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BlockTheFall: Wearable Device-based Fall Detection Framework Powered by Machine Learning and Blockchain for Elderly Care
Conference proceeding   Peer reviewed

BlockTheFall: Wearable Device-based Fall Detection Framework Powered by Machine Learning and Blockchain for Elderly Care

Bilash Saha, Md Saiful Islam, Abm Kamrul Riad, Sharaban Tahora, Hossain Shahriar and Sweta Sneha
2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Vol.2023-, pp.1412-1417
Annual Computers, Software, and Applications Conference (COMPSAC), 47th (Torino, Italy, 06/26/2023–06/30/2023)
08/2023
DOI: https://doi.org/10.1109/COMPSAC57700.2023.00216
Web of Science ID: WOS:001046484100206

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8 Times Cited - Web of Science

Abstract

Blockchain technology Blockchains Fall detection Reliability Security Wearable devices Machine Learning Sociology Software Wearable Computers
Falls among the elderly are a major health concern, frequently resulting in serious injuries and a reduced quality of life. In this paper, we propose "BlockTheFall," a wearable device-based fall detection framework which detects falls in real time by using sensor data from wearable devices. To accurately identify patterns and detect falls, the collected sensor data is analyzed using machine learning algorithms. To ensure data integrity and security, the framework stores and verifies fall event data using blockchain technology. The proposed framework aims to provide an efficient and dependable solution for fall detection with improved emergency response, and elderly individuals' overall well-being. Further experiments and evaluations are being carried out to validate the effectiveness and feasibility of the proposed framework, which has shown promising results in distinguishing genuine falls from simulated falls. By providing timely and accurate fall detection and response, this framework has the potential to substantially boost the quality of elderly care.

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