Fall detection based on wireless channel state information

doi:10.12267/j.issn.2096-5931.2023.09.010

Abstract

Abstract:

As China officially enters into an aging society, falls of the elderly have been concerned in recent years. To deal with this problem, wireless sensing (such as Wi-Fi sensing) is used to detect and alarm falls in time. The realization of wireless sensing technology is based on the reflection characteristics of electromagnetic waves. The signals generated by the transmitter are reflected by various objects in the indoor environment to produce different propagation paths, and finally superimpose together at the receiving end. Those signals carry information such as the orientations and activity states of the objects. By establishing a connection between the received signals and the states of the actual objects, the objects can be positioned and their actions can be identified. This paper studies the fall detection based on wireless channel state information, and verifies the feasibility of the scheme by constructing practical experimental environments.

Key words: fall detection, wireless sensing, channel state information

CLC Number:

TN929.11

CHEN Tingwei, LI Yang, HAN Kaifeng, LI Xiaoyang, LI Hang, ZHU Guangxu. Fall detection based on wireless channel state information[J]. Information and Communications Technology and Policy, 2023, 49(9): 67-73.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2023.09.010

http://ictp.caict.ac.cn/EN/Y2023/V49/I9/67

Figures/Tables 8

References 13

[1]	WANG Y, WU K, NI L M. WiFall: device-free fall detection by wireless networks[J]. IEEE Transactions on Mobile Computing, 2017, 16(2): 581-594. doi: 10.1109/TMC.2016.2557792 URL
[2]	ZHANG D, WANG H, WANG Y, et al. Anti-fall: a non-intrusive and real-time fall detector leveraging CSI from commodity WiFi devices[C]//Inclusive Smart Cities and e-Health:13th International Conference on Smart Homes and Health Telematics. Springer, 2015: 181-193.
[3]	NAKAMURA T, BOUAZIZI M, YAMAMOTO K, et al. Wi-Fi-based fall detection using spectrogram image of channel state information[J]. IEEE Internet of Things Journal, 2022, 9(18): 17220-17234. doi: 10.1109/JIOT.2022.3152315 URL
[4]	HU Y, ZHANG F, WU C, et al. DeFall: environment-independent passive fall detection using WiFi[J]. IEEE Internet of Things Journal, 2022, 9(11): 8515-8530. doi: 10.1109/JIOT.2021.3116136 URL
[5]	CHEN S, YANG W, XU Y, et al. AFall: Wi-Fi-based device-free fall detection system using spatial angle of arrival[J]. IEEE Transactions on Mobile Computing, 2023, 22(8):4471-4484. doi: 10.1109/TMC.2022.3157666 URL
[6]	PALIPANA S, ROJAS D, AGRAWAL P, et al. FallDeFi: ubiquitous fall detection using commodity Wi-Fi devices[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 1(4): 1-25.
[7]	BAHA’A A, ALMAZARI M M, ALAZRAI R, et al. A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments[J]. Data in Brief, 2020(33): 106534.
[8]	WANG Z, GU Z, YIN J, et al. Syncope detection in toilet environments using Wi-Fi channel state information[C]// Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 2018: 287-290.
[9]	LIU P, ZHU G, WANG S, et al. Toward ambient intelligence: federated edge learning with task-oriented sensing, computation, and communication integration[J]. IEEE Journal of Selected Topics in Signal Processing, 2023, 17(1): 158-172. doi: 10.1109/JSTSP.2022.3226836 URL
[10]	LIU P, ZHU G, JIANG W, et al. Vertical federated edge learning with distributed integrated sensing and communication[J]. IEEE Communications Letters, 2022, 26(9): 2091-2095. doi: 10.1109/LCOMM.2022.3181612 URL
[11]	XING H, ZHU G, LIU D, et al. Task-oriented integrated sensing, computation and communication for wireless edge AI[J]. arXiv preprint arXiv:2306.06603, 2023.
[12]	ZHU G, LYU Z, JIAO X, et al. Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G[J]. Science China Information Sciences, 2023, 66(3):130301. doi: 10.1007/s11432-022-3652-2
[13]	HALPERIN D, HU W, SHETH A, et al. Tool release: gathering 802.11n traces with channel state information[J]. ACM SIGCOMM Computer Communication Review, 2011, 41(1): 53. doi: 10.1145/1925861.1925870

衡量指标	单台接收机幅度	3台接收机幅度	单台接收机相位	3台接收机相位
准确率	0.755	0.775	0.780	0.803
精确率	0.621	0.731	0.644	0.689
召回率	0.584	0.572	0.700	0.745
F1分数	0.600	0.636	0.429	0.710

衡量指标	单台接收机幅度	3台接收机幅度	单台接收机相位	3台接收机相位
准确率	0.755	0.775	0.780	0.803
精确率	0.621	0.731	0.644	0.689
召回率	0.584	0.572	0.700	0.745
F1分数	0.600	0.636	0.429	0.710

衡量指标	单台接收机幅度	3台接收机幅度	单台接收机相位	3台接收机相位
准确率	0.808	0.733	0.775	0.775
精确率	0.743	0.722	0.633	0.644
召回率	0.65	0.325	0.775	0.725
F1分数	0.693	0.448	0.697	0.682

衡量指标	单台接收机幅度	3台接收机幅度	单台接收机相位	3台接收机相位
准确率	0.808	0.733	0.775	0.775
精确率	0.743	0.722	0.633	0.644
召回率	0.65	0.325	0.775	0.725
F1分数	0.693	0.448	0.697	0.682

衡量指标	单台接收机幅度	3台接收机幅度	单台接收机相位	3台接收机相位
准确率	0.723	0.751	0.800	0.746
精确率	0.579	0.671	0.718	0.663
召回率	0.574	0.478	0.643	0.461
F1分数	0.576	0.558	0.679	0.544