Fall Detection in Elderly People

Rashid Mushtaq, Shahid Rafique, Muhammad Waseem Iqbal and Sadaquat Ali Ruk
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Rashid Mushtaq: Department of Computer Science & Information Technology, Superior University, Lahore, Pakistan
Shahid Rafique: Department of Computer Science & Information Technology, Superior University, Lahore, Pakistan
Muhammad Waseem Iqbal: Department of Software Engineering, Superior University, Lahore, Pakistan
Sadaquat Ali Ruk: Department of Computer Science, Shah Abdul Latif University, Ghotki Campus, Pakistan

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 1, 228-236

Abstract: Falls in elderly people are the second leading cause of accidental or unintentional injury deaths worldwide according to the World Health Organization (WHO). Around 6 million people die as a result of fatal falls with 80 percent of them coming from low- and middle-income countries. 37.3 million Elderly people suffer severe falls that necessitate medical attention. In rural and remote areas the lack of multispecialty healthcare infrastructure and specialized medical experts necessitates low-cost quick and skilled infrastructure/expert independent solutions for early fall detection mechanisms. The essential factors which are worked and discussed in the studies on the identification of incidents & procedures or movements associated with the sudden falling activities in the ageing people or senior people are identified in this review, which may provide support to the future research on the same subject. However, other parts of this study and literature which includes the sample size to be investigated targeted users or specific age having users under our study and methods for obtaining information regarding every application have yet to reach consensus.

Keywords: fall detection; wearable devices; cloud computing; framework; Sensors (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:1:p:228-236

DOI: 10.61506/01.00194

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