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Sensors 2014, 14(9), 17037-17054; doi:10.3390/s140917037

Gait Characteristic Analysis and Identification Based on the iPhone’s Accelerometer and Gyrometer

1
School of Electronics and Information Engineering, Beihang University, 37 Xueyuan Road Haidian District, Beijing 100191, China
2
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
3
Department of Mathematics, the University of Texas-Pan American, 1201 West University Drive, Edinburg, TX 78539, USA
*
Author to whom correspondence should be addressed.
Received: 7 August 2014 / Revised: 29 August 2014 / Accepted: 9 September 2014 / Published: 12 September 2014
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [6972 KB, uploaded 12 September 2014]   |  

Abstract

Gait identification is a valuable approach to identify humans at a distance. In thispaper, gait characteristics are analyzed based on an iPhone’s accelerometer and gyrometer,and a new approach is proposed for gait identification. Specifically, gait datasets are collectedby the triaxial accelerometer and gyrometer embedded in an iPhone. Then, the datasets areprocessed to extract gait characteristic parameters which include gait frequency, symmetrycoefficient, dynamic range and similarity coefficient of characteristic curves. Finally, aweighted voting scheme dependent upon the gait characteristic parameters is proposed forgait identification. Four experiments are implemented to validate the proposed scheme. Theattitude and acceleration solutions are verified by simulation. Then the gait characteristicsare analyzed by comparing two sets of actual data, and the performance of the weightedvoting identification scheme is verified by 40 datasets of 10 subjects. View Full-Text
Keywords: gait; iPhone; accelerometer; identification; weighted voting gait; iPhone; accelerometer; identification; weighted voting
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Sun, B.; Wang, Y.; Banda, J. Gait Characteristic Analysis and Identification Based on the iPhone’s Accelerometer and Gyrometer. Sensors 2014, 14, 17037-17054.

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