research-article

FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices

Authors:

Sameera Palipana,

Piyush Agrawal,

Dirk PeschAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 4

Article No.: 155, Pages 1 - 25

https://doi.org/10.1145/3161183

Published: 08 January 2018 Publication History

Abstract

Falling or tripping among elderly people living on their own is recognized as a major public health worry that can even lead to death. Fall detection systems that alert caregivers, family members or neighbours can potentially save lives. In the past decade, an extensive amount of research has been carried out to develop fall detection systems based on a range of different detection approaches, i.e, wearable and non-wearable sensing and detection technologies. In this paper, we consider an emerging non-wearable fall detection approach based on WiFi Channel State Information (CSI). Previous CSI based fall detection solutions have considered only time domain approaches. Here, we take an altogether different direction, time-frequency analysis as used in radar fall detection. We use the conventional Short-Time Fourier Transform (STFT) to extract time-frequency features and a sequential forward selection algorithm to single out features that are resilient to environment changes while maintaining a higher fall detection rate. When our system is pre-trained, it has a 93% accuracy and compared to RTFall and CARM, this is a 12% and 15% improvement respectively. When the environment changes, our system still has an average accuracy close to 80% which is more than a 20% to 30% and 5% to 15% improvement respectively.

References

[1]

United Nations, Department of Economic and Social Affairs, Population Division (2015). World Population Prospects: The 2015 Revision, custom data acquired via website. https://esa.un.org/unpd/wpp/. Accessed: 2017-04-24.

[2]

B. Erol, M. G. Amin, B. Boashash, F. Ahmad, and Y. D. Zhang. Wideband radar based fall motion detection for a generic elderly. In 2016 50th Asilomar Conference on Signals, Systems and Computers, pages 1768--1772, 2016.

[3]

Moeness G Amin, Yimin D Zhang, Fauzia Ahmad, and KC Dominic Ho. Radar signal processing for elderly fall detection: The future for in-home monitoring. IEEE Signal Processing Magazine, 33(2):71--80, 2016.

[4]

Bo Yu Su, KC Ho, Marilyn J Rantz, and Marjorie Skubic. Doppler radar fall activity detection using the wavelet transform. IEEE Transactions on Biomedical Engineering, 62(3):865--875, 2015.

[5]

E. Cippitelli et al. Radar and RGB-Depth Sensors for Fall Detection: A Review. IEEE Sensors Journal, 17(12):3585--3604, 2017.

[6]

L Day. Falls in Older People: Risk Factors and Strategies for Prevention. Injury Prevention, 9(1):93--94, 2003.

[7]

Leila Takayama, Caroline Pantofaru, David Robson, Bianca Soto, and Michael Barry. Making technology homey: finding sources of satisfaction and meaning in home automation. In Proc. of the 2012 ACM Conf. on Ubiquitous Computing, pages 511--520. ACM, 2012.

Digital Library

[8]

Ossi Kaltiokallio, Maurizio Bocca, and Neal Patwari. Enhancing the accuracy of radio tomographic imaging using channel diversity. In Mobile Adhoc and Sensor Systems (MASS), 2012 IEEE 9th International Conference on, pages 254--262. IEEE, 2012.

Digital Library

[9]

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, 16(2):511--526, 2017.

Digital Library

[10]

Muhammad Mubashir et al. A survey on fall detection: Principles and approaches. Neurocomputing, 100:144--152, 2013.

Digital Library

[11]

Raul Igual et al. Challenges, issues and trends in fall detection systems. Biomedical engineering online, 12(1):66, 2013.

[12]

Natthapon Pannurat et al. Automatic fall monitoring: a review. Sensors, 14(7):12900--12936, 2014.

[13]

Xuefeng Liu, Jiannong Cao, Shaojie Tang, Jiaqi Wen, and Peng Guo. Contactless Respiration Monitoring Via Off-the-Shelf WiFi Devices. IEEE Transactions on Mobile Computing, 15(10):2466--2479, 2016.

[14]

Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu. Understanding and Modeling of WiFi Signal Based Human Activity Recognition. In Proc. of the 21st Annual Int. Conf. on Mobile Computing and Networking, MobiCom‘15.

Digital Library

[15]

Brad Mager, Neal Patwari, and Maurizio Bocca. Fall detection using RF sensor networks. In 24th Int. Symp. on Personal Indoor and Mobile Radio Communications (PIMRC), pages 3472--3476. IEEE, 2013.

