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.

翻译：老年人跌倒是一个主要的健康问题，常导致严重伤害和生活质量下降。本文提出"BlockTheFall"——一种基于可穿戴设备的跌倒检测框架，通过可穿戴设备的传感器数据实时检测跌倒。为准确识别模式并检测跌倒，采用机器学习算法对收集的传感器数据进行分析。为确保数据完整性和安全性，该框架利用区块链技术存储和验证跌倒事件数据。所提出的框架旨在为跌倒检测提供高效可靠的解决方案，改善应急响应能力并提升老年人的整体福祉。正在进行进一步的实验和评估以验证该框架的有效性和可行性，目前已在区分真实跌倒与模拟跌倒方面显示出良好前景。通过提供及时准确的跌倒检测与响应，该框架有望显著提升老年人照护质量。