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

Junliang Chen,Min Zhang,Shuo Gao,Wenbin Tian,Lijun Xu,Yanning Dai,Yuedong Xie

doi:10.1177/1550147720905441

Junliang Chen, Min Zhang + Show 5 more

Open Access

https://doi.org/10.1177/1550147720905441

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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 d33 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 d33 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.

Highlights

Gait analysis has been widely used in recent years for sports training,[1,2] chronic disease diagnosis,[3,4] and emergency situation detection,[5] attracting worldwide attention and yielding both commercial and research products.[6]
Users are constrained in a limited space due to the small area of the plate, which prohibits its use for long-term disease analysis in daily life
A key challenge for piezoelectric sensing—unstable force– voltage responsivity—has yet not been effectively addressed. This issue mainly originates from the dependency of the piezoelectric coefficient on frequency, indicating that when the same stress amplitude is applied to the piezoelectric device at different frequencies, the piezoelectric response varies due to the molecular structure.[14]

Summary

Introduction

Gait analysis has been widely used in recent years for sports training,[1,2] chronic disease diagnosis,[3,4] and emergency situation detection,[5] attracting worldwide attention and yielding both commercial and research products.[6]. A variety of piezoelectric-based research outcomes have been reported in the literature.[14,15,16] a key challenge for piezoelectric sensing—unstable force– voltage responsivity—has yet not been effectively addressed This issue mainly originates from the dependency of the piezoelectric coefficient on frequency, indicating that when the same stress amplitude is applied to the piezoelectric device at different frequencies, the piezoelectric response varies due to the molecular structure.[14] For applications that do not require high force sensitivity, for example, touch panels, the shift of piezoelectric coefficient is tolerated.

Methodology

Results and discussion

Conclusion