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, 2017)
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.