An approach based on statistical features to fall detection

Falls in elderly is a very serious health problem. For these years, the wearable devices based on tri-axial accelerator has been proven to be an effective way to fall detection. Most current methods for fall detection are based on threshold and machine learning. A approach based on statistical features was proposed to distinguish falls and normal activities of daily living (ADL) in this paper. What is worth mentioning is that Kernel Principal component analysis (KPCA) is firstly used to extract the statistical features from the original 3D data of acceleration, we don’t need to design features specially. The support vector machine (SVM) algorithm and K-Nearest Neighbor(KNN) algorithm are combined for prediction. Finally the validation of the prediction is done to improve the accuracy. Algorithm is mainly conducted on the public databases(UCI). And our method obtained the result is proved to be better compared with the other literature based on this public databases. © 2016 SERSC.