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Contactless Fall Detection for the Elderly

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Contactless Human Activity Analysis

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 200))

Abstract

Early fall detection imposes a challenge for preventing life-threatening conditions to the health in geriatrics. As per the World Health Organization, around that half of total elderly individuals fall every year, which is considered a major cause of death. Fall-down detection and prediction is an active research topic to track seniors who reside at home alone or under treatment in a hospital. With the advancement of pervasive sensing technology and artificial intelligence techniques, automatic and contactless fall detection is now possible via passive monitoring systems. However, such systems’ accuracy is not up to an admissible limit due to the context and ambient conditions. Among the two main categories of fall detection systems, such as wearable sensor-based and contactless sensor-based, security issues of wearable sensor-based detection systems make it unacceptable to researchers. Moreover, wearable sensors are required to be worn by the user for a longer period, making it undesirable by the users. As a result, throughout recent years, ambient and wireless sensor-based systems have been commonly used for fall detection. To define existing approaches for potential research directions, it is important to analyze the elderly fall detection system’s ongoing practices. This book chapter aims to provide a detailed overview of different existing methods/systems of fall detection based on contactless sensors.

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Acknowledgements

This research was supported by the Information and Communication Technology division of the Government of the People’s Republic of Bangladesh in 2018–2019.

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Authors and Affiliations

  1. Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh

    M. Jaber Al Nahian, Mehedi Hasan Raju & Zarin Tasnim

  2. Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  3. Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh

    Md Atiqur Rahman Ahad

  4. Department of Media Intelligent, Osaka University, Osaka, Japan

    Md Atiqur Rahman Ahad

  5. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M Shamim Kaiser

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  1. M. Jaber Al Nahian
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Correspondence to M. Jaber Al Nahian .

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  1. Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh

    Md Atiqur Rahman Ahad

  2. Multimedia R&D and Standards, Qualcomm Technologies Inc., San Diego, CA, USA

    Upal Mahbub

  3. College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA

    Tauhidur Rahman

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Nahian, M.J.A., Raju, M.H., Tasnim, Z., Mahmud, M., Ahad, M.A.R., Kaiser, M.S. (2021). Contactless Fall Detection for the Elderly. In: Ahad, M.A.R., Mahbub, U., Rahman, T. (eds) Contactless Human Activity Analysis. Intelligent Systems Reference Library, vol 200. Springer, Cham. https://doi.org/10.1007/978-3-030-68590-4_8

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