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

Author

Listed:
  • 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)

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.

Suggested Citation

  • E. Valarezo & P. Rivera & J. M. Park & G. Gi & T. Y. Kim & M. A. Al-Antari & M. Al-Masni & T.-S. Kim, 2017. "Human Activity Recognition Using a Single Wrist IMU Sensor via Deep Learning Convolutional and Recurrent Neural Nets," Journal of ICT, Design, Engineering and Technological Science, Juhriyansyah Dalle, vol. 1(1), pages 1-5.
    • Handle: RePEc:avb:jitdet:2017:p:1-5
    DOI: 10.33150/JITDETS-1.1.1
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