[16]

Heba Abdelnasser, Moustafa Youssef, and Khaled A Harras. Wigest: A ubiquitous wifi-based gesture recognition system. In IEEE Conference on Computer Communications (INFOCOM), pages 1472--1480. IEEE, 2015.

[17]

Zicheng Chi, Y. Yao, Tiantian Xie, Zhichuan Huang, M. Hammond, and Ting Zhu. Harmony: Exploiting coarse-grained received signal strength from IoT devices for human activity recognition. In IEEE 24th Int. Conf. on Network Protocols (ICNP), pages 1--10, 2016.

[18]

Anh Luong, Alemayehu Solomon Abrar, Thomas Schmid, and Neal Patwari. RSS step size: 1 dB is not enough! In Proc. of the 3rd Workshop on Hot Topics in Wireless, pages 17--21. ACM, 2016.

Digital Library

[19]

Youngwook Kim and Hao Ling. Human activity classification based on micro-doppler signatures using a support vector machine. IEEE Transactions on Geoscience and Remote Sensing, 47(5):1328--1337, 2009.

[20]

Ph Van Dorp and FCA Groen. Feature-based human motion parameter estimation with radar. IET Radar, Sonar 8 Navigation, 2(2):135--145, 2008.

[21]

Ajay Gadde, Moeness G Amin, Yimin D Zhang, and Fauzia Ahmad. Fall detection and classifications based on time-scale radar signal characteristics. In SPIE Defense+ Security, pages 907712--907712. International Society for Optics and Photonics, 2014.

[22]

Luis Ramirez Rivera et al. Radar-based fall detection exploiting time-frequency features. In Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit 8 International Conference on, pages 713--717. IEEE, 2014.

[23]

Baris Erol and Moeness G Amin. Fall motion detection using combined range and doppler features. In Signal Processing Conference (EUSIPCO), 2016 24th European, pages 2075--2080. IEEE, 2016.

[24]

Qisong Wu, Yimin D Zhang, Wenbing Tao, and Moeness G Amin. Radar-based fall detection based on Doppler time--frequency signatures for assisted living. IET Radar, Sonar 8 Navigation, 9(2):164--172, 2015.

[25]

J. Hong, S. Tomii, and T. Ohtsuki. Cooperative fall detection using Doppler radar and array sensor. In IEEE 24th Annual Int. Symp. on Personal, Indoor, and Mobile Radio Communications (PIMRC), pages 3492--3496, 2013.

[26]

Yan Wang et al. E-eyes: device-free location-oriented activity identification using fine-grained wifi signatures. In Proc. of the 20th annual int. conf. on Mobile computing and networking (Mobicom), pages 617--628. ACM, 2014.

[27]

Wei Wang, Alex X. Liu, and Muhammad Shahzad. Gait Recognition Using WiFi Signals. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp‘16, 2016.

Digital Library

[28]

Kun Qian, Chenshu Wu, Zimu Zhou, Yue Zheng, Zheng Yang, and Yunhao Liu. Inferring Motion Direction using Commodity Wi-Fi for Interactive Exergames. In Proc. of the 2017 CHI Conference on Human Factors in Computing Systems, May 6--11 2017.

Digital Library

[29]

C. Han et al. Wifall: Device-free fall detection by wireless networks. In IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, 2014.

[30]

Yuxi Wang et al. Wifall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 16(2):581--594, 2017.

Digital Library

[31]

Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall. Predictable 802.11 Packet Delivery from Wireless Channel Measurements. SIGCOMM Comput. Commun. Rev., 40(4):159--170, August 2010.

Digital Library

[32]

Sameera Palipana, Piyush Agrawal, and Dirk Pesch. Channel State Information Based Human Presence Detection Using Non-linear Techniques. In Proc. of the 3rd ACM Int. Conf. on Systems for Energy-Efficient Built Environments, BuildSys‘16, 2016.

Digital Library

[33]

Jonathon Shlens. A tutorial on principal component analysis. CoRR, abs/1404.1100, 2014.

[34]

Baris Erol, Moeness Amin, Fauzia Ahmad, and Boualem Boashash. Radar fall detectors: A comparison. In SPIE Defense+ Security, pages 982918--982918. International Society for Optics and Photonics, 2016.

[35]

W. Tao et al. Color Image Segmentation Based on Mean Shift and Normalized Cuts. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(5):1382--1389, 2007.

Digital Library

[36]

Wenbing Tao, Hai Jin, Yimin Zhang, Liman Liu, and Desheng Wang. Image thresholding using graph cuts. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(5):1181--1195, 2008.

