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Fall Detection with Unobtrusive Infrared Array Sensors

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Book cover Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System (MFI 2017)

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Abstract

As the world’s aging population grows, fall is becoming a major problem in public health. It is one of the most vital risks to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video cameras. Several machine learning based fall detection classifiers have been developed to process sensor data with various degrees of success. In this paper, we present a fall detection system using infrared array sensors with several deep learning methods, including long-short-term-memory and gated recurrent unit models. Evaluated with fall data collected in two different sets of configurations, we show that our approach gives significant improvement over existing works using the same infrared array sensor.

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Author information

Authors and Affiliations

  1. Swansea University, Swansea, UK

    Xiuyi Fan

  2. Nanyang Technological University, Singapore, Singapore

    Huiguo Zhang & Zhiqi Shen

  3. The University of British Columbia, Vancouver, Canada

    Cyril Leung

Authors
  1. Xiuyi Fan
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  2. Huiguo Zhang
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  3. Cyril Leung
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  4. Zhiqi Shen
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Corresponding author

Correspondence to Huiguo Zhang .

Editor information

Editors and Affiliations

  1. Intelligent Systems Research Institute, Sungkyunkwan University, Suwon, Korea (Republic of)

    Sukhan Lee

  2. School of Electrical Engineering, Korea University, Seoul, Korea (Republic of)

    Hanseok Ko

  3. Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea (Republic of)

    Songhwai Oh

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Fan, X., Zhang, H., Leung, C., Shen, Z. (2018). Fall Detection with Unobtrusive Infrared Array Sensors. In: Lee, S., Ko, H., Oh, S. (eds) Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System. MFI 2017. Lecture Notes in Electrical Engineering, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-319-90509-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-90509-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90508-2

  • Online ISBN: 978-3-319-90509-9

  • eBook Packages: EngineeringEngineering (R0)

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