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Open AccessArticle

Self-Powered Smart Insole for Monitoring Human Gait Signals

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Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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China Ship Development and Design Center, Wuhan 430064, China
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Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
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Materials and Structures Centre, Department of Mechanical Engineering, University of Bath, Bath BA27AY, UK
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Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(24), 5336; https://doi.org/10.3390/s19245336
Received: 28 September 2019 / Revised: 18 November 2019 / Accepted: 26 November 2019 / Published: 4 December 2019
With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 μW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals. View Full-Text
Keywords: self-powered; piezoelectric; smart insole; gait monitoring; multi-scale entropy self-powered; piezoelectric; smart insole; gait monitoring; multi-scale entropy
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Wang, W.; Cao, J.; Yu, J.; Liu, R.; Bowen, C.R.; Liao, W.-H. Self-Powered Smart Insole for Monitoring Human Gait Signals. Sensors 2019, 19, 5336.

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