Digital Library

[37]

Philipp Heidenreich, Luke A Cirillo, and Abdelhak M Zoubir. Morphological image processing for FM source detection and localization. Signal Processing, 89(6):1070--1080, 2009.

Digital Library

[38]

Aihua Zhang, Bin Yang, and Ling Huang. Feature Extraction of EEG Signals Using Power Spectral Entropy. In Proc. of the 2008 Int. Conf. on BioMedical Engineering and Informatics - Volume 02, BMEI‘08, pages 435--439, 2008.

Digital Library

[39]

Daniel Halperin et al. Tool release: gathering 802.11n traces with channel state information. ACM SIGCOMM Comput. Commun. Rev., 41(1):53--53, 2011.

Digital Library

[40]

Sameera Palipana. Falldefi source code and data. https://github.com/dmsp123/FallDeFi, 2017.

[41]

David L Donoho. De-noising by soft-thresholding. IEEE transactions on information theory, 41(3):613--627, 1995.

Digital Library

[42]

Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321--357, 2002.

[43]

Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Digital Library

Cited By

Zheng NLi YJiang SLi YYao RDong CChen TYang YYin ZLiu Y(2025)AdaWiFi, Collaborative WiFi Sensing for Cross-Environment AdaptationIEEE Transactions on Mobile Computing10.1109/TMC.2024.347485324:2(845-858)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TMC.2024.3474853
Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
https://doi.org/10.1109/OJIM.2024.3502885
Regani SWang BHu YZhu GLiu K(2025)FallAware: An Explainable Learning Approach to Robust Fall Detection With WiFiIEEE Journal of Selected Areas in Sensors10.1109/JSAS.2024.35205172(71-83)Online publication date: 2025
https://doi.org/10.1109/JSAS.2024.3520517
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Index Terms

FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 4

December 2017

1298 pages

EISSN:2474-9567

DOI:10.1145/3178157

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Copyright © 2018 ACM.

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Publication History

Published: 08 January 2018

Accepted: 01 October 2017

Revised: 01 August 2017

Received: 01 May 2017

Published in IMWUT Volume 1, Issue 4

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Cited By

Zheng NLi YJiang SLi YYao RDong CChen TYang YYin ZLiu Y(2025)AdaWiFi, Collaborative WiFi Sensing for Cross-Environment AdaptationIEEE Transactions on Mobile Computing10.1109/TMC.2024.347485324:2(845-858)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TMC.2024.3474853
Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
https://doi.org/10.1109/OJIM.2024.3502885
Regani SWang BHu YZhu GLiu K(2025)FallAware: An Explainable Learning Approach to Robust Fall Detection With WiFiIEEE Journal of Selected Areas in Sensors10.1109/JSAS.2024.35205172(71-83)Online publication date: 2025
https://doi.org/10.1109/JSAS.2024.3520517
Dave RPatel D(2025)Efficient Human Activity Recognition using Deep Unfolding in 802.11bf Wi-Fi Sensing2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)10.1109/COMSNETS63942.2025.10885555(557-565)Online publication date: 6-Jan-2025
https://doi.org/10.1109/COMSNETS63942.2025.10885555
Yi EWu DXiong JZhang FNiu KLi WZhang DVanbever LZhang I(2024)BFMSenseProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691918(1697-1712)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691918
Zhu HDong EXu MLv HWu F(2024)Commodity Wi-Fi-Based Wireless Sensing Advancements over the Past Five YearsSensors10.3390/s2422719524:22(7195)Online publication date: 10-Nov-2024
https://doi.org/10.3390/s24227195
Ye KWu SCai YZhou LXiao LZhang XZheng ZLin J(2024)Transfer-Learning-Based Human Activity Recognition Using Antenna ArrayRemote Sensing10.3390/rs1605084516:5(845)Online publication date: 28-Feb-2024
https://doi.org/10.3390/rs16050845
Zhou ZLiu ZLiu YZhao YWang JZhang BXia YZhang XLi S(2024)TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack methodDIGITAL HEALTH10.1177/2055207624125904710Online publication date: 4-Jun-2024
https://doi.org/10.1177/20552076241259047
Wang ZChen ZZhang YGeng CSong WSun ZGuo BYu ZChen L(2024)GrainSense: A Wireless Grain Moisture Sensing System Based on Wi-Fi SignalsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785898:3(1-25)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678589
Hou WWu C(2024)RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data AugmentationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596208:2(1-26)Online publication date: 15-May-2024
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