{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T01:05:03Z","timestamp":1776474303195,"version":"3.51.2"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Electromyography (EMG) systems are essential for the advancement of neuroprosthetics and human-machine interfaces. However, the gap between low-density and high-density systems poses challenges to researchers in experiment design and knowledge transfer. Medium-density surface EMG systems offer a balanced alternative, providing greater spatial resolution than low-density systems while avoiding the complexity and cost of high-density arrays. In this study, we developed a research-friendly medium-density EMG system and evaluated its performance with eleven volunteers performing grasping tasks. To enhance decoding accuracy, we introduced a novel spatio-temporal convolutional neural network that integrates spatial information from additional EMG sensors with temporal dynamics. The results show that medium-density EMG sensors significantly improve classification accuracy compared to low-density systems while maintaining the same footprint. Furthermore, the proposed neural network outperforms traditional gesture decoding approaches. This work highlights the potential of medium-density EMG systems as a practical and effective solution, bridging the gap between low- and high-density systems. These findings pave the way for broader adoption in research and potential clinical applications.<\/jats:p>","DOI":"10.3389\/fnbot.2025.1531815","type":"journal-article","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T05:41:27Z","timestamp":1745991687000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Medium density EMG armband for gesture recognition"],"prefix":"10.3389","volume":"19","author":[{"given":"Eisa","family":"Aghchehli","sequence":"first","affiliation":[]},{"given":"Milad","family":"Jabbari","sequence":"additional","affiliation":[]},{"given":"Chenfei","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Dyson","sequence":"additional","affiliation":[]},{"given":"Kianoush","family":"Nazarpour","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1109\/TNSRE.2024.3435740","article-title":"Digital sensing systems for electromyography","volume":"32","author":"Aghchehli","year":"2024","journal-title":"IEEE Trans. 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