Functional Sock With Laser-Induced Graphene-Based Triboelectric Sensor for Gait Monitoring Applications

This study reported a sock-based triboelectric gait sensor designed for gait monitoring. The sensor integrates laser-induced graphene (LIG) on Kevlar textile, encapsulated in Ecoflex, which functions as the negative triboelectric layer during interaction with floor surfaces. The LIG-based triboelectric sensor achieves a stable voltage output of ±12 V and a short-circuit current of ±1.5 μA at a contact frequency of 1.0 Hz and a separation distance of 25 mm. A Random Forest classifier was employed to distinguish between walking, running, and jumping based on voltage amplitude and frequency features, achieving an accuracy of 95.39%. To enhance user accessibility, a graphical user interface (GUI) was developed for intuitive visualization of classification results. This flexible, self-powered sock-based sensor offers a comfortable and efficient alternative to traditional insole-based systems, advancing wearable technology for precise gait analysis.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-05-14
DOI
https://doi.org/10.1109/jsen.2025.3568296
Citations
1
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.67

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article

Functional Sock With Laser-Induced Graphene-Based Triboelectric Sensor for Gait Monitoring Applications

Pei Song Chee, Eng-Hock Lim, Chun Hui Tan, Chong Liu et al.
1 citations
IEEE Sensors Journal
Advanced Sensor and Energy Harvesting Materials
0.67
article

Functional Sock With Laser-Induced Graphene-Based Triboelectric Sensor for Gait Monitoring Applications

Pei Song Chee, Eng-Hock Lim, Chun Hui Tan, Chong Liu, Kwong-Long Wong
article en
1 citations

Abstract

This study reported a sock-based triboelectric gait sensor designed for gait monitoring. The sensor integrates laser-induced graphene (LIG) on Kevlar textile, encapsulated in Ecoflex, which functions as the negative triboelectric layer during interaction with floor surfaces. The LIG-based triboelectric sensor achieves a stable voltage output of ±12 V and a short-circuit current of ±1.5 μA at a contact frequency of 1.0 Hz and a separation distance of 25 mm. A Random Forest classifier was employed to distinguish between walking, running, and jumping based on voltage amplitude and frequency features, achieving an accuracy of 95.39%. To enhance user accessibility, a graphical user interface (GUI) was developed for intuitive visualization of classification results. This flexible, self-powered sock-based sensor offers a comfortable and efficient alternative to traditional insole-based systems, advancing wearable technology for precise gait analysis.

IEEE Sensors JournalVol. 25(13)
Monash University Malaysia (MY), Universiti Tunku Abdul Rahman (MY)
Universiti Tunku Abdul Rahman
Affordable and clean energy
Openalex Percentile: Top 32%
Advanced Sensor and Energy Harvesting Materials
0.67
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