Fall detection system by machine learning framework for public health.

Abstract

The elderly population is growing every year in Brazil. Consequently, health risks in elderly is a concern for public health system. During the aging process, the mobility is affected, and falls are more frequent causing injuries and even death, whose causes can be prevented, with reduction of financial costs. Therefore, a low-cost inertial sensor-based system is a tool to fulfill the need for detecting falls in elderly. In this paper, we present our system as a proof of concept for the study of fall and we propose a low cost and more accessible system for fall detection using inertial sensors. The inertial sensor collects data, identifies and detect four different “fall states”. The aim is to use this system in public health. In real-time, it will advise any person around the elder about the fall. Different machine learning classifiers are tested in the train dataset, and the best one was used for training the sensor data. Then, the model was compared with unknown sensor data (captured and from available datasets) to guess at which state the person is. We found out that there were only 15 wrong observations from all trials, thus, the system has potential to be used to detect falls.