp":1760710022518,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T00:00:00Z","timestamp":1571011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Advanced Innovation Center of Intelligent Robots and Systems","award":["2016IRS23"],"award-info":[{"award-number":["2016IRS23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Feature extraction, as an important method for extracting useful information from surface electromyography (SEMG), can significantly improve pattern recognition accuracy. Time and frequency analysis methods have been widely used for feature extraction, but these methods analyze SEMG signals only from the time or frequency domain. Recent studies have shown that feature extraction based on time-frequency analysis methods can extract more useful information from SEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwell transform (S-transform) to improve hand movement recognition accuracy from forearm SEMG signals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vector from forearm SEMG signals. Second, to reduce the amount of calculations and improve the running speed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of the feature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is used for recognizing hand movements. Experimental results show that the proposed feature extraction based on the S-transform analysis method can improve the class separability and hand movement recognition accuracy compared with wavelet transform and power spectral density methods.<\/jats:p>","DOI":"10.3390\/s19204457","type":"journal-article","created":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T12:14:05Z","timestamp":1571055245000},"page":"4457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy"],"prefix":"10.3390","volume":"19","author":[{"given":"Haotian","family":"She","sequence":"first","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinying","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Beijing Advanced Innovation Center for Intelligent Robot and System, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanchao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Yokoi","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Intelligent Robot and System, Beijing 100081, China"},{"name":"School of informatics and Engineering, University of Electro-Communications, Tokyo 163-8001, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/TBME.2006.883695","article-title":"A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand","volume":"53","author":"Chu","year":"2006","journal-title":"IEEE Trans. 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