A force–voltage responsivity stabilization method for piezoelectric-based insole gait analysis for high detection accuracy in health monitoring. (March 2020)

• Record Type: Journal Article
• Authors: Chen, Junliang; Zhang, Min; Dai, Yanning; Xie, Yuedong; Tian, Wenbin; Xu, Lijun; Gao, Shuo
• Is Part Of: International journal of distributed sensor networks. Volume 16:Number 3(2020)
• Publication Date: 2020-03
• Subjects: Insole gait analysis -- piezoelectric sensing -- piezoelectric coefficient dependency on frequency and responsivity calibration; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147720905441
• Languages: English
• ISSNs: 1550-1329

Abstract

Gait analysis has become a hot spot in recent years, because it is proven that the status of a vast number of chronic diseases can be reflected by changes in gait. Furthermore, gait analysis can also help in improving the performance of athletes. Among the diverse gait analysis techniques, the piezoelectric-based insole technique has received broad attention due to its merits such as passive detection, high sensitivity, and low power consumption. However, the key coefficient of detecting plantar normal stress, the piezoelectric d 33 coefficient, relies on the force frequency, which occupies a relatively wide bandwidth (1 Hz–1 kHz) during walking events. In order to get the frequency information of the signal, in this work, empirical mode decomposition is used to separate the gait signal into several intrinsic mode functions, and then the frequency information of each function is interpreted using the normalized Hilbert transform. In this way, the piezoelectric d 33 coefficient is calibrated at every moment, obtaining higher accuracy (2.65% maximum improvement) in gait signal detection, promoting the development of gait analysis–based disease diagnosis and treatment.