Infrared image fall detection for the elderly based on improved residual network

Abstract

To solve the problem that traditional computer vision for elderly fall detection cannot protect the privacy of the elderly, this paper uses infrared array sensors for elderly fall detection and constructs a human infrared image fall detection system using an independently designed Raspberry Pi 4b to collect human infrared image datasets. The ECA-ResNet18 network model based on the ResNet18 network model is constructed for infrared human action recognition. Experiments were conducted on different datasets. The detection accuracy of the method reached 97.5% and 99.5% for the infrared and visible datasets, respectively, which is 5.7 and 0.8 percentage points higher than the original model; the accuracy was also improved compared with other neural network models. The results show that the ECA-ResNet18 network model has high recognition accuracy and fast detection speed in action recognition of infrared images, which has some practical application value for the promotion of intelligent pension.