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Sensors 2019, 19(8), 1757; https://doi.org/10.3390/s19081757

Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole

1
Department of Data Science, Dankook University, Yongin 16890, Korea
2
Department of Internal Medicine, Chung-Ang University, Seoul 06984, Korea
3
Department of Computer Science and Engineering, Dankook University, Yongin 16890, Korea
*
Authors to whom correspondence should be addressed.
Received: 26 February 2019 / Revised: 6 April 2019 / Accepted: 9 April 2019 / Published: 12 April 2019
(This article belongs to the Special Issue Ambient Intelligent Systems using Wearable Sensors)
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Abstract

In this paper, we proposed a gait type classification method based on deep learning using a smart insole with various sensor arrays. We measured gait data using a pressure sensor array, an acceleration sensor array, and a gyro sensor array built into a smart insole. Features of gait pattern were then extracted using a deep convolution neural network (DCNN). In order to accomplish this, measurement data of continuous gait cycle were divided into unit steps. Pre-processing of data were then performed to remove noise followed by data normalization. A feature map was then extracted by constructing an independent DCNN for data obtained from each sensor array. Each of the feature maps was then combined to form a fully connected network for gait type classification. Experimental results for seven types of gait (walking, fast walking, running, stair climbing, stair descending, hill climbing, and hill descending) showed that the proposed method provided a high classification rate of more than 90%. View Full-Text
Keywords: gait type classification; deep learning; feature extraction; sensor array; smart insole gait type classification; deep learning; feature extraction; sensor array; smart insole
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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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Lee, S.-S.; Choi, S.T.; Choi, S.-I. Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole. Sensors 2019, 19, 1757.

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