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Intelligent Fall Detection with Wearable IoT

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Complex, Intelligent, and Software Intensive Systems (CISIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 993))

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Abstract

Falls of older adults is a significant concern for themselves and caregivers as most of the times a fall leads to serious physical injuries. In the age of the Internet of things (IoT), connected smart homes and monitoring services have opened up opportunities for quality of life for the older adults. Detecting falls with wearable IoT devices can provide peace of mind for older adults and caregivers. Accelerometer based fall detection is investigated in this paper. Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) based Deep Learning network is applied to detect fall. LSTM network provides good accuracy based on the experiment. This experiment provides a promising indication that IoT-based fall monitoring can assure post-fall procedures to older adults and caregivers and this can increase the safety level and well-being of the older adults.

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Author information

Authors and Affiliations

  1. Western Sydney University, Sydney, Australia

    Farhad Ahamed, Seyed Shahrestani & Hon Cheung

Authors
  1. Farhad Ahamed
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  2. Seyed Shahrestani
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  3. Hon Cheung
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Corresponding author

Correspondence to Farhad Ahamed .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Faculty of Information Engineering, Fukuoka, Japan

    Leonard Barolli

  2. School of Software, University of Technology Sydney (UTS), Ultimo, NSW, Australia

    Farookh Khadeer Hussain

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Faculty of Information Engineering, Fukuoka, Japan

    Makoto Ikeda

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Ahamed, F., Shahrestani, S., Cheung, H. (2020). Intelligent Fall Detection with Wearable IoT. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_35

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