# A Smart Textile-Based Tactile Sensing System for Multi-Channel Sign Language Recognition

## TL;DR

A smart textile system is developed to recognize sign language gestures through tactile sensing, offering reliable performance regardless of lighting or occlusions.

## Contribution

A wearable tactile sensing system using triboelectric yarns is introduced for sign language recognition, independent of environmental conditions.

## Key findings

- The system achieved a classification accuracy of 94.66% for American Sign Language letter gestures.
- The tactile sensor array effectively detects subtle wrist and finger motions for static sign recognition.
- The system performs reliably in diverse environments, overcoming limitations of vision-based methods.

## 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.