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

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.