Machine learning-driven gait-assisted self-powered wearable sensing: a triboelectric nanogenerator-based advanced healthcare monitoring
Journal
Journal of Materials Chemistry A
Journal Volume
13
Journal Issue
19
Start Page
13750
End Page
13762
ISSN
20507488
Date Issued
2025-02-12
Author(s)
Parashar, Parag
Sharma, Manish Kumar
Nahak, Bishal Kumar
Khan, Arshad
Hsu, Wei-Zan
Tseng, Yao-Hsuan
Chowdhury, Jaba Roy
Huang, Yu-Hui
Liao, Jen-Chung
Kao, Fu-Cheng
Abstract
Advanced point-of-care healthcare systems are vital in contemporary healthcare, providing decentralized, real-time diagnostics and enabling continuous physiological assessment. Gait monitoring, a key application, benefits from wearable sensors integrated with flexible electronics, enabling precise, real-time tracking of biomechanical parameters. Despite the advantages, state-of-the-art systems are constrained by external power requirements, limited operational life, and low sensitivities, which impede their applicability. Triboelectric nanogenerators (TENGs), harnessing biomechanical motion to generate electrical signals, present a viable self-powered alternative, enhancing system autonomy and performance for real-time gait monitoring and rehabilitation. Herein, we propose a next-generation machine learning (ML)-driven TENG-based wearable sensing system for gait-assisted healthcare monitoring. The system features four TENG sensors with nylon 6/6 nanofibers and drop-like microstructured PTFE films as triboelectric layers. The sensors exhibit stable performance for 10 000 seconds, unaffected by environmental factors such as temperature and humidity. Integrated into shoe insoles at key anatomical points, they enable real-time monitoring of gait and plantar pressure distribution. By analyzing temporal variations in foot-ground contact during the gait cycle, the system accurately identifies biomechanical deviations associated with the pes planus condition, facilitating the development of prompt personalized orthotic prescriptions. Furthermore, highly sensitive TENG-based wearable sensors capture unique biomechanical signatures that can be leveraged to develop an automated, ML-driven advanced health monitoring system. First, the integration of ML algorithms enables high-precision user identification, achieving a remarkable accuracy of 99.6%. Subsequently, upon identification, personalized rehabilitation and athletic exercise programs are continuously monitored using highly accurate ML-driven systems, thereby laying a foundation for healthcare professionals to accurately assess individual recovery trajectories and optimize performance strategies. Therefore, this work represents a transformative approach to personalized healthcare, offering a self-powered, low-cost, scalable, and durable point-of-care solution for gait analysis and rehabilitation, with significant commercial potential due to its ease of use and accessibility.
Publisher
Royal Society of Chemistry
Type
journal article