Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices (2402.17277v3)

Published 27 Feb 2024 in eess.SY and cs.SY

Abstract: Human activity recognition (HAR) holds significant importance in smart homes, security, and healthcare. Existing systems face limitations because of the insufficient spatial diversity provided by a limited number of antennas. Furthermore, inefficiencies in noise reduction and feature extraction from sensing data pose challenges to recognition performance. This study presents a reconfigurable intelligent surface (RIS)-assisted passive human activity recognition (RISAR) method, compatible with commercial Wi-Fi devices. RISAR leverages a RIS to enhance the spatial diversity of Wi-Fi signals, effectively capturing a wider range of information distributed across the spatial domain. A novel high-dimensional factor model based on random matrix theory is proposed to address noise reduction and feature extraction in the temporal domain. A dual-stream spatial-temporal attention network model is developed to assign variable weights to different characteristics and sequences, mimicking human cognitive processes in prioritizing essential information. Experimental analysis shows that RISAR significantly outperforms existing HAR methods in accuracy and efficiency, achieving an average accuracy of 97.26%. These findings underscore RISAR's adaptability and potential as a robust activity recognition solution in real environments.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. M. M. Hassan, M. Z. Uddin, A. Mohamed, and A. Almogren, “A robust human activity recognition system using smartphone sensors and deep learning,” Future Generation Computer Systems, vol. 81, pp. 307–313, 2018.
  2. X. Zhou, W. Liang, I. Kevin, K. Wang, H. Wang, L. T. Yang, and Q. Jin, “Deep-learning-enhanced human activity recognition for Internet of healthcare things,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6429–6438, 2020.
  3. Y. Zhao, H. Zhou, S. Lu, Y. Liu, X. An, and Q. Liu, “Human activity recognition based on non-contact radar data and improved PCA method,” Applied Sciences, vol. 12, no. 14, p. 7124, 2022.
  4. O. D. Lara and M. A. Labrador, “A survey on human activity recognition using wearable sensors,” IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1192–1209, 2012.
  5. Q. Pu, S. Gupta, S. Gollakota, and S. Patel, “Whole-home gesture recognition using wireless signals,” in Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, 2013, pp. 27–38.
  6. H. Wang, 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, pp. 511–526, 2016.
  7. W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Device-free human activity recognition using commercial WiFi devices,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 5, pp. 1118–1131, 2017.
  8. H. Yan, Y. Zhang, Y. Wang, and K. Xu, “WiAct: A passive WiFi-based human activity recognition system,” IEEE Sensors Journal, vol. 20, no. 1, pp. 296–305, 2019.
  9. X. Lu, Y. Li, W. Cui, and H. Wang, “CeHAR: CSI-based channel-exchanging human activity recognition,” IEEE Internet of Things Journal, vol. 10, no. 7, pp. 5953–5961, 2022.
  10. N. Honma, D. Sasakawa, N. Shiraki, T. Nakayama, and S. Iizuka, “Human monitoring using MIMO radar,” in 2018 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM).   IEEE, 2018, pp. 1–2.
  11. L. Van Der Maaten, E. O. Postma, H. J. van den Herik et al., “Dimensionality reduction: A comparative review,” Journal of Machine Learning Research, vol. 10, no. 66-71, p. 13, 2009.
  12. G. Lan, M. F. Imani, P. Del Hougne, W. Hu, D. R. Smith, and M. Gorlatova, “Wireless sensing using dynamic metasurface antennas: Challenges and opportunities,” IEEE Communications Magazine, vol. 58, no. 6, pp. 66–71, 2020.
  13. R. Liu, M. Li, H. Luo, Q. Liu, and A. L. Swindlehurst, “Integrated sensing and communication with reconfigurable intelligent surfaces: Opportunities, applications, and future directions,” IEEE Wireless Communications, vol. 30, no. 1, pp. 50–57, 2023.
  14. M. Rihan, A. Zappone, S. Buzzi, G. Fodor, and M. Debbah, “Passive vs. active reconfigurable intelligent surfaces for integrated sensing and communication: Challenges and opportunities,” IEEE Network, 2023.
  15. X. Xu, J. Tang, X. Zhang, X. Liu, H. Zhang, and Y. Qiu, “Exploring techniques for vision based human activity recognition: Methods, systems, and evaluation,” Sensors, vol. 13, no. 2, pp. 1635–1650, 2013.
  16. K. Kim, A. Jalal, and M. Mahmood, “Vision-based human activity recognition system using depth silhouettes: A smart home system for monitoring the residents,” Journal of Electrical Engineering & Technology, vol. 14, pp. 2567–2573, 2019.
  17. A. Franco, A. Magnani, and D. Maio, “A multimodal approach for human activity recognition based on skeleton and RGB data,” Pattern Recognition Letters, vol. 131, pp. 293–299, 2020.
  18. V. Sharma, M. Gupta, A. K. Pandey, D. Mishra, and A. Kumar, “A review of deep learning-based human activity recognition on benchmark video datasets,” Applied Artificial Intelligence, vol. 36, no. 1, p. 2093705, 2022.
  19. G. Bhat, R. Deb, V. V. Chaurasia, H. Shill, and U. Y. Ogras, “Online human activity recognition using low-power wearable devices,” in 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).   IEEE, 2018, pp. 1–8.
