Application of High-Level Fuzzy Petri Nets to fall detection system using smartphone

The coming of aging society signifies that falling down has become one of the very important issues in global public health. The smart phone, which is more commonly used than human fall detection devices, was selected as a mobile device of human fall detection and a fall detection algorithm was developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for fall detection. The signals detected by the tri-axial G-sensor adopted were converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data were captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use were used as input parameters. High-Level Fuzzy Petri Net was used for analysis and development to identify walking, exercising and falling down. The result of this study may be used in relevant equipment or the field of home nursing.