Human activity recognition (HAR) based on smartphone sensors provides an efficient way for studying the connection between human physical activities and health issues. In this paper, three feature sets are involved, including tri-axial angular velocity data collected from gyroscope sensor, tri-axial total acceleration data collected from accelerometer sensor, and the estimated tri-axial body acceleration data. The FFT components of the three feature sets are used to divide activities into six types like walking, walking upstairs, walking downstairs, sitting, standing and lying. Two kinds of CNN architectures are designed for HAR. The one is Architecture A in which only one set of features is combined at the first convolution layer; and the other one is Architecture B in which two sets of the features are combined at the first convolution layer. The validation data set is used to automatically determine the iteration number during the training process. It is shown that the performance of Architecture B is better compared to Architecture A. And the Architecture B is further improved by varying the number of the features maps at each convolution layer and the one producing the best result is selected. Compared with five other HAR methods using CNN, the proposed method could achieve a better recognition accuracy of 97.5% for a UCI HAR dataset.
|Title of host publication||Web and Big Data - APWeb-WAIM 2018 International Workshops|
|Subtitle of host publication||MWDA, BAH, KGMA, DMMOOC, 2018, Revised Selected Papers|
|Editors||Leong Hou U, Haoran Xie|
|Number of pages||10|
|Publication status||Published - 2018|
|Event||Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018 - Macau, China|
Duration: 23 Jul 2018 → 25 Jul 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018|
|Period||23/07/2018 → 25/07/2018|
Bibliographical noteFunding Information:
Acknowledgements. This work is supported by the National Key R&D Program of China (Grants No. 2017YFE0111900).
© 2018, Springer Nature Switzerland AG.
Copyright 2018 Elsevier B.V., All rights reserved.
- Convolutional neural network
- Human activity recognition
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)