Human Activity Recognition Using a Single Wrist IMU Sensor via Deep Learning Convolutional and Recurrent Neural Nets

E. Valarezo, P. Rivera, J. M. Park, G. Gi, T. Y. Kim, M. A. Al-Antari, M. Al-Masni and T.-S. Kim
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E. Valarezo: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
P. Rivera: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
J. M. Park: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
G. Gi: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
T. Y. Kim: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
M. A. Al-Antari: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
M. Al-Masni: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
T.-S. Kim: Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea

Journal of ICT, Design, Engineering and Technological Science, 2017, vol. 1, issue 1, 1-5

Abstract: In this paper, the authors aimed to propose novel deep learning-based HAR systems with a single wrist IMU sensor. This research used time-series activity data from only one IMU sensor at a wrist to build two deep learning algorithm-based HAR systems: one is based on Convolutional Neural Nets (CNN) and the other Recurrent Neural Nets (RNN). Our two HAR systems are evaluated by 5-fold cross-validation tests to compare the performance of both systems. Five primary daily activities, including standing, walking, running, walking downstairs, and walking upstairs, were recognized. Our results show that the CNN-based HAR system achieved an average accuracy of 95.43% and the RNN-based HAR system accuracy of 96.95%. This result presents the feasibility of HAR for some macro human activities with only a single wearable IMU device.

Keywords: Human Activities; Inertial Measurement Units (IMUs); Convolutional Neural Nets (CNN); Recurrent Neural Nets (RNN); HAR System (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:avb:jitdet:2017:p:1-5

DOI: 10.33150/JITDETS-1.1.1

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