ArticleSensors (Basel, Switzerland)2025
A Smart Textile-Based Tactile Sensing System for Multi-Channel Sign Language Recognition.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps.Journal of imaging · 2026Review
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.