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Improved User Adaptable Human Fall Detection and Verification Using Statistical Analysis

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Recent Advances in Intelligent Information Systems and Applied Mathematics (ICITAM 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 863))

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Abstract

Human fall detection systems are an important part of human monitoring systems especially for elderlies. Different studies were conducted using varieties of sensor to develop systems to accurately classify unintentional human falls from other activities of daily life. The major issues with the current studies using depth maps were the use of single threshold based algorithms and highly complex machine learning to detect falls. Therefore, the available systems cannot cater for the detection of fall events from people with different physical capabilities and differing environments they are living. This study proposes a user adaptable fall detection system with a statistical analysis based fall verification to overcome the issues of related works.

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Acknowledgements

This work is funded under FRGS Grant (VOT 1580) titled “Biomechanics computational modeling using depth maps for improvement on gait analysis”. The Author Yoosuf Nizam would also like to thank the Universiti Tun Hussein Onn Malaysia for providing lab components and GPPS (Project Vot No. U462) sponsor for his studies.

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Authors and Affiliations

  1. Biomedical Engineering Modeling and Simulation (BIOMEMS) Research Group, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    M. Mahadi Abdul Jamil, Yoosuf Nizam & Radzi Ambar

  2. Embedded Computing Systems (EmbCos), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Yoosuf Nizam, Mohd Norzali Hj Mohd, Radzi Ambar & Mohd Helmy Abd Wahab

  3. Department of Engineering, Faculty of Engineering, Science and Technology, The Maldives National University, 20371, Malé, Maldives

    Yoosuf Nizam & Radzi Ambar

Authors
  1. M. Mahadi Abdul Jamil
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  2. Yoosuf Nizam
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  3. Mohd Norzali Hj Mohd
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  4. Radzi Ambar
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  5. Mohd Helmy Abd Wahab
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Corresponding author

Correspondence to Mohd Norzali Hj Mohd .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Haldia Institute of Technology, Haldia, West Bengal, India

    Dipak Kumar Jana

  3. Maulana Abul Kalam Azad University of Technology, Haringhata, India

    Debasis Giri

  4. Haldia Institute of Technology, Haldia, India

    Arif Ahmed

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Mahadi Abdul Jamil, M., Nizam, Y., Mohd, M.N.H., Ambar, R., Wahab, M.H.A. (2020). Improved User Adaptable Human Fall Detection and Verification Using Statistical Analysis. In: Castillo, O., Jana, D., Giri, D., Ahmed, A. (eds) Recent Advances in Intelligent Information Systems and Applied Mathematics. ICITAM 2019. Studies in Computational Intelligence, vol 863. Springer, Cham. https://doi.org/10.1007/978-3-030-34152-7_52

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