Vol. 174

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2022-07-05

A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition

By Federico Succetti, Antonello Rosato, Francesco Di Luzio, Andrea Ceschini, and Massimo Panella
Progress In Electromagnetics Research, Vol. 174, 127-141, 2022
doi:10.2528/PIER22042605

Abstract

Despite recent advances, fast and reliable Human Activity Recognition in confined space is still an open problem related to many real-world applications, especially in health and biomedical monitoring. With the ubiquitous presence of Wi-Fi networks, the activity recognition and classification problems can be solved by leveraging some characteristics of the Channel State Information of the 802.11 standard. Given the well-documented advantages of Deep Learning algorithms in solving complex pattern recognition problems, many solutions in the Human Activity Recognition domain are taking advantage of those models. To improve the time and precision of activity classification of time-series data stemming from Channel State Information, we propose herein a fast deep neural model encompassing concepts not only from state-of-the-art recurrent neural networks, but also using convolutional operators with added randomization. Results from real data in an experimental environment show promising results.

Citation


Federico Succetti, Antonello Rosato, Francesco Di Luzio, Andrea Ceschini, and Massimo Panella, "A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition," Progress In Electromagnetics Research, Vol. 174, 127-141, 2022.
doi:10.2528/PIER22042605
http://jpier.org/PIER/pier.php?paper=22042605

References


    1. Tian, Y., S. Li, C. Chen, Q. Zhang, C, Zhuang, and X. Ding, "Small CSI samples-based activity recognition: A deep learning approach using multidimensional features," Security and Communication Networks, Vol. 2021, 5632298, 2021.
    doi:

    504 Gateway Time-out


    2. O'Neill, J., "An overview of neural network compression,", 2020.
    doi:The server didn't respond in time.

    3. Foerster, F., M. Smeja, and J. Fahrenberg, "Detection of posture and motion by accelerometry: A validation study in ambulatory monitoring," Computers in Human Behavior, Vol. 15, No. 5, 571-583, 1999.
    doi:10.1016/S0747-5632(99)00037-0

    4. Shalaby, E., N. ElShennawy, and A. Sarhan, "Utilizing deep learning models in CSI-based human activity recognition," Neural Computing and Applications, 1-18, 2022.

    5. Golestani, N. and M. Moghaddam, "Magnetic induction-based human activity recognition (MI-HAR)," 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, 2019.

    6. Delahoz, Y. S. and M. A. Labrador, "Survey on fall detection and fall prevention using wearable and external sensors," Sensors, Vol. 14, No. 10, 19806-19842, 2014.
    doi:10.3390/s141019806

    7. Politi, O., I. Mporas, and V. Megalooikonomou, "Human motion detection in daily activity tasks using wearable sensors," 2014 22nd European Signal Processing Conference (EUSIPCO), IEEE, 2014.

    8. Jobanputra, C., J. Bavishi, and N. Doshi, "Human activity recognition: A survey," Procedia Computer Science, Vol. 155, 698-703, 2019.
    doi:10.1016/j.procs.2019.08.100

    9. Vrigkas, M., C. Nikou, and I. A. Kakadiaris, "A review of human activity recognition methods," Frontiers in Robotics and AI, Vol. 2, 2015.

    10. Wang, H., D. Zhang, Y. Wang, J. Ma, Y. Wang, and S. Li, "RT-Fall: A real-time and contactless fall detection system with commodity WiFi devices," IEEE Transactions on Mobile Computing, Vol. 16, No. 2, 511-526, 2016.
    doi:10.1109/TMC.2016.2557795

    11. Wang, Y., K. Wu, and L. M. Ni, "Wifall: Device-free fall detection by wireless networks," IEEE Transactions on Mobile Computing, Vol. 16, No. 2, 581-594, 2016.
    doi:10.1109/TMC.2016.2557792

    12. Murad, A. and J.-Y. Pyun, "Deep recurrent neural networks for human activity recognition," Sensors, Vol. 17, No. 11, 2017.
    doi:10.3390/s17112556

    13. Moshiri, F., R. Shahbazian, M. Nabati, and S. A. Ghorashi, "A CSI-based human activity recognition using deep learning," Sensors, Vol. 21, No. 21, 2021.

    14. Ibrahim, M., M. Torki, and M. ElNainay, "CNN based indoor localization using RSS time-series," 2018 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2018.

    15. Halperin, D., W. Hu, A. Sheth, and D. Wetherall, "Tool release: Gathering 802.11 n traces with channel state information," ACM SIGCOMM Computer Communication Review, Vol. 41, No. 1, 53-53, 2011.
    doi:10.1145/1925861.1925870

    16. Lv, J., W. Yang, X. Du, and M. Yu, "Robust WLAN-based indoor intrusion detection using PHY layer information," IEEE Access, Vol. 6, 30117-30127, 2017.

