Human Fall Detection Analysis with Image Recognition Using Convolutional Neural Network Approach

Abstract

Human falling may cause injuries and sometimes may lead to deadly conditions. Therefore, in recent decade, the systems used for monitoring of human falling and non-falling are receiving attention among research community for its diversified features and social benefits. These systems solve the problem of falling and gets activated to avert the likely incident with an alarm message, and uses fall recognition classifiers. System helps to identify the human in the intended regions, and classifiers are trained using the information available in the images. The lack of massive scale datasets and human errors limits the generalization of models in terms of robustness and efficiency to invisible regions. In the proposed work, an automatic fall detection using deep learning is modeled using dataset of falling and non-falling images. The sensitive information available in the original images is kept secure and private to maintain the safety and protection by the presented work. The experiments were conducted using real-world fall datasets having both types of human images, i.e., falling and non-falling, and the results obtained clearly indicate system enhancement for falling and non-falling image recognition using convolutional neural network (CNN) algorithm and achieving higher accuracy and reduced loss with a trained dataset which finds the optimal performance from real-time environments.