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A Highly Flexible Self-Powered Triboelectric Sensor Array for Silent Speech Recognition and Swallowing Motion
Parag Parashar1, Li-Chien Shen2, Yu-Hao Lee2
1Department of Biomedical Engineering, National Taiwan University, Taipei, 10617, Taiwan.
Small (Weinheim an Der Bergstrasse, Germany)
|June 12, 2025
Summary
A novel self-powered tactile sensor array using triboelectric nanogenerators (TENGs) offers a non-invasive solution for silent speech recognition and swallowing analysis. This technology advances wearable assistive devices for communication and rehabilitation.
Area of Science:
- Biomedical Engineering
- Materials Science
- Wearable Technology
Background:
- Speech and swallowing disorders are increasing, requiring advanced non-invasive diagnostic and rehabilitation tools.
- Current silent speech recognition (SSR) and swallowing assessment methods have limitations like invasiveness, environmental sensitivity, and lack of continuous monitoring.
- Existing technologies such as vision-based SSR, ultrasound, sEMG, VFSS, and FEES have significant drawbacks hindering widespread clinical adoption.
Purpose of the Study:
- To develop a flexible, self-powered tactile sensor array for non-invasive silent speech recognition (SSR) and swallowing motion analysis.
- To overcome the limitations of conventional SSR and swallowing assessment techniques.
- To create a foundation for next-generation wearable assistive technologies.
Main Methods:
- Fabrication of a flexible, self-powered tactile sensor array using a triboelectric nanogenerator (TENG).
- The sensor utilizes a microstructured polydimethylsiloxane (PDMS) layer and an electrospun Nylon 6/6 nanofiber film for efficient triboelectric charge generation.
- Integration of the TENG sensor into a 2x2 matrix to capture lip and laryngeal movements, analyzed using machine learning algorithms.
Main Results:
- The TENG sensor array accurately captured lip and laryngeal movements.
- Achieved 97.06% accuracy for silent speech-based user authentication.
- Demonstrated high-precision classification (98.04%) of critical swallowing rehabilitation maneuvers.
Conclusions:
- The developed TENG-based sensor array provides a robust, non-invasive, and self-sustaining solution for real-time speech and swallowing analysis.
- This technology represents a significant advancement for wearable assistive devices in communication and rehabilitation.
- The findings pave the way for improved clinical diagnostics and rehabilitation strategies for individuals with speech and swallowing disorders.
