An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation

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.

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Publication Details

Journal
Science Advances
Published
2026-05-01
DOI
https://doi.org/10.1126/sciadv.aec8995
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation

Ki Jun Yu, T Kim, Yejee Shin, Doyun Kim et al.
Science Advances
Hand Gesture Recognition Systems
article

An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation

Ki Jun Yu, T Kim, Yejee Shin, Doyun Kim, Yeonsik Choi, 백준형, Yunkyu Park, In Sik Min, Kyubeen Kim, Sang Hoon Park, Byunghwan Jeon, Jongyoo Kim, Young Uk Cho, Kyowon Kang, D K Hwang, Yeeun Lee, J Hong, Jiwon Lee, Jaejin Park, Sunho Park
article en

Abstract

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.

Science AdvancesVol. 12(18)
Georgia Institute of Technology (US), Incheon National University (KR), Yonsei University (KR), Washington University in St. Louis (US), Korean Association Of Science and Technology Studies (KR), Yonsei University College of Dentistry, University of Virginia (US), Ajou University (KR), Korea Institute of Science and Technology (KR), Hankuk University of Foreign Studies (KR)
National Research Foundation of Korea
Quality Education
Openalex Percentile: Top 10%
Hand Gesture Recognition Systems
DOIOpen Access PDFOpenAlex
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