{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:16:13Z","timestamp":1783973773986,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technology Innovation Program","award":["00144288"],"award-info":[{"award-number":["00144288"]}]},{"name":"Technology Innovation Program","award":["00144290"],"award-info":[{"award-number":["00144290"]}]},{"name":"Ministry of Trade, Industry and Energy (MOTIE, Korea)","award":["00144288"],"award-info":[{"award-number":["00144288"]}]},{"name":"Ministry of Trade, Industry and Energy (MOTIE, Korea)","award":["00144290"],"award-info":[{"award-number":["00144290"]}]},{"name":"IDEC","award":["00144288"],"award-info":[{"award-number":["00144288"]}]},{"name":"IDEC","award":["00144290"],"award-info":[{"award-number":["00144290"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, human\u2013machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and intuitively control the device. Among the various sensors used in the HGR system, the surface electromyography (sEMG) sensor is independent of the acquisition environment, easy to wear, and requires a small amount of data. Focusing on these advantages, previous sEMG-based HGR systems used several sensors or complex deep-learning algorithms to achieve high classification accuracy. However, systems that use multiple sensors are bulky, and embedded platforms with complex deep-learning algorithms are difficult to implement. To overcome these limitations, we propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor, which is implemented on a field-programmable gate array (FPGA). The proposed HGR system classifies nine dynamic gestures that can be useful in real life rather than static gestures that can be classified relatively easily. Raw sEMG data collected from a dynamic gesture are converted into a spectrogram with information in the time-frequency domain and transferred to the classifier. As a result, the proposed HGR system achieved 95.4% classification accuracy, with a computation time of 14.1 ms and a power consumption of 91.81 mW.<\/jats:p>","DOI":"10.3390\/s23031436","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:01:18Z","timestamp":1675044078000},"page":"1436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["sEMG-Based Hand Gesture Recognition Using Binarized Neural Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0015-1768","authenticated-orcid":false,"given":"Soongyu","family":"Kang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2693-5942","authenticated-orcid":false,"given":"Haechan","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8469-8498","authenticated-orcid":false,"given":"Chaewoon","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3874-9685","authenticated-orcid":false,"given":"Yunseong","family":"Sim","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9344-7052","authenticated-orcid":false,"given":"Seongjoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea"},{"name":"Department of Convergence Engineering of Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2299-9911","authenticated-orcid":false,"given":"Yunho","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea"},{"name":"Department of Smart Air Mobility, Korea Aerospace University, Goyang-si 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1109\/LRA.2021.3089999","article-title":"Multi-sensor guided hand gesture recognition for a teleoperated robot using a recurrent neural network","volume":"6","author":"Qi","year":"2021","journal-title":"IEEE Robot. 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