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Sensors 2017, 17(3), 478; doi:10.3390/s17030478

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network

1
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2
School of Science and Technology, Middlesex University, London NW4 4BT, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Received: 7 December 2016 / Revised: 17 February 2017 / Accepted: 22 February 2017 / Published: 28 February 2017
(This article belongs to the Special Issue Wearable Biomedical Sensors)
View Full-Text   |   Download PDF [10155 KB, uploaded 28 February 2017]   |  

Abstract

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns. View Full-Text
Keywords: gait recognition; gait authentication; gait labeling; angle embedded gait dynamic image; convolutional neural network; biometrics; wearable devices gait recognition; gait authentication; gait labeling; angle embedded gait dynamic image; convolutional neural network; biometrics; wearable devices
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, Y.; Zhou, S. Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network. Sensors 2017, 17, 478.

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