BlockTheFall: Wearable Device-based Fall Detection Framework Powered by Machine Learning and Blockchain for Elderly Care
This addresses fall detection for elderly care, but it appears incremental as it combines existing technologies like wearables, ML, and blockchain without a clear novel breakthrough.
The paper tackles fall detection for the elderly by proposing BlockTheFall, a framework using wearable sensors and machine learning to analyze data in real time, with blockchain for security, showing promising results in distinguishing genuine from simulated falls.
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.