Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network

Article


Zhao, Y. and Zhou, S. 2017. Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network. Sensors. 17 (3), pp. 1-20. https://doi.org/10.3390/s17030478
TypeArticle
TitleWearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network
AuthorsZhao, Y. and Zhou, S.
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.

LanguageEnglish
PublisherMDPI
JournalSensors
ISSN1424-8220
Publication dates
Print28 Feb 2017
Publication process dates
Deposited23 Feb 2018
Accepted22 Feb 2017
Output statusPublished
Publisher's version
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Copyright Statement

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Digital Object Identifier (DOI)https://doi.org/10.3390/s17030478
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