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WiBFall: A Device-Free Fall Detection Model for Bathroom

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Mobile Networks and Management (MONAMI 2021)

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Abstract

Falling detection, especially for elderly people in confined areas such as bathrooms is vital for timely rescue. The mainstream vision-based fall detection approaches however are not applicable here for strong privacy concerns. It is therefore necessary to design a privacy-preserving fall detection model that utilizes other signals such as widely existed Wi-Fi for this scenario. Existing Wi-Fi based fall detection approaches often suffer from environment noise removal, resulting in moderate accuracy. In this paper, a Wi-Fi based fall detection model for bathroom environment, termed WiBFall, is proposed. Firstly, time series CSI data is reconstructed into a two-dimensional frequency energy map structure to obtain more feature capacity. Secondly, the reconstructed CSI data stream is filtered by Butterworth filter for noise elimination. Finally, the filtered data is used to train the established deep learning network to get a high accuracy fall detection model for bathroom. The experimental results show that the WiBFall not only reaches a fall detection accuracy of up to 99.63% in home bathroom environment, but also enjoys high robustness comparing to other schemes in different bathroom settings.

Supported by National Natural Science Foundation of China under Grant 61972092, Collaborative Innovation Major Project of Zhengzhou under Grant 20XTZX06013,the Research Foundation Plan in Higher Education Institutions of Henan Province under Grant 21A520043

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Authors and Affiliations

  1. School of Software Engineering, Zhengzhou University, Zhengzhou, 450002, China

    Pengsong Duan, Jingxin Li, Chenfei Jiao, Yangjie Cao & Jinsheng Kong

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  1. Pengsong Duan
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  2. Jingxin Li
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  3. Chenfei Jiao
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  4. Yangjie Cao
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  5. Jinsheng Kong
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Corresponding author

Correspondence to Yangjie Cao .

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Editors and Affiliations

  1. Technical University of Valencia, Valencia, Spain

    Carlos T. Calafate

  2. VTT Technical Research Centre of Finland, Oulu, Finland

    Xianfu Chen

  3. University of Macau, Taipa, China

    Yuan Wu

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Duan, P., Li, J., Jiao, C., Cao, Y., Kong, J. (2022). WiBFall: A Device-Free Fall Detection Model for Bathroom. In: Calafate, C.T., Chen, X., Wu, Y. (eds) Mobile Networks and Management. MONAMI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-94763-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-94763-7_14

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  • Online ISBN: 978-3-030-94763-7

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