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

arXiv: https://arxiv.org/abs/2311.15683 · HTML: https://mlsanity.com/paper/2311.15683/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life](https://mlsanity.com/paper/2408.00753/) (2024)
- [Wearable intelligent throat enables natural speech in stroke patients with dysarthria](https://mlsanity.com/paper/2411.18266/) (2024)
- [Soft Active Electromyography Interface for Machine Learning-Enabled Silent Speech Recognition](https://mlsanity.com/paper/2608.27048/) (2026)

## Abstract (arXiv)

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.