Wi-Fi Beyond Communications: Experimental Evaluation of Respiration Monitoring and Motion Detection Using COTS Devices (2407.05155v1)
Abstract: Wi-Fi sensing has become an attractive option for non-invasive monitoring of human activities and vital signs. This paper explores the feasibility of using state-of-the-art commercial off-the-shelf (COTS) devices for Wi-Fi sensing applications, particularly respiration monitoring and motion detection. We utilize the Intel AX210 network interface card (NIC) to transmit Wi-Fi signals in both 2.4 GHz and 6 GHz frequency bands. Our experiments rely on channel frequency response (CFR) and received signal strength indicator (RSSI) data, which are processed using a moving average algorithm to extract human behavior patterns. The experimental results demonstrate the effectiveness of our approach in capturing and representing human respiration and motion patterns. Furthermore, we compare the performance of Wi-Fi sensing across different frequency bands, highlighting the advantages of using higher frequencies for improved sensitivity and clarity. Our findings showcase the practicality of using COTS devices for Wi-Fi sensing and lay the groundwork for the development of non-invasive, contactless sensing systems. These systems have potential applications in various fields, including healthcare, smart homes, and Metaverse.
- Y. Zhou, H. Huang, S. Yuan, H. Zou, L. Xie, and J. Yang, “MetaFi++: WiFi-enabled transformer-based human pose estimation for Metaverse avatar simulation,” IEEE Internet Things J., vol. 10, no. 16, pp. 14 128–14 136, 2023.
- J. Liu, Y. Ma, A. Elzanaty, and R. Tafazolli, “Near-field fading channel modeling for ELAAs: From communication to ISAC,” arXiv:2401.17014, Jan. 2024.
- L. Qiao, A. Liao, Z. Li, H. Wang, Z. Gao, X. Gao, Y. Su, P. Xiao, L. You, and D. W. K. Ng, “Sensing user’s activity, channel, and location with near-field extra-large-scale MIMO,” IEEE Trans. Commun., vol. 72, no. 2, pp. 890–906, Feb. 2024.
- X. Ma, H. He, H. Zhang, W. Xi, Z. Chen, and J. Zhao, “Measuring and modeling multipath of Wi-Fi to locate people in indoor evironments,” in IEEE Int. Conf. Parallel Distrib Syst. (ICPADS), 2021, pp. 185–192.
- Z. Li, Z. Gao, and T. Li, “Sensing user’s channel and location with Terahertz extra-large reconfigurable intelligent surface under hybrid-field beam squint effect,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 4, pp. 893–911, Jul. 2023.
- Z. Gao, Z. Wan, D. Zheng, S. Tan, C. Masouros, D. W. K. Ng, and S. Chen, “Integrated sensing and communication with mmWave massive MIMO: A compressed sampling perspective,” IEEE Trans. Wireless Commun., vol. 22, no. 3, pp. 1745–1762, Mar. 2023.
- Z. Dai, S. Zhai, P. Qin, R. Chai, and P. Zhao, “WVGR: Gesture recognition based on WiFi-video fusion,” in IEEE/CIC Int. Conf. Commun. China (ICCC), 2023, pp. 1–6.
- Z. Jiang, T. H. Luan, X. Ren, D. Lv, H. Hao, J. Wang, K. Zhao, W. Xi, Y. Xu, and R. Li, “Eliminating the barriers: Demystifying Wi-Fi baseband design and introducing the PicoScenes Wi-Fi sensing platform,” IEEE Internet Things J., vol. 9, no. 6, pp. 4476–4496, Mar. 2022.
- X. Liu, J. Cao, S. Tang, J. Wen, and P. Guo, “Contactless respiration monitoring via off-the-shelf WiFi devices,” IEEE Trans. Mob. Comput., vol. 15, no. 10, pp. 2466–2479, Oct. 2016.
- Y. Gu, X. Zhang, C. Li, F. Ren, J. Li, and Z. Liu, “Your WiFi knows how you behave: Leveraging WiFi channel data for behavior analysis,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2018, pp. 1–6.
- P.-J. Lai, Y.-S. Zhan, W.-L. Yeh, M.-L. Ku, and C.-M. Yu, “MUSIC-based breathing rate monitoring using Wi-Fi CSI,” in Proc. Annu. Ubiquitous Comput. Electron. Mobile Commun. Conf. (UEMCON), 2022, pp. 0380–0384.
- X. Zhang, Y. Gu, H. Yan, Y. Wang, M. Dong, K. Ota, F. Ren, and Y. Ji, “Wital: A COTS WiFi devices based vital signs monitoring system using nlos sensing model,” IEEE Trans. Human-Mach. Syst., vol. 53, no. 3, pp. 629–641, Jun. 2023.
- H. Guan, A. Sharma, D. Mishra, and A. Seneviratne, “Experimental accuracy comparison for 2.4GHz and 5GHz WiFi sensing systems,” in Proc. IEEE Int. Conf. Commun. (ICC), 2023, pp. 4755–4760.
- Y. Zhang, G. Wang, H. Liu, W. Gong, and F. Gao, “WiFi-based indoor human activity sensing: A selective sensing strategy and a multi-level feature fusion approach,” IEEE Internet Things J., May 2024.
- H. Korkalainen, T. Leppänen, B. Duce, S. Kainulainen, J. Aakko, A. Leino, L. Kalevo, I. O. Afara, S. Myllymaa, and J. Töyräs, “Detailed assessment of sleep architecture with deep learning and shorter epoch-to-epoch duration reveals sleep fragmentation of patients with obstructive sleep apnea,” IEEE J. Biomed. Health Inform., vol. 25, no. 7, pp. 2567–2574, Jul. 2021.
- J. Liu, Y. Ma, J. Wang, N. Yi, R. Tafazolli, S. Xue, and F. Wang, “A non-stationary channel model with correlated NLoS/LoS states for ELAA-mMIMO,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2021, pp. 1–6.
- J. Liu, Y. Ma, and R. Tafazolli, “A spatially non-stationary fading channel model for simulation and (semi-) analytical study of ELAA-MIMO,” IEEE Trans. Wireless Commun., vol. 23, no. 5, pp. 5203–5218, May 2024.
- S. Hansun, “A new approach of moving average method in time series analysis,” in in Proc. Conf. New Media Stud. (CoNMedia), 2013, pp. 1–4.
- E. Reshef and C. Cordeiro, “Future directions for Wi-Fi 8 and beyond,” IEEE Commun. Mag., vol. 60, no. 10, pp. 50–55, Oct. 2022.
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