PCA-SVM Algorithm for Classification of Skeletal Data-Based Eigen Postures
Nguyễn Thanh Hải, Trinh Hoai An · 2016
Falls are the major reason of serious injury and dangerous accident for elderly people. A recognition system is necessary to recognize falls early for help and treatment. In recent years, researchers have been developing many new methods of effective detection and recognition of human falls. In this paper, three subjects were introduced a system of fall recognition with five pairs of human postures (non-fall-fall, fall-stand, fall-sit, fall-bend, fall-lying) using a Kinect camera system. Features of skeletaldata with the human postures obtained from the camera system are extracted using a PCA algorithm. For fall recognition and sending notification message, a SVM algorithm is applied for training the feature data and classifying these postures. Experimental results show that the high effectiveness of the proposed approach for fall recognition and alert is nearly 82%.