A Real-time intelligent shoe system for fall detection

Injury duo to falling has accounted for a significant portion of accident. In order to provide prompt first-aid service to the victims, automatic and wearable devices are necessary to report the accident as soon as it occurs. This paper presents an intelligent shoe system which can not only detect the fall, but also classify the fall direction, especially the serious backward fall. In the prototype, eight pairs of force sensing resistors (FSRs) acquire the forces in different location of the insole. To reduce the computational cost and power consumption, and enhance the real-time performance, we propose an approach to reduce the sensor number based on principle component analysis (PCA), and lighten the system into a four-pair version. By means of artificial neural network (ANN), we classify the system input into three observations, and develop a finite state machine to trigger correct alarm and prevent false alarm by other complex human actions. To overcome the problem in shortage of learning data to detect the falling direction, nearest neighbor approach is utilized, which learns the necessary pattern from abundant tilted standing data. The experiment validates system and approaches to detect fall and fall direction.