Emergency Robotic System for the Elderly Behavior Detection

The phenomenon of population aging is escalating, and China is rapidly reaching 100 million empty-nesters as a result of the country's uneven regional economic development and declining population growth. We created an emergency robot system for detecting geriatric behavior due to the issue that empty-nesters may experience acute disease or other emergency situation without prompt medical attention. To accomplish our goals, we employ the YOLO V3 algorithm to recognize persons in a complicated setting. Meanwhile we use the AlphaPose model firstly in order to detect the joint points of the elderly. And at that basis, we use the ST-GCN models to complete the posture estimation and detection, finish the categorization of postures. In this way, we could determine whether the elderly are in an emergency scenario or not. Also, the M5StickC open-source bracelets are used to improve the accuracy of the emergency detection. They were used concurrently to measure information including acceleration to assist in determining whether the elderly person is in emergency at the same time. Once the emergency situation has been verified, a robot car will be sent to quickly bring first aid supplies to the elderly. This approach can prevent losing the initial opportunity to perform first aid.