Published on: April 2026
VISION-BASED ELDER ACTIVITY MONITORING AND FALL DETECTION SYSTEM
Kishore K Kavish Kumar S Ragunandhan S
Divya M
Article Status
Available Documents
Abstract
Index Terms—Fall Detection, Computer Vision, Pose Estima- tion, Elderly Monitoring, Real-Time Systems, YOLOv8, Human Activity Recognition
How to Cite this Paper
K, K., S, K. K. & S, R. (2026). Vision-Based Elder Activity Monitoring and Fall Detection System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.300
K, Kishore, et al.. "Vision-Based Elder Activity Monitoring and Fall Detection System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.300.
K, Kishore,Kavish S, and Ragunandhan S. "Vision-Based Elder Activity Monitoring and Fall Detection System." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.300.
References
- Ganthade, A. S. Belhe, P. S. Uravane, A. S. Gadade, and M. Rashid, “Fall Detection Methods for Elderly People–A Comprehensive Survey,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), IEEE, 2023, pp. 2477–2482.
- R. Sykes, “Next-generation fall detection: Harnessing human pose estimation and transformer technology,” Health Systems, vol. 14, no. 2,pp. 85–103, Apr. 2025.
- N. Aydog˘an and E. Cengiz, “Fall Detection Using Transformer and Pose Estimation,” in 2024 32nd Signal Processing and Communications Applications Conference (SIU), IEEE, 2024, p. 962.
- H. Hoang, J. W. Lee, M. J. Piran, and C. S. Park, “Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches,” IEEE Access, vol. 11, pp. 92322–92352, 2023.
- A. R. Azghadi, T. T. H. Nguyen, H. Fournier, M. Wachowicz, R. Richard, F. Palma, and H. Cao, “SF2D: Semi-supervised Federated Learning for Fall Detection using (Un)labelled Data in Edge-Cloud,” in Canadian AI 2025, 2025.
- Kong, S. Soeng, M. Thon, W. S. Cho, A. Nayyar, and T. K. Kim, “PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos,” PLOS ONE, vol. 20, no. 6, p. e0325253, Jun. 2025.
- Shin, A. S. M. Miah, R. Egawa, N. Hassan, K. Hirooka, and Y. Tomioka, “Multimodal Fall Detection Using Spatial–Temporal Attention and Bi-LSTM-Based Feature Fusion,” Future Internet, vol. 17, no. 4, p. 173, Apr. 2025.
- N. Alwakid, M. Humayun, and Z. Ahmad, “A Computer Vision- Enabled Smart Healthcare and Assistive Technology Framework for Urban Digital Environments to Support Elderly Individuals and People with Disabilities,” Journal of Disability Research, vol. 4, no. 3, Jun. 2025.
- X. Gaya-Morey, C. Manresa-Yee, and J. M. B. Rubio, “Deep learning for computer vision based activity recognition and fall detection of the elderly: A systematic review,” Applied Intelligence, vol. 54, no. 19, pp. 8982–9007, 2024.
- Cai, J. Chen, Q. He, J. Mou, and D. Camacho, “Fall Monitoring with Single IMU: A Large-Scale Dataset and A Novel Dual-Branch Network,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, 2025.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 13 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.
← Previous Article
Video Action Recognition in Noisy EnvironmentsNext Article →
Visual Rag System for Web Page Analysis

