Wearable System for Real Time Detection and Prediction of Falls

Ebunoluwa Adebesin; Sai Pranavi Jeedigunta; Shamant Mallikarjunappa; Atharv Joshi; Manuel Mersini; Rolando Herrero; Sergey Aityan; Abdolreza Mosaddegh; Haitham Tayyar

Abstract

Falls represent a major health challenge for elderly individuals, often leading to serious injuries and reduced quality of life. This paper presents a fall-detection architecture that performs intelligent data analysis through a centralized application. The system uses wearable sensing devices, accelerometers, pressure sensors, and gyroscopes, to capture motion data from the elderly subject. These sensor readings are transmitted using the low-power Long-Range (LoRa) communication protocol to the central application, where machine-learning algorithms evaluate the data in real time to identify potential fall events. This approach reduces the computational burden on wearable devices, supports scalable monitoring, and enables timely detection and rapid response in emergency situations. Overall, the framework enhances safety and autonomy for older adults while reducing strain on healthcare infrastructure.

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