Falls detection and notification system using tri-axial accelerometer and gyroscope sensors of a smartphone

Fall situations can be risky especially for elderly people with health problems. This fact has highlighted the need of fall detection systems. Various fall detection systems have been proposed. The proposed systems commonly used special devices placed at several position of user's body. On the other hand, smartphones with various sensors including gyroscopes are available in the market nowadays. This has motivated us to propose a prototype system of fall detection application designed for a smart phone. A threshold based fall detection algorithm is adapted for the proposed system since it relatively requires lower computational processes compared to complex reasoning techniques. Hence is more suitable to be implemented in mart phones with limited resource and processing power. The basic idea of the algorithm is to detect dynamic situations of posture, followed by unintentional falls to lying postures. The algorithm monitors sets of linear acceleration data and angular velocity and compares them to set of thresholds obtained from training data from the observed user. The process to detect a fall situation followed by sending notification to colleagues can be done in real-time manners while still maintain high accuracy for certain fall situations.