Optimization of Knitted Strain Sensor Structures for a Real-Time Korean Sign Language Translation Glove System

TL;DR

This paper presents a knitted glove system that translates sign language into text and audio in real time using optimized strain sensors.

Contribution

The study introduces a novel knitted strain sensor design optimized for real-time Korean Sign Language recognition with high accuracy.

Findings

A plain-plated-knit sensor with no elastic yarn and uniform conductive yarn placement achieved a gauge factor of 88.

The sensor responded to finger movements in under 0.1 seconds and showed stable performance across different conditions.

The system recognized 12 Korean Sign Language gestures with 98.67% accuracy in real time.

Abstract

Herein, an integrated system is developed based on knitted strain sensors for real-time translation of sign language into text and audio voices. To investigate how the structural characteristics of the knit affect the electrical performance, the position of the conductive yarn and the presence or absence of elastic yarn are set as experimental variables, and five distinct sensors are manufactured. A comprehensive analysis of the electrical and mechanical performance, including sensitivity, responsiveness, reliability, and repeatability, reveals that the sensor with a plain-plated-knit structure, no elastic yarn included, and the conductive yarn positioned uniformly on the back exhibits the best performance, with a gauge factor (GF) of 88. The sensor exhibited a response time of less than 0.1 s at 50 cycles per minute (cpm), demonstrating that it detects and responds promptly to finger joint bending movements. Moreover, it exhibits stable repeatability and reliability across various angles and speeds, confirming its optimization for sign language recognition applications. Based on this design, an integrated textile-based system is developed by incorporating the sensor, interconnections, snap connectors, and a microcontroller unit (MCU) with built-in Bluetooth Low Energy (BLE) technology into the knitted glove. The complete system successfully recognized 12 Korean Sign Language (KSL) gestures in real time and output them as both text and audio through a dedicated application, achieving a high recognition accuracy of 98.67%. Thus, the present study quantitatively elucidates the structure–performance relationship of a knitted sensor and proposes a wearable system that accounts for real-world usage environments, thereby demonstrating the commercialization potential of the technology.

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