Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency

Chenyu Tang; Muzi Xu; Wentian Yi; Zibo Zhang; Edoardo Occhipinti; Chaoqun Dong; Dafydd Ravenscroft; Sung-Min Jung; Sanghyo Lee; Shuo Gao; Jong Min Kim; Luigi G. Occhipinti

doi:10.1038/s41528-024-00315-1

arXiv · 2311.15683

Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency

Chenyu Tang·Muzi Xu·Wentian Yi·Zibo Zhang·Edoardo Occhipinti·Chaoqun Dong·Dafydd Ravenscroft·Sung-Min Jung·Sanghyo Lee·Shuo Gao·Jong Min Kim·Luigi G. Occhipinti

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Abstract

Our research presents a wearable Silent Speech Interface (SSI) technology that excels in device comfort, time-energy efficiency, and speech decoding accuracy for real-world use. We developed a biocompatible, durable textile choker with an embedded graphene-based strain sensor, capable of accurately detecting subtle throat movements. This sensor, surpassing other strain sensors in sensitivity by 420%, simplifies signal processing compared to traditional voice recognition methods. Our system uses a computationally efficient neural network, specifically a one-dimensional convolutional neural network with residual structures, to decode speech signals. This network is energy and time-efficient, reducing computational load by 90% while achieving 95.25% accuracy for a 20-word lexicon and swiftly adapting to new users and words with minimal samples. This innovation demonstrates a practical, sensitive, and precise wearable SSI suitable for daily communication applications.

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BibTeX RIS

Chenyu Tang, Muzi Xu, Wentian Yi, Zibo Zhang, Edoardo Occhipinti, Chaoqun Dong, Dafydd Ravenscroft, Sung-Min Jung, Sanghyo Lee, Shuo Gao, Jong Min Kim, Luigi G. Occhipinti. 2023-11-27. Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency. https://doi.org/10.1038/s41528-024-00315-1

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