A Smart Textile-Based Tactile Sensing System for Multi-Channel Sign Language Recognition
Year: 2025
Type: article
Abstract: Sign language recognition plays a crucial role in enabling communication for deaf individuals, yet current methods face limitations such as sensitivity to lighting conditions, occlusions, and lack of adaptability in diverse environments. This study presents a wearable multi-channel tactile sensing system based on smart textiles, designed to capture subtle wrist and finger motions for static sign language recognition. The system leverages triboelectric yarns sewn into gloves and sleeves to construct a skin-conformal tactile sensor array, capable of detecting biomechanical interactions through contact and deformation. Unlike vision-based approaches, the proposed sensor platform operates independently of environmental lighting or occlusions, offering reliable performance in diverse conditions. Experimental validation on American Sign Language letter gestures demonstrates that the proposed system achieves high signal clarity after customized filtering, leading to a classification accuracy of 94.66%. Experimental results show effective recognition of complex gestures, highlighting the system's potential for broader applications in human-computer interaction.
Source: Sensors
DOI: 10.3390/s25154602
Authors: Keran Chen, Longnan Li, Qinyao Peng, Mengyuan He, Liyun Ma, Xinxin Li, Zhenyu Lu
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: Royal Society
OpenAlex ID: W4412915918
