Exploring Machine Learning in IoT based Fall Detection and Deterrence Systems for Elderly People

Abstract: Falls among the elderly are a major public health concern, often leading to severe injuries or even fatalities. This paper presents the design and implementation of an IoT-enabled wristband based fall detection system aimed at mitigating the risk of unattended falls in elderly individuals. The system integrates accelerometer and gyroscope sensors to continuously monitor the user’s movements and posture. Data from the sensors are processed using an ESP8266 microcontroller, which communicates wirelessly with a cloud-based server for real-time monitoring. A Machine Learning algorithm is employed to accurately detect falls, distinguishing them from normal activities like walking or sitting. Upon detecting a fall, the system triggers an alert through a relay module, which can be connected to a variety of external systems, such as alarms or emergency services. The proposed solution is both cost-effective and scalable, offering a reliable means of fall detection and alert generation to enhance elderly care.