Recognizing elderly peoples by analyzing their walking pattern using body posture skeleton

The increasing age of the population has become a significant concern internationally. During the COVID-19 pandemic situation, it has been seen that the most sensitive and affected class of the population is the class of Elder’s. It is therefore necessary to track the movement and behavior of the old persons. This kind of monitoring could help them in providing assistance in their needy time. Our objective is to develop an approach to classify elderly people using skeleton data for their assistance. OpenPose algorithm is used here to detect human skeletons (joint positions) from the video sequences. OpenPose algorithm with a sliding window of size ‘N’ is used to achieve a real-time posture recognition framework. Posture features from each extracted skeleton are then used to build a classifier for recognizing elderly people. We also introduce here a new dataset that includes old person walk and young person walk video’s. The experimental outcomes reveal that the proposed method has achieved up to 98.45% training accuracy and 96.16% testing accuracy for deep feed-forward neural network (FFNN) classifier. This asserts the effectiveness of the approach.