  20. V. Bianchi, M. Bassoli, G. Lombardo, P. Fornacciari, M. Mordonini, and I. De Munari, “IoT wearable sensor and deep learning: An integrated approach for personalized human activity recognition in a smart home environment,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8553–8562, 2019.
  21. S. Zhang, Y. Li, S. Zhang, F. Shahabi, S. Xia, Y. Deng, and N. Alshurafa, “Deep learning in human activity recognition with wearable sensors: A review on advances,” Sensors, vol. 22, no. 4, p. 1476, 2022.
  22. S. Zhu, R. G. Guendel, A. Yarovoy, and F. Fioranelli, “Continuous human activity recognition with distributed radar sensor networks and CNN–RNN architectures,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–15, 2022.
  23. G. A. Oguntala, R. A. Abd-Alhameed, N. T. Ali, Y.-F. Hu, J. M. Noras, N. N. Eya, I. Elfergani, and J. Rodriguez, “SmartWall: Novel RFID-enabled ambient human activity recognition using machine learning for unobtrusive health monitoring,” IEEE Access, vol. 7, pp. 68 022–68 033, 2019.
  24. S. Yousefi, H. Narui, S. Dayal, S. Ermon, and S. Valaee, “A survey on behavior recognition using WiFi channel state information,” IEEE Communications Magazine, vol. 55, no. 10, pp. 98–104, 2017.
  25. Z. Chen, L. Zhang, C. Jiang, Z. Cao, and W. Cui, “WiFi CSI based passive human activity recognition using attention based BLSTM,” IEEE Transactions on Mobile Computing, vol. 18, no. 11, pp. 2714–2724, 2018.
  26. C. Feng, S. Arshad, S. Zhou, D. Cao, and Y. Liu, “Wi-multi: A three-phase system for multiple human activity recognition with commercial wifi devices,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 7293–7304, 2019.
  27. C. Xiao, Y. Lei, Y. Ma, F. Zhou, and Z. Qin, “DeepSeg: Deep-learning-based activity segmentation framework for activity recognition using WiFi,” IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5669–5681, 2020.
  28. L. Guo, H. Zhang, C. Wang, W. Guo, G. Diao, B. Lu, C. Lin, and L. Wang, “Towards CSI-based diversity activity recognition via LSTM-CNN encoder-decoder neural network,” Neurocomputing, vol. 444, pp. 260–273, 2021.
  29. E. Shalaby, N. ElShennawy, and A. Sarhan, “Utilizing deep learning models in CSI-based human activity recognition,” Neural Computing and Applications, pp. 1–18, 2022.
  30. Z. Sun, Q. Ke, H. Rahmani, M. Bennamoun, G. Wang, and J. Liu, “Human action recognition from various data modalities: A review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  31. M. M. Islam, S. Nooruddin, F. Karray, and G. Muhammad, “Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects,” Computers in Biology and Medicine, p. 106060, 2022.
  32. Z. Yang, Z. Zhou, and Y. Liu, “From RSSI to CSI: Indoor localization via channel response,” ACM Computing Surveys (CSUR), vol. 46, no. 2, pp. 1–32, 2013.
  33. R. Xiong, X. Dong, T. Mi, and R. C. Qiu, “Optimal discrete beamforming of reconfigurable intelligent surface,” arXiv preprint arXiv:2211.04167, 2022.
  34. R. Xiong, J. Zhang, X. Dong, Z. Wang, J. Liu, T. Mi, and R. C. Qiu, “RIS-aided wireless communication in real-world: Antennas design, prototyping, beam reshape and field trials,” arXiv preprint arXiv:2303.03287, 2023.
  35. D. Halperin, 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, pp. 53–53, 2011.
  36. Y. Ma, G. Zhou, and S. Wang, “Wifi sensing with channel state information: A survey,” ACM Computing Surveys (CSUR), vol. 52, no. 3, pp. 1–36, 2019.
  37. K. Qian, C. Wu, Z. Yang, Y. Liu, and Z. Zhou, “PADS: Passive detection of moving targets with dynamic speed using PHY layer information,” in 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS).   IEEE, 2014, pp. 1–8.
  38. W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Understanding and modeling of wifi signal based human activity recognition,” in Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, 2015, pp. 65–76.
  39. V. A. Marchenko and L. A. Pastur, “Distribution of eigenvalues for some sets of random matrices,” Matematicheskii Sbornik, vol. 114, no. 4, pp. 507–536, 1967.
  40. L. Xu, W. Yang, Y. Cao, and Q. Li, “Human activity recognition based on random forests,” in 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).   IEEE, 2017, pp. 548–553.
  41. W. Zhang, X. Zhao, and Z. Li, “A comprehensive study of smartphone-based indoor activity recognition via xgboost,” IEEE Access, vol. 7, pp. 80 027–80 042, 2019.
  42. M. M. H. Shuvo, N. Ahmed, K. Nouduri, and K. Palaniappan, “A hybrid approach for human activity recognition with support vector machine and 1D convolutional neural network,” in 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).   IEEE, 2020, pp. 1–5.
  43. Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, pp. 48–62, 2021.
  44. J. Baik and J. W. Silverstein, “Eigenvalues of large sample covariance matrices of spiked population models,” Journal of Multivariate Analysis, vol. 97, no. 6, pp. 1382–1408, 2006.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now