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G. Gi
Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin,South Korea
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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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; T.-S. Kim
Journal of ICT, Design, Engineering and Technological Science Volume 1, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-1.1.1

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.
Co-Authors E. Valarezo J. M. Park M. A. Al-Antari M. Al-Masni P. Rivera T. Y. Kim T.-S. Kim