Efficient Residual Neural Network for Human Activity Recognition using WiFi CSI Signals-Reference-Cited by-同舟云学术

Efficient Residual Neural Network for Human Activity Recognition using WiFi CSI Signals

Published:2024-04-12 Issue: Volume: Page:113-119
ISSN:
Container-title:Proceedings of the 2024 9th International Conference on Information and Education Innovations
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Author:

Hnoohom Narit¹^ORCID,Mekruksavanich Sakorn²^ORCID,Theeramunkong Thanaruk³^ORCID,Jitpattanakul Anuchit⁴^ORCID

Affiliation:

1. Department of Computer Engineering, Faculty of Engineering, Mahidol University, Thailand

2. Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Thailand

3. School of Information and Computer Technology, Sirindhorn International Institute of Technology, Thammasat University, Thailand

4. Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Thailand and Intelligent and Nonlinear Dynamic Innovations Research Center, Science and Technology Research Institute, King Mongkut's University of Technology North Bangkok, Thailand

Funder

National Science, Research and Innovation Fund (NSRF), King Mongkut's University of Technology North Bangkok

Thailand Research Fund under grant number RTA6080013, and the TRF Research Team Promotion Grant (RTA)

Thammasat University?s research fund, Center of Excellence in Intelligent Informatics, Speech and Language Technology and Service Innovation (CILS), and Intelligent Informatics and Service Innovation (IISI) Research Center

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664934.3664950

Reference14 articles.

1. A Review of Multiple-Person Abnormal Activity Recognition

2. Attention-Based Hybrid Deep Learning Network for Human Activity Recognition Using WiFi Channel State Information

3. Zhuravchak, A., Kapshii, O. and Pournaras, E. 2021. Human activity recognition based on Wi-Fi CSI data-a deep neural network approach. In the 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2021), (Leuven, Belgium), Elsevier B.V., 59-66.

4. Dang X. Cao Y. Hao Z. and Liu Y. 2020. WiGId: Indoor group identification with CSI-based random forest. Sensors 2020 20 (16) 4607. https://doi.org/10.3390/s20164607

5. CSI-Based Human Continuous Activity Recognition Using GMM–HMM