Research on Fall Detection System Based on Commercial Wi-Fi Devices

doi:10.12142/ZTECOM.202304008

ZTE Communications ›› 2023, Vol. 21 ›› Issue (4): 60-68.DOI: 10.12142/ZTECOM.202304008

收稿日期:2023-04-07 出版日期:2023-12-07 发布日期:2023-12-07

Research on Fall Detection System Based on Commercial Wi-Fi Devices

GONG Panyin¹, ZHANG Guidong¹, ZHANG Zhigang²^,³, CHEN Xiao²^,³, DING Xuan¹()

^1.School of software, Tsinghua University, Beijing 100084, China
^2.ZTE Corporation, Shenzhen 518057, China
^3.State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518055, China

Received:2023-04-07 Online:2023-12-07 Published:2023-12-07
About author:GONG Panyin received his bachelor’s degree in software engineering from Huazhong University of Science and Technology, China in 2018 and is currently studying for a master’s degree in the School of Software, Tsinghua University, China. His research interests include the Internet of Things and wireless sensing.
ZHANG Guidong received his BE degree from the Department of Electronic Engineering and Information Science, University of Science and Technology of China in 2018. He is currently working toward his PhD degree with the School of Software, Tsinghua University. His research interests include wireless sensing and mobile computing.
ZHANG Zhigang graduated from Xi’an Jiaotong University, China. Currently, he is the planning director of the cable FM product team of ZTE Corporation. With more than 10 years of experience in research and planning of telecommunication products, he has accumulated and practiced for many years in home networks, smart homes, IP protocols, etc. He has participated in translating and publishing the textbook, Principles of Compilation, applied for three patents, and made several special speeches in technical forums.
CHEN Xiao graduated from Nanjing University of Aeronautics and Astronautics, China. He is currently the director of the Wireline Architecture Department of ZTE Corporation. He has more than 20 years of experience in research and planning of telecommunication products and related technologies. He has organized many national science and technology projects, and published many papers in various publications. He is the leading inventor of many patents.
DING Xuan (dingx04@gmail.com) received his bachelor’s degree from the School of Software, Tsinghua University, China in 2008, and PhD degree from the Department of Computer Science and Technology, Tsinghua University in 2014. He is currently a research assistant professor in the School of Software, Tsinghua University. His research interests include privacy-preserving computing, blockchain, RFID and wireless sensing.

摘要/Abstract

Abstract:

Falls are a major cause of disability and even death in the elderly, and fall detection can effectively reduce the damage. Compared with cameras and wearable sensors, Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy. Wi-Fi devices sense user activity by analyzing the channel state information (CSI) of the received signal, which makes fall detection possible. We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance. In the feature extraction stage, we select the discrete wavelet transform (DWT) spectrum as the feature for activity classification, which can balance the temporal and spatial resolution. In the feature classification stage, we design a deep learning model based on convolutional neural networks, which has better performance compared with other traditional machine learning models. Experimental results show our work achieves a false alarm rate of 4.8% and a missed alarm rate of 1.9%.

Key words: fall detection, commercial Wi-Fi devices, discrete wavelet transform, deep learning model

. [J]. ZTE Communications, 2023, 21(4): 60-68.

GONG Panyin, ZHANG Guidong, ZHANG Zhigang, CHEN Xiao, DING Xuan. Research on Fall Detection System Based on Commercial Wi-Fi Devices[J]. ZTE Communications, 2023, 21(4): 60-68.

