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A Data-Driven Approach for Online Pre-impact Fall Detection with Wearable Devices

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Sensor- and Video-Based Activity and Behavior Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 291))

Abstract

The implementation of wearable airbags to prevent fall injuries depends on accurate pre-impact fall detection and a clear distinction between activities of daily living (ADL) and them. We propose a novel pre-impact fall detection algorithm that is robust against ambiguous falling activities. We present a data-driven approach to estimate the fall risk from acceleration and angular velocity features and use thresholding techniques to robustly detect a fall before impact. In the experiment, we collect simulated fall data from subjects wearing an inertial sensor on their waist. As a result, we succeeded in significantly improving the accuracy of fall detection from 50.00 to 96.88%, the recall from 18.75 to 93.75%, and the specificity 81.25 to 100.00% over the baseline method.

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Acknowledgements

This work is supported by JSPS KAKENHI Grant Number JP17H01762, JST CREST, and NICT.

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Authors and Affiliations

  1. Graduate School of Enginnering, Nagoya University, Nagoya, Japan

    Takuto Yoshida, Kazuma Kano, Keisuke Higashiura, Kohei Yamaguchi, Koki Takigami, Kenta Urano, Takuro Yonezawa & Nobuo Kawaguchi

  2. National Institute of Informatics, Tokyo, Japan

    Shunsuke Aoki

Authors
  1. Takuto Yoshida
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  2. Kazuma Kano
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  3. Keisuke Higashiura
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  4. Kohei Yamaguchi
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  5. Koki Takigami
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  6. Kenta Urano
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  7. Shunsuke Aoki
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  8. Takuro Yonezawa
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  9. Nobuo Kawaguchi
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Corresponding author

Correspondence to Takuto Yoshida .

Editor information

Editors and Affiliations

  1. University of East London, London, UK

    Md Atiqur Rahman Ahad

  2. Kyushu Institute of Technology, Fukuoka, Japan

    Sozo Inoue

  3. Sussex University, Brighton, UK

    Daniel Roggen

  4. Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan

    Kaori Fujinami

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yoshida, T. et al. (2022). A Data-Driven Approach for Online Pre-impact Fall Detection with Wearable Devices. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Sensor- and Video-Based Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-19-0361-8_8

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