Ionic Composite Nanofiber Membrane‐Based Ultra‐Sensitive and Anti‐Interference Flexible Pressure Sensors for Intelligent Sign Language Recognition

Abstract The escalating population affected by deafness and hearing loss demands solutions to revolutionize traditional sign language recognition based on interpreters. The emergence of wearable sensors could provide a promising alternative but suffer from poor mechanical stability, external signal inferences, less sensitivity, and signal hysteresis. Herein, an ultrasensitive and anti‐interference flexible ionic composite nanofiber membranes (ICNM) based pressure sensor is developed through precisely manipulating polymer‐blending interactions, where ionic liquid and silver nanowire additives are well anchored on thermoplastic polyurethane polymer scaffolds without leakage via unique hydrogen bond networks, leading to a substantial areal capacitance of 20 µF cm −2 , and effectively mitigating external noise. The ICNM‐based sensor showcases high sensitivity (57.2 kPa −1 ), ultralow detection limit (≈1.2 Pa), fast response time (15 ms), expansive detection range (1.2 Pa –220 kPa), and exceptional stability for over 10 000 continuous compression and recovery cycles, showing great promise for capturing subtle facial expressions, large joint movements, and high‐frequency (≈25.5 Hz) pressure sensing in a high accuracy and resolution. Together with advanced machine learning algorithms, an intelligent sign language recognition glove achieves 96.8% accuracy for 24 letters within 0.1 s, ushering in a new era for ultrasensitive pressure sensors and significantly contributing to next‐generation intelligent sign language recognition systems.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-02-16
DOI
https://doi.org/10.1002/adfm.202425586
Citations
38
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
25.62

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article

Ionic Composite Nanofiber Membrane‐Based Ultra‐Sensitive and Anti‐Interference Flexible Pressure Sensors for Intelligent Sign Language Recognition

Sai Kishore Ravi, Tianshuang Wang, Geyu Lu, Swee Ching Tan et al.
38 citations
Advanced Functional Materials
Advanced Sensor and Energy Harvesting Materials
25.62
article

Ionic Composite Nanofiber Membrane‐Based Ultra‐Sensitive and Anti‐Interference Flexible Pressure Sensors for Intelligent Sign Language Recognition

Sai Kishore Ravi, Tianshuang Wang, Geyu Lu, Swee Ching Tan, Liupeng Zhao, Shuai Guo, Peng Sun, Yue Zhou, Yun Zhou, Yan Xu
article en
38 citations

Abstract

Abstract The escalating population affected by deafness and hearing loss demands solutions to revolutionize traditional sign language recognition based on interpreters. The emergence of wearable sensors could provide a promising alternative but suffer from poor mechanical stability, external signal inferences, less sensitivity, and signal hysteresis. Herein, an ultrasensitive and anti‐interference flexible ionic composite nanofiber membranes (ICNM) based pressure sensor is developed through precisely manipulating polymer‐blending interactions, where ionic liquid and silver nanowire additives are well anchored on thermoplastic polyurethane polymer scaffolds without leakage via unique hydrogen bond networks, leading to a substantial areal capacitance of 20 µF cm −2 , and effectively mitigating external noise. The ICNM‐based sensor showcases high sensitivity (57.2 kPa −1 ), ultralow detection limit (≈1.2 Pa), fast response time (15 ms), expansive detection range (1.2 Pa –220 kPa), and exceptional stability for over 10 000 continuous compression and recovery cycles, showing great promise for capturing subtle facial expressions, large joint movements, and high‐frequency (≈25.5 Hz) pressure sensing in a high accuracy and resolution. Together with advanced machine learning algorithms, an intelligent sign language recognition glove achieves 96.8% accuracy for 24 letters within 0.1 s, ushering in a new era for ultrasensitive pressure sensors and significantly contributing to next‐generation intelligent sign language recognition systems.

Advanced Functional MaterialsVol. 35(29)
National University of Singapore (SG), City University of Hong Kong (HK), Jilin University (CN), State Key Laboratory on Integrated Optoelectronics (CN), Jilin International Studies University (CN)
National University of Singapore, National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 0%
Advanced Sensor and Energy Harvesting Materials
25.62
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