Data from: 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 non-signers. However, preliminary
systems rely on glove-type wearables or wired sensor arrays, which
constrain hand movement, reduce comfort, and require non-personalized
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 multi-link 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 88.4% and
88.9% 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.