Fall Detection with the Optimal Feature Vectors Based on Support Vector Machine

摘要

Falls have caused extensive interest of the researchers for it becomes the second largest accidental injury to death in the world. And there are lots of approaches to fall detection at present. However, on account for the complexity of this problem, a preferable effective method for fall detection hasn't been present so far. This paper adopts a relatively high-predicted and stable SVM classifier to predict falls. 10 healthy young subjects participated in this study based on the Xsens MVN Biomech system. With the extraction of feature vectors, as well as the exploration of the best position, it found that the waist would be the best to measure body's motion, and the simple accelerometer can offer the preferable features for the classifier to determinate the falls well. Meanwhile it can get a high accuracy up to 96% by setting an optimal C and g with five-fold cross-validation testing.