    17. Cortes, C. and V. Vapnik, "Support-vector networks," Machine Learning, Vol. 20, 273-297, 1995.

    18. Breiman, L., "Random forests," Machine Learning, Vol. 45, 5-32, 2001.
    doi:10.1023/A:1010933404324

    19. Xi, W., J. Zhao, X.-Y. Li, K. Zhao, S. Tang, X. Liu, and Z. Jiang, "Electronic frog eye: Counting crowd using WiFi," IEEE INFOCOM 2014 | IEEE Conference on Computer Communications, IEEE, 2014.

    20. Liu, X., J. Cao, S. Tang, and J. Wen, "Wi-Sleep: Contactless sleep monitoring via WiFi signals," 2014 IEEE Real-Time Systems Symposium, IEEE, 2014.

    21. Liu, J., Y. Wang, Y. Chen, J. Yang, X. Chen, and J. Cheng, "Tracking vital signs during sleep leveraging off-the-shelf WiFi," Proceedings of the 16th ACM International Symposium on Mobile ad hoc Networking and Computing, 2015.

    22. Wang, Y., J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. Liu, "E-eyes: Device-free location- oriented activity identification using fine-grained WiFi signatures," Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, 2014.

    23. He, Z., "Deep learning in image classification: A survey report," 2020 2nd International Conference on Information Technology and Computer Application (ITCA), IEEE, 2020.
    doi:10.1109/TNNLS.2020.2979670

    24. Otter, D. W., J. R. Medina, and J. K. Kalita, "A survey of the usages of deep learning for natural language processing," IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 2, 604-624, 2020.

    25. Kumar, A., S. Verma, and H. Mangla, "A survey of deep learning techniques in speech recognition," 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), IEEE, 2018.
    doi:10.1038/s41591-018-0316-z

    26. Esteva, A., A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, "A guide to deep learning in healthcare," Nature Medicine, Vol. 25, No. 1, 24-29, 2019.

    27. Rosato, A., M. Panella, A. Andreotti, A. M. Osama, and R. Araneo, "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, 291, 2021.

    28. Rizzi, A., N. M. Buccino, M. Panella, and A. Uncini, "Genre classification of compressed audio data," 2008 IEEE 10th Workshop on Multimedia Signal Processing, 654-659, IEEE, 2008.

    29. Erhan, D., P.-A. Manzagol, Y. Bengio, S. Bengio, and P. Vincent, "The difficulty of training deep architectures and the effect of unsupervised pre-training," Artificial Intelligence and Statistics, PMLR, 2009.

    30. Vincent, P., H. Larochelle, Y. Bengio, and P.-A. Manzagol, "Extracting and composing robust features with denoising autoencoders," Proceedings of the 25th International Conference on Machine Learning, 2008.

    31. Fukushima, K., "Neocognitron: A hierarchical neural network capable of visual pattern recognition," Neural networks, Vol. 1, No. 2, 119-130, 1988.

    32. Hochreiter, S. and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol. 9, No. 8, 1735-1780, 1997.

    33. Grushin, A., D. D. Monner, J. A. Reggia, and A. Mishra, "Robust human action recognition via long short-term memory," The 2013 International Joint Conference on Neural Networks (IJCNN), IEEE, 2013.

    34. Schafer, B., B. R. Barrsiwal, M. Kokhkharova, H. Adil, and J. Liebehenschel, "Human activity recognition using CSI information with nexmon," Appl. Sci., Vol. 11, 8860, 2021.

    35. Chen, Z., L. Zhang, C. Jiang, Z. Cao, and W. Cui, "WiFi CSI based passive human activity recognition using attention based BiLSTM," IEEE Transactions on Mobile Computing, Vol. 18, No. 11, 2714-2724, 2018.

    36. Khan, D. A., S. Razak, B. Raj, and R. Singh, "Human behaviour recognition using WiFi channel state information," ICASSP 2019 --- 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019.

    37. Sheng, B., F. Xiao, L. Sha, and L. Sun, "Deep spatial-temporal model based cross-scene action recognition using commodity WiFi," IEEE Internet of Things Journal, Vol. 7, No. 4, 3592-3601, 2020.

    38. Damodaran, N., E. Haruni, M. Kokhkharova, and J. Schafer, "Device free human activity and fall recognition using WiFi Channel State Information (CSI)," CCF Transactions on Pervasive Computing and Interaction, Vol. 2, No. 1, 1-17, 2020.

    39. Alsaify, B. A., M. M. Almazari, R. Alazrai, S. Alouneh, and M. I. Daoud, "A CSI-based multi-environment human activity recognition framework," Appl. Sci., Vol. 12, 930, 2022.

    40. Schafer, J., "CSI human activity," IEEE Dataport, August 2, 2021.

    41. Kingma, D. and J. Ba, "Adam: A method for stochastic optimization," International Conference on Learning Representations, 2014.