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

TL;DR

BlockTheFall is a wearable device-based fall detection system that combines machine learning for accurate detection with blockchain technology for secure data management, aiming to improve elderly care and emergency response.

Contribution

It introduces a novel framework integrating wearable sensors, machine learning, and blockchain to enhance fall detection accuracy and data security in elderly care.

Findings

Promising results in distinguishing genuine falls from simulated ones

Improved accuracy and reliability in fall detection

Enhanced data security and integrity through blockchain

Abstract

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.