Self-Powered Smart Insole for Monitoring Human Gait Signals

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)...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 19; no. 24; p. 5336
Main Authors Wang, Wei, Cao, Junyi, Yu, Jian, Liu, Rong, Bowen, Chris R., Liao, Wei-Hsin
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 04.12.2019
MDPI
Subjects
Aluminum
Design
Electric Power Supplies
Energy
Experiments
Fitness equipment
Gait
Humans
Nanotechnology - instrumentation
Nanotechnology - methods
Nanowires
Physiology
Polyvinyls - chemistry
Sensors
Shoes
Wearable Electronic Devices
Wireless Technology
Zinc oxides
multi-scale entropy
smart insole
gait monitoring
piezoelectric
self-powered
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s19245336