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Sensors 2017, 17(5), 1172; doi:10.3390/s17051172

Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods

1
Department of Control and Instrumentation Engineering, Korea University, Sejong-Ro 2511, Sejong-City 30016, Korea
2
Research Institute of Korean Air Lines Co., Ltd., Daejeon-City 34054, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Edward Sazonov and Subhas Chandra Mukhopadhyay
Received: 26 March 2017 / Revised: 12 May 2017 / Accepted: 17 May 2017 / Published: 20 May 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Abstract

By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP) methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors. View Full-Text
Keywords: walking; fall detection; wearable sensors; Gaussian process; dynamic model; dimensionality reduction; novelty detection; latent feature space walking; fall detection; wearable sensors; Gaussian process; dynamic model; dimensionality reduction; novelty detection; latent feature space
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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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Kim, T.; Park, J.; Heo, S.; Sung, K.; Park, J. Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods. Sensors 2017, 17, 1172.

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