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Recent research has focused on improving the accuracy of gesture recognition by increasing the complexity of network models. However, training a complex model necessitates a significant amount of data, thereby escalating both user burden and computational costs. Moreover, owing to the considerable variability of surface electromyography (sEMG) signals across different users, conventional machine learning approaches reliant on a single feature fail to meet the demand for precise gesture recognition tailored to individual users. Therefore, to solve the problems of large computational cost and poor cross-user pattern recognition performance, we propose a feature selection method that combines mutual information, principal component analysis and the Pearson correlation coefficient (MPP). This method can filter out the optimal subset of features that match a specific user while combining with an SVM classifier to accurately and efficiently recognize the user\u2019s gesture movements. To validate the effectiveness of the above method, we designed an experiment including five gesture actions. The experimental results show that compared to the classification accuracy obtained using a single feature, we achieved an improvement of about 5% with the optimally selected feature as the input to any of the classifiers. This study provides an effective guarantee for user-specific fine hand movement decoding based on sEMG signals.<\/jats:p>","DOI":"10.3390\/s24123970","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T08:06:06Z","timestamp":1718784366000},"page":"3970","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hand Gesture Recognition Based on High-Density Myoelectricity in Forearm Flexors in Humans"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiaoling","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China"},{"name":"Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaigang","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China"},{"name":"Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137809","DOI":"10.1109\/ACCESS.2021.3118281","article-title":"RobHand: A Hand Exoskeleton with Real-Time EMG-Driven Embedded Control. 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