Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm

The fall is a risky event in the elderly people's daily living, especially the independent living, it often cause serious injury both in physiology and psychology. Wearable sensor based fall detection system had been proved in many experiments for its feasibility and effectiveness, but there remain some crucial problems, include: the people maybe forget to wear the clothes with micro sensors, which device standard should be selected between medical device standard and mass market standard, and how to control the false alarm probability to fit the individualized requirements. To deal with these problems, we think it is a reasonable design to combine micro sensors with an ambulatory daily using device which has a common network interface, and adjust the classification parameters via a remote server. In this paper, we embed a tri-axial accelerometer in a cellphone, connect to Internet via the wireless channel, and using 1-Class SVM (Support Vector Machine) algorithm for the preprocessing, KFD (Kernel Fisher Discriminant) and k-NN (Nearest Neighbour) algorithm for the precise classification. And there were 32 volunteers, 12 elders (age 60-80) and 20 younger (age 20-39), attended our experiments, the results show that this method can detect the falls effectively and make less disturbance to people's daily living than the general wearable sensor based fall detection systems.