ArticleNature communications2021
AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed.
- Large-Area Noise-Resilient Multiplexing Triboelectric Biomechanical Sensing on Clothing for High-Precision Full-Body Motion Capture in Wearables.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps.Journal of imaging · 2026Review
- Self-Powered Neuromorphic Systems Based on Tribotronics Synaptic Devices.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Bioinspired heliconical auxetic biofibers for intelligent biomechanical surveillance.Science advances · 2026Article
- Sensing Techniques in Virtual Reality for Human Interaction: A Bibliometric Analysis.Sensors (Basel, Switzerland) · 2026Review
- An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.Science advances · 2026Article
- Eco-Friendly Corn Silk-Based Triboelectric Nanogenerator Sensor for Automated Human Motion Recognition using Boosting-Driven Machine Learning.Precision chemistry · 2026Article
- Wearable thumb sleeves enabled by self-supervised learning with few stretchable sensors and few-shot data for switchable finger tasks.Science advances · 2026Article
- Artificial Intelligence-Enhanced Flexible Sensors for Human Motion and Posture Sensing.Sensors (Basel, Switzerland) · 2026Review
- 3D-Printed Ion-Conductive Hydrogels with Tunable Mechanical-Electrical Properties for Multimodal Sign Language Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies.Current aging science · 2026Review
- Skin-Integrated Soft Wearable XR Interfaces for Seamless and Realistic User Experience.Chemical reviews · 2025Review
- A novel deep transformer based CvT model for sign language recognition in visual communication.Scientific reports · 2025Article
- Finger joint angle and gesture estimation under natural conditions with a soft printed electrode array.APL bioengineering · 2025Article
- Strain-Sensitive Thermochromic Smart Electronic Skin for Joint and Spine Healthcare Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Wearable interactive full-body motion tracking and haptic feedback network systems with deep learning.Nature communications · 2025Article
- Article
- Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides.Nano-micro letters · 2025Article
- AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers' daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers.
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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.