{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:11:06Z","timestamp":1791994266341,"version":"4.4.0"},"reference-count":25,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and ICT","award":["IITP-2024-RS-2022-00156409"],"award-info":[{"award-number":["IITP-2024-RS-2022-00156409"]}]},{"name":"Ministry of Science and ICT","award":["P0017124"],"award-info":[{"award-number":["P0017124"]}]},{"name":"KIAT grant MOTIE","award":["IITP-2024-RS-2022-00156409"],"award-info":[{"award-number":["IITP-2024-RS-2022-00156409"]}]},{"name":"KIAT grant MOTIE","award":["P0017124"],"award-info":[{"award-number":["P0017124"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of gesture recognition technology, accurately detecting human gestures is crucial. In this research, ultrasonic transducers were utilized for gesture recognition. Due to the wide beamwidth of ultrasonic transducers, it is difficult to effectively distinguish between multiple objects within a single beam. However, they are effective at accurately identifying individual objects. To leverage this characteristic of the ultrasonic transducer as an advantage, this research involved constructing an ultrasonic array. This array was created by arranging eight transmitting transducers in a circular formation and placing a single receiving transducer at the center. Through this, a wide beam area was formed extensively, enabling the measurement of unrestricted movement of a single hand in the X, Y, and Z axes. Hand gesture data were collected at distances of 10 cm, 30 cm, 50 cm, 70 cm, and 90 cm from the array. The collected data were trained and tested using a customized Convolutional Neural Network (CNN) model, demonstrating high accuracy on raw data, which is most suitable for immediate interaction with computers. The proposed system achieved over 98% accuracy.<\/jats:p>","DOI":"10.3390\/s24206763","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"6763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Hand Gesture Recognition Using Ultrasonic Array with Machine Learning"],"prefix":"10.3390","volume":"24","author":[{"given":"Jaewoo","family":"Joo","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2874-9614","authenticated-orcid":false,"given":"Jinhwan","family":"Koh","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6869-2726","authenticated-orcid":false,"given":"Hyungkeun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5410","DOI":"10.1109\/TMM.2023.3333206","article-title":"Exploring Rich Semantics for Open-Set Action Recognition","volume":"26","author":"Hu","year":"2024","journal-title":"IEEE Trans. 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