IDEAS home Printed from https://ideas.repec.org/a/spt/admaec/v11y2021i6f11_6_1.html
   My bibliography  Save this article

Elderly Fall Detection Devices Using Multiple AIoT Biomedical Sensors

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/AMAE%2fVol%2011_6_1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlos Medrano & Raul Igual & Inmaculada Plaza & Manuel Castro, 2014. "Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    2. Chih-Ning Huang & Chia-Tai Chan, 2014. "A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare," IJERPH, MDPI, vol. 11(4), pages 1-16, April.
    3. José Carlos Castillo & Davide Carneiro & Juan Serrano-Cuerda & Paulo Novais & Antonio Fernández-Caballero & José Neves, 2014. "A multi-modal approach for activity classification and fall detection," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(4), pages 810-824, April.
    4. Eduardo Casilari & Jose Antonio Santoyo-Ramón & Jose Manuel Cano-García, 2016. "Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-17, December.
    5. Melissa C Kilby & Semyon M Slobounov & Karl M Newell, 2014. "Postural Instability Detection: Aging and the Complexity of Spatial-Temporal Distributional Patterns for Virtually Contacting the Stability Boundary in Human Stance," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-12, October.
    6. Dave Archer & Michael A August & Georgios Bouloukakis & Christopher Davison & Mamadou H Diallo & Dhrubajyoti Ghosh & Christopher T Graves & Michael Hay & Xi He & Peeter Laud & Steve Lu & Ashwin Machan, 2022. "Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy," The Journal of Defense Modeling and Simulation, , vol. 19(3), pages 501-517, July.
    7. Ionut Anghel & Tudor Cioara & Dorin Moldovan & Marcel Antal & Claudia Daniela Pop & Ioan Salomie & Cristina Bianca Pop & Viorica Rozina Chifu, 2020. "Smart Environments and Social Robots for Age-Friendly Integrated Care Services," IJERPH, MDPI, vol. 17(11), pages 1-31, May.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spt:admaec:v:11:y:2021:i:6:f:11_6_1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.