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Inspired by the audio signal analysis outcome that features extracted with Mel Frequency Cepstral Coefficient (MFCC) empower better representation, this paper proposes a comparative study of a gesture recognition method by using and improving with the MFCC features of sEMG. Comparing and combining with the conventional time-domain and frequency-domain features, different learning-based techniques are deployed to evaluate the performance of the proposed approach on the NinaPro datasets. The proposed approach was evaluated on the NinaPro-DB1 and NinaPro-DB2 datasets, achieving the improvements of 3.42% and 3.67%, respectively, in terms of the highest accuracy using the standard MFCC method. Correspondingly, when combined with the improved MFCC, the accuracy was further increased, reaching the maximum values of 89.82% and 87.82%, respectively, on the two datasets. The impact on the performance reveals the effectiveness of MFCC, and the results show that the proposed method has the potential to realize high-precision gesture recognition. <\/jats:p>","DOI":"10.1142\/s0219843623500172","type":"journal-article","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T01:57:52Z","timestamp":1696039072000},"source":"Crossref","is-referenced-by-count":4,"title":["Application and Improvement of MFCC in Gesture Recognition with Surface Electromyography"],"prefix":"10.1142","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3429-7321","authenticated-orcid":false,"given":"Shiwei","family":"Zhu","sequence":"first","affiliation":[{"name":"Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. 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