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

Md. Sabir Hossain, Md. Mahfuzur Rahman, Md. Mahfuzur Rahman

2025

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.

Paper Citation

in Harvard Style

Hossain M. and Rahman M. (2025). Human Fall Detection in Poor Lighting Conditions Using CNN-Based Model. In Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: IS4WB_SC; ISBN 978-989-758-743-6, SciTePress, pages 414-420. DOI: 10.5220/0013502200003938

in Bibtex Style

@conference{is4wb_sc25,
author={Md. Hossain and Md. Rahman},
title={Human Fall Detection in Poor Lighting Conditions Using CNN-Based Model},
booktitle={Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: IS4WB_SC},
year={2025},
pages={414-420},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013502200003938},
isbn={978-989-758-743-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: IS4WB_SC
TI - Human Fall Detection in Poor Lighting Conditions Using CNN-Based Model
SN - 978-989-758-743-6
AU - Hossain M.
AU - Rahman M.
PY - 2025
SP - 414
EP - 420
DO - 10.5220/0013502200003938
PB - SciTePress