Yolov5-based fall detection algorithm for homebound people

Abstract

According to the statistics from the official website of the National Bureau of Statistics, the population over 60 years old in China is close to 260 million, accounting for about 18.7% of the total population. As society moves towards aging, the safety problem of the elder people alone is gradually highlighted, and the accidental fall of the elderly at home has become a problem that cannot be ignored, and developing fall detection technology to mitigate the danger of falls of the elderly is imperative. So, this paper proposes a fall detection method which is based on deep learning. Specifically, the method is based on the Yolov3 method, and to enhance the accuracy and speed, the Yolov3 algorithm is updated to Yolov5s. The final experimental results also confirm that the above method has achieved the purpose.