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Falls among the elderly are a major health concern, frequently resulting inserious injuries and a reduced quality of life. In this paper, we propose"BlockTheFall," a wearable device-based fall detection framework which detectsfalls in real time by using sensor data from wearable devices. To accuratelyidentify patterns and detect falls, the collected sensor data is analyzed usingmachine learning algorithms. To ensure data integrity and security, theframework stores and verifies fall event data using blockchain technology. Theproposed framework aims to provide an efficient and dependable solution forfall detection with improved emergency response, and elderly individuals'overall well-being. Further experiments and evaluations are being carried outto validate the effectiveness and feasibility of the proposed framework, whichhas shown promising results in distinguishing genuine falls from simulatedfalls. By providing timely and accurate fall detection and response, thisframework has the potential to substantially boost the quality of elderly care.

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