Performance Analysis of Deep Learning based Human Activity Recognition Methods
Abstract
Human Activity Recognition (HAR) is one of the most important branches of human-centered research activities. Along with the development of artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In recent years, there is a growing interest in Human Activity Recognition systems applied in healthcare, security surveillance, and human motion-based activities. A HAR system is essentially made of a wearable device equipped with a set of sensors (like accelerometers, gyroscopes, magnetometers, heart-rate sensors, etc.). Different methods are being applied for improving the accuracy and performance of the HAR system. In this paper, we implement Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) in combination with Long Short-term Memory (LSTM) methods with different layers and compare their outputs towards the accuracy in the HAR system. We compare the accuracy of different HAR methods and observed that the performance of our proposed model of CNN 2 layers with LSTM 1 layer is the best.
References
Ronao C.A. & Cho SB. (2015). Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors. In: Arik S., Huang T., Lai W., Liu Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science, vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_6
Moya Reuda, F., Grzezick, R., A. Fink, G., Feldhorst, S., & Ten Hompel, M. (2018). Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors, Journal of Informatics (5020026). https://doi.org/10.3390/informatics5020026
Jobanputra, C., Bavishi, J., & Doshi, N. (2019). Human activity recognition: A survey. Procedia Computer Science, 155, 698-703. https://doi.org/10.1016/j.procs.2019.08.100
Xu, W., Pang, Y., Yang, Y., & Lui, Y. (2018). Human Activity Recognition Based on Convolutional Neural Network, (2018), 24th International Conference on Pattern Recognition (ICPR) Beijing, China, August 20-24. https://doi.org/10.1109/ICPR.2018.8545435
A. Jain and V. Kanhangad, (2018). Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors, IEEE Sensors Journal, 18(3), 1169-1177. https://doi.org/10.1109/JSEN.2017.2782492
Yang, J., Nguyen, M. N., San, P. P., Li, X. L., & Krishnaswamy, S. (2015). Deep convolutional neural networks on multichannel time series for human activity recognition. In Twenty-fourth international joint conference on artificial intelligence (IJCAI), 3995-4001.
Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T., & Li, S. (2020). Wearable sensor-based human activity recognition using hybrid deep learning techniques. Security and communication Networks, 2020. https://doi.org/10.1155/2020/2132138
Xi, R., Hou, M., Fu, M., Qu, H., & Liu, D. (2018). Deep dilated convolution on multimodality time series for human activity recognition. In 2018 international joint conference on neural networks (IJCNN), 1-8. https://doi.org/10.1109/IJCNN.2018.8489540
Ignatov Andrey (2018). Real-time human activity recognition from accelerometer data using Convolutional Neural Networks, Applied Soft Computing, Volume 62, 2018, Pages 915-922, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2017.09.027
Alake, R (2020). Batch Normalization in Neural Networks Explained (Algorithm Breakdown) https://towardsdatascience.com/batch-normalization-in-neural-networks-1ac91516821
Dua, D. & Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml
Hammerla, N. Y., Halloran, S., & Plötz, T. (2016). Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880. https://doi.org/10.48550/arXiv.1604.08880
Song-Mi Lee, Sang Min Yoon, and Heeryon Cho. (2017). Human activity recognition from accelerometer data using Convolutional Neural Network. 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), 2017, 131-134. https://doi.org/10.1109/BIGCOMP.2017.7881728
Jayabalan, A., Karunakaran, H., Murlidharan, S., & Shizume, T. (2016). Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data. https://doi.org/10.48550/arXiv.1612.06703.
Copyright (c) 2022 UNIMAS Publisher
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.
To download Copyright Transfer Statement for Journal, click here
