Multi-Sensor Fall Detection for Smartphones

Elderly people fall detection is a very relevant and widely studied problem. It is associated with the need to detect fall events using pervasive and largely accepted technologies and the need to suddenly manage the first aid phase after a fall occurs. This research work aims to provide a non-invasive fall detection system, widely accepted by users, limiting data exchange only to the first aid request and help phase in order to reduce privacy issues. This paper proposes a fall detection methodology and a mobile application built on sensors available on smartphones, such as accelerometer, gyroscope, proximity sensor, microphone and GPS. The proposed fall detection method combines data from the five sensors using a threshold-based algorithm; the data processing allows fall detection and enables a first aid request sending a message to rescuers