  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.
