ArticleScience advances2026
An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.
Jaejin Park, Yejee Shin, In Sik Min, Yeeun Lee, Jiwon Lee, Jung-Hoon Hong, Sunho Park, Sang Hoon Park, Junhyeong Baek, Taemin Kim and 10 more
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In one paragraphArticle in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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20 authors.
Jaejin ParkDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0009-0000-1919-3306 Yejee ShinDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0009-0008-0678-6354 In Sik MinDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0009-0003-0314-4915 Yeeun LeeDepartment of Artificial Intelligence, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0009-0007-2611-2181 Jiwon LeeDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0009-0008-2015-2147 Jung-Hoon HongDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0009-0002-5215-3642 Sunho ParkDepartment of Materials Science and Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.
Sang Hoon ParkDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.
Junhyeong BaekDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0009-0006-4677-8055 Taemin KimDepartment of Electrical and Computer Engineering, Ajou University, Suwon 16499, South Korea.ORCID 0000-0002-5405-6603 Kyubeen KimDepartment of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63105, USA.ORCID 0000-0002-7425-2871 Doyun KimDepartment of Chemistry, Hankuk University of Foreign Studies (HUFS), Yongin 17035, Republic of Korea.
Yunkyu ParkDepartment of Physics, Memory and Catalyst Research Center, Hankuk University of Foreign Studies, Yongin 17035 Republic of Korea.
Jongyoo KimDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0000-0002-2435-9195 Young Uk ChoDepartment of Biomedical & Robotics Engineering, Incheon National University, Yeonsu-gu, Incheon 22012, Republic of Korea.ORCID 0000-0003-2578-5177 Yeonsik ChoiDepartment of Materials Science and Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0000-0003-3813-3442 Byunghwan JeonDivision of Computer Engineering Hankuk University of Foreign Studies, Yongin 17035, South Korea.ORCID 0000-0002-0414-1762 Kyowon KangDepartment of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea.ORCID 0000-0002-6892-3069 Dosik HwangDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0000-0002-2217-2837 Ki Jun YuDepartment of Electrical and Electronic Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.ORCID 0000-0002-2922-2702 Funding
No grant is acknowledged in the PubMed record.
6 · The paper itselfAbstract
Sign language translation systems have long aimed to bridge the communication between signers and nonsigners. However, preliminary systems rely on glove-type wearables or wired sensor arrays, which constrain hand movement, reduce comfort, and require nonpersonalized sensor positions that limit adaptability across users. Here, we introduce a wirelessly connected, ring-type sign language translator (WRSLT) designed to overcome these limitations by enabling full finger mobility through independent sensor rings and multilink communication. The system supports static and dynamic gesture detection using selected fingers via quantitative relevance analysis and achieves robust user-independent performance without per-user calibration. WRSLT demonstrated high recognition accuracy on large-scale datasets comprising 100 American Sign Language and 100 International Sign Language words, achieving 88.3 and 88.5% accuracy, respectively, under unseen-user conditions (i.e., test users not included in model training). Furthermore, a custom sequential word detection framework enables sentence-level translation from continuous signing input without requiring separate training on entire sentence structures.
Indexed as
Artificial IntelligenceSign LanguageWearable Electronic DevicesGesturesHumans
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PMID42066080
PMCPMC13134630
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LicenceCC BY-NC
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