Elderly Fall Detection Using Machine Learning

Fall detection is a critical application within the realms of healthcare and eldercare, with the primary objective of promptly identifying and notifying caregivers or medical professionals when a fall occurs. This paper introduces a machine learning-centric approach to fall detection, harnessing sensor data and advanced algorithms to enhance precision and responsiveness. The system employs an array of sensors, such as accelerometers, gyroscopes, and depth cameras, for continuous monitoring of user movements. These sensor data undergo processing via machine learning algorithms, encompassing deep neural networks and feature extraction methodologies, to achieve precise fall detection while minimizing false positives.