{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T03:12:40Z","timestamp":1774321960541,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,21]],"date-time":"2023-05-21T00:00:00Z","timestamp":1684627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51705475"],"award-info":[{"award-number":["51705475"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person\u2019s intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the \u03b1 Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1\u201320 kHz (adjustable). The \u03b1 Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the \u03b1 Armband and extracted three different image samples from the time\u2013frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the \u03b1 Armband is highly practical and robust, with excellent development potential.<\/jats:p>","DOI":"10.3390\/s23104940","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T02:28:42Z","timestamp":1684722522000},"page":"4940","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["High-Performance Surface Electromyography Armband Design for Gesture Recognition"],"prefix":"10.3390","volume":"23","author":[{"given":"Ruihao","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingping","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huixin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhi","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunze","family":"Li","sequence":"additional","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bawa, A., and Banitsas, K. 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