图/表 14

参考文献 32

1	EROL B, AMIN M G, BOASHASH B, et al. Wideband radar based fall motion detection for a generic elderly [C]//The 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2017: 1768–1772. DOI: 10.1109/ACSSC.2016.7869686
2	BIAN Z P, HOU J H, CHAU L P, et al. Fall detection based on body part tracking using a depth camera [J]. IEEE journal of biomedical and health informatics, 2015, 19(2): 430–439. DOI: 10.1109/JBHI.2014.2319372
3	STONE E E, SKUBIC M. Fall detection in homes of older adults using the microsoft kinect [J]. IEEE journal of biomedical and health informatics, 2015, 19(1): 290–301. DOI: 10.1109/JBHI.2014.2312180
4	KAU L J, CHEN C S. A smart phone-based pocket fall accident detection, positioning, and rescue system [J]. IEEE journal of biomedical and health informatics, 2015, 19(1): 44–56. DOI: 10.1109/JBHI.2014.2328593
5	PIERLEONI P, BELLI A, PALMA L, et al. A high reliability wearable device for elderly fall detection [J]. IEEE sensors journal, 2015, 15(8): 4544–4553. DOI: 10.1109/JSEN.2015.2423562
6	RAMEZANI R, XIAO Y B, NAEIM A. Sensing-Fi: Wi-Fi CSI and accelerometer fusion system for fall detection [C]//Proceedings of 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2018: 402–405. DOI: 10.1109/BHI.2018.8333453
7	FOROUGHI H, ASKI B S, POURREZA H. Intelligent video surveillance for monitoring fall detection of elderly in home environments [C]//The 11th International Conference on Computer and Information Technology. IEEE, 2009: 219–224. DOI: 10.1109/ICCITECHN.2008.4803020
8	JUDERAJENDRAN P, DALALI S. A smart and passive floor-vibration based-fall detector for elderly [C]//The 2nd International Conference on Information & Communication Technologies. IC-TTA, 2006. DOI: 10.1109/ICTTA.2006.1684511
9	HAN Y T, LI H, ZHU G X, et al. Indoor target detection and localization method based on WiFi [J]. ZTE technology journal. 2022, 27(5): 46–52. DOI: 10.12142/ZTETJ.202205009
10	LI F L, YANG W C, ZHANG X B. Design and application on collaborative networking scheme of 5G and WiFi6 [J]. ZTE technology journal. 2022, 27(4): 7–13. DOI: 10.12142/ZTETJ.202204003
11	ABDELNASSER H, YOUSSEF M, HARRAS K A. WiGest: A ubiquitous WiFi-based gesture recognition system [C]//IEEE Conference on Computer Communications (INFOCOM). IEEE, 2015: 1472–1480. DOI: 10.1109/INFOCOM.2015.7218525
12	JIANG W J, MIAO C L, MA F L, et al. Towards environment independent device free human activity recognition [C]//The 24th Annual International Conference on Mobile Computing and Networking. ACM, 2018. DOI: 10.1145/3241539.3241548
13	KORANY B, KARANAM C R, CAI H, et al. XModal-ID: Using WiFi for through-wall person identification from candidate video footage [C]//The 25th Annual International Conference on Mobile Computing and Networking. ACM, 2019. DOI: 10.1145/3300061.3345437
14	ZENG Y Z, PATHAK P H, MOHAPATRA P. WiWho: WiFi-based person identification in smart spaces [C]//The 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 2016: 1–12. DOI: 10.1109/IPSN.2016.7460727
15	LI X, ZHANG D Q, LV Q, et al. IndoTrack: Device-free indoor human tracking with commodity Wi-Fi [J]. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2017, 1(3): 1–22. DOI: 10.1145/3130940
16	XIE Y X, XIONG J, LI M, et al. mD-track: Leveraging multi-dimensionality for passive indoor Wi-Fi tracking [C]//The 25th Annual International Conference on Mobile Computing and Networking. ACM, 2019: 1–16. DOI: 10.1145/3300061.3300133
17	WANG Y X, WU K S, 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
18	WANG H, ZHANG D Q, WANG Y S, et al. RT-fall: A real-time and contactless fall detection system with commodity WiFi devices [J]. IEEE transactions on mobile computing, 2017, 16(2): 511–526. DOI: 10.1109/TMC.2016.2557795
19	ZHANG D Q, WANG H, WANG Y S, et al. Anti-fall: a non-intrusive and real-time fall detector leveraging CSI from commodity WiFi devices[C]//International Conference on Smart Homes and Health Telematics. Springer, 2015: 181–193.10.1007/978-3-319-19312-0_15
20	WANG Y C, YANG S, LI F, et al. FallViewer: A fine-grained indoor fall detection system with ubiquitous Wi-Fi devices [J]. IEEE Internet of Things journal, 2021, 8(15): 12455–12466. DOI: 10.1109/JIOT.2021.3063531
21	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. ACM, 2019. DOI: 10.1145/3161183
22	ZHANG L, WANG Z R, YANG L. Commercial Wi-Fi based fall detection with environment influence mitigation [C]//The 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2019: 1–9. DOI: 10.1109/SAHCN.2019.8824989
23	NAKAMURA T, BOUAZIZI M, YAMAMOTO K, et al. Wi-Fi-CSI-based fall detection by spectrogram analysis with CNN [C]//IEEE Global Communications Conference. IEEE, 2021: 1–6. DOI: 10.1109/GLOBECOM42002.2020.9322323
24	YANG Z, ZHOU Z M, LIU Y H. From RSSI to CSI: indoor localization via channel response [J]. ACM computing surveys, 46(2): 1–32. DOI: 10.1145/2543581.2543592
25	XIAO Y. IEEE 802.11n: Enhancements for higher throughput in wireless LANs [J]. IEEE wireless communications, 2005, 12(6): 82–91. DOI: 10.1109/MWC.2005.1561948
26	QIAN K, WU C S, ZHOU Z M, et al. Inferring motion direction using commodity Wi-Fi for interactive exergames [C]//The 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2017: 1961–1972. DOI: 10.1145/3025453.3025678
27	GRIFFIN D, LIM J. Signal estimation from modified short-time Fourier transform [J]. IEEE transactions on acoustics, speech, and signal processing, 1984, 32(2): 236–243. DOI: 10.1109/TASSP.1984.1164317
28	WANG W, LIU A X, SHAHZAD M, et al. Understanding and modeling of WiFi signal based human activity recognition [C]//The 21st Annual International Conference on Mobile Computing and Networking. ACM, 2015: 65–76. DOI: 10.1145/2789168.2790093
29	SHENSA M J. The discrete wavelet transform: wedding the Atrous and Mallat algorithms [J]. IEEE transactions on signal processing, 1992, 40(10): 2464–2482. DOI: 10.1109/78.157290
30	NOBRE J, NEVES R F. Combining principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets [J]. Expert systems with applications, 2019, 125: 181–194. DOI: 10.1016/j.eswa.2019.01.083
31	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84–90. DOI: 10.1145/3065386
32	SHIN H C, ROTH H R, GAO M C, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning [J]. IEEE transactions on medical imaging, 2016, 35(5): 1285–1298. DOI: 10.1109/TMI.2016.2528162

Research on Fall Detection System Based on Commercial Wi-Fi Devices

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 32

相关文章 0

编辑推荐

Metrics