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Elderly Fall Detection Devices Using Multiple AIoT Biomedical Sensors

Author

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  • Cheng-Wen Lee
  • Hsiu-Mang Chuang

Abstract

Due to the influence of degeneration and chronic diseases of elderly people, a higher chance of fall-related injuries occurs among them. Falling is one of the accidents frequently confronted by elderly people, so this issue is worthy of concern. We propose diverse models to analyze falls through a wearable device. Then, we use Artificial Intelligence of Things (AIoT) biomedical sensors for fall detection to build a system for monitoring elderly people’s falls caused by dementia. The system can meet the safety needs of elderly people by providing communication, position tracking, fall detection, and pre-warning services. This device can be worn on the waist of an elderly people. Moreover, the device can monitor whether or not the person is walking normally, transmit the information to the rear-end system, and inform his/her family member via a cellphone app while an accident is occurring. Considering the risks on the fall test of elderly people, this study adopts activities of daily living (ADL) to verify the test. According to the test results, the accuracy of fall detection is 93.7%, the false positive rate is 6.2%, and the false negative rate is 6.5%. To improve the accuracy of fall detection and the timely handling of appropriate referrals, may be highly expected to reduce the occurrence of fall-related injuries. JEL classification numbers: D61, I30, O32.

Suggested Citation

  • Cheng-Wen Lee & Hsiu-Mang Chuang, 2021. "Elderly Fall Detection Devices Using Multiple AIoT Biomedical Sensors," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(6), pages 1-1.
    • Handle: RePEc:spt:admaec:v:11:y:2021:i:6:f:11_6_1
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    References listed on IDEAS

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    1. Mark V Albert & Konrad Kording & Megan Herrmann & Arun Jayaraman, 2012. "Fall Classification by Machine Learning Using Mobile Phones," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
    2. Fabio Bagalà & Clemens Becker & Angelo Cappello & Lorenzo Chiari & Kamiar Aminian & Jeffrey M Hausdorff & Wiebren Zijlstra & Jochen Klenk, 2012. "Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
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    More about this item

    Keywords

    Fall Detection; AIoT Sensor; Elderly People.;
    All these keywords.

    JEL classification:

    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • I30 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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