Human Fall Detection in Poor Lighting Conditions Using CNN-Based Model

Md. Sabir Hossain, Md. Mahfuzur Rahman

Abstract: Human fall detection for elderly care has become a crucial field of research as it can cause serious injuries and impact the quality of life. In this article, we present a deep learning-based approach for human fall detection in low-lighting conditions using a convolutional neural network (CNN). We trained and evaluated our model on multiple datasets, both annotated for fall detection. The proposed architecture captures and analyzes the falls-related features effectively, even in achieving a significant amount of precision, recall, and F1-scores for human fall detection. Moreover, our proposed architecture outperforms (91% accuracy) several state-of-the-art models, including ResNet50, InceptionV3, MobileNet, XceptionNet, VGG16, VGG19, and DenseNet. With a reliable human fall detection architecture, this research significantly contributes to enhancing safety measures for elderly individuals.