Fall detection for elderly people using the variation of key points of human skeleton

2021/4/26 21:06:52   来源:

ABSTRACT In the area of health care, fall is a dangerous problem for aged persons. Sometimes, they are a serious cause of death. On the other hand, the number of aged persons will increase in the future. Therefore,it is necessary to develop an accurate system to detect fall. In this paper, we present spatiotemporal method todetect fall form videos filmed by surveillance cameras. Firstly, we computed key points of human skeleton.We calculated distances and angles between key points of each two pair sequences frames. After that,we applied Principal Component Analysis (PCA) to unify the dimension of features. Finally, we utilizedSupport Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbors (KNN) to classify features. We found that SVM is the best classifier to our method. The results of our algorithm are as follow: accuracy is 98.5%, sensitivity is 97% and the specificity is 100%.


In this section, we present our proposed method to detect fall. Firstly, we extracted features from the videos. Indeed, extracting features is devised to two steps: we detect key points of the human body’s skeleton. We used a 2D skeleton model to detect key points. Our challenge is using just simple RGB camera to detect fall. After that, we use these key points to compute the change of distance and angle between the same key points into each two pair sequential frames. After that, we apply Principal Component Analysis (PCA) to unify the size of the videos. Finally, we classify the features that we have computed to detect fall in the video.