Indoor Human Fall Detection Using Data Augmentation-Assisted Transfer Learning in an Aging Population for Smart Homecare: A Deep Convolutional Neural Network Approach

We provide a one-of-a-kind solution to the problem of detecting human falls in naturalistic environments. This is crucial since falls cause thousands of deaths each year, and vision-based approaches provide a promising and effective way to identify falls. We consider this tough problem to be an example of action detection, and we solve it using the power of deep networks. In this study, the YOLOv3 model, a cutting-edge deep transfer learning object identification approach, is utilized to construct a standing and fall detection model. The detection model, according to the study's findings, has a training and validation accuracy of 97.60% and 92.63%, respectively, with an mAP value of 99.96%. The suggested model is suited for Smart Home Care for the Elderly because of its superior performance over existing algorithms for fall detection. The system has a total testing accuracy of 100%, with detection per frame accuracy ranging from 75% to 99%.