Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.159-167
Fall Detection in Elderly People at Home Using the YOLOv11-Pose Model
Qaseem Riyadh Khawam and Muhaned Al-Hashimi Abstract: Falls are a leading cause of serious harm and unintentional death among the elderly, especially in homes where direct monitoring or rapid assistance is unavailable. Most of the models based on computer vision developed to determine falls lack decent performance in the real world, e.g., they cannot work properly in the case of views lighting conditions. Such deficiencies result in the realization of detections or false alarms particularly in residences. The study will focus on promoting the accuracy of fall detection among older adults, by proposing a strong real-time system capability, where object detection is combined with human pose estimation. In this study, we propose an automated fall detection system using computer vision techniques, specifically the YOLOv11-pose model. A phone camera was used to capture high-resolution image data, which was then properly processed, labelled, and optimized before training multiple versions of YOLO11-pose using standardized hyperparameter settings to ensure fair comparison. Experimental results showed that all models performed well in fall detection and pose estimation, with smaller models ideal for rapid implementation, achieving high frames per second (FPS) of around 95 fps and larger models, such as YOLOv11m, achieved an accurate mAP@50 of 99.43%. This study determined that YOLOv11-Pose is an effective model for detecting falls in the elderly, with promising accuracy and speed. Future directions include improving performance in challenging imaging conditions, expanding the model to include more activities.
Keywords: Fall Detection, YOLO, Deep Learning, Pose Estimations, Real-Time Detection.
DOI: 10.25673/122081
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References:
- A. R. Khekan, H. S. Aghdasi, and P. Salehpoor, “Fast and high-precision human fall detection using improved YOLOv8 model,” 2024.
- A. Bansal, R. Sharma, and M. Kathuria, “A vision-based approach to enhance fall detection with fine-tuned Faster R-CNN,” in Proc. Int. Conf. Advanced Computing & Communication Technologies (ICACCTech), 2023, pp. 678–684.
- R. G. Cumming, G. Salkeld, M. Thomas, and G. Szonyi, “Prospective study of the impact of fear of falling on activities of daily living, SF-36 scores, and nursing home admission,” Journals of Gerontology: Series A, vol. 55, no. 5, pp. M299–M305, 2000.
- M. Montero-Odasso et al., “New horizons in falls prevention and management for older adults: A global initiative,” Age and Ageing, vol. 50, no. 5, pp. 1499–1507, 2021.
- B. Sarıçayır, E. Alican, and C. Özcan, “Deep learning-based human detection for fall injuries,” Computational Technologies in Healthcare, vol. 1, no. 2, pp. 83–92, 2023.
- A. R. Khekan, H. S. Aghdasi, and P. Salehpoor, “The impact of YOLO algorithms within fall detection applications: A review,” 2024.
- T. Chen, Z. Ding, and B. Li, “Elderly fall detection based on improved YOLOv5s network,” IEEE Access, vol. 10, pp. 91273–91282, 2022.
- X. Lv, Z. Gao, C. Yuan, M. Li, and C. Chen, “Hybrid real-time fall detection system based on deep learning and multi-sensor fusion,” in Proc. 6th Int. Conf. Big Data and Information Analytics (BigDIA), 2020, pp. 386–391.
- P. V. Er and K. K. Tan, “Wearable solution for robust fall detection,” in Assistive Technology for the Elderly. Elsevier, 2020, pp. 81–105.
- L. Ma, M. Liu, N. Wang, L. Wang, Y. Yang, and H. Wang, “Room-level fall detection based on ultra-wideband monostatic radar and convolutional long short-term memory,” Sensors, vol. 20, no. 4, p. 1105, 2020.
- K. Wang, G. Zhan, and W. Chen, “A new approach for IoT-based fall detection system using commodity mmWave sensors,” in Proc. 7th Int. Conf. Information Technology: IoT and Smart City, 2019, pp. 197–201.
- A. Benkaci, L. Sliman, and H. N. Dellys, “Vision-based human fall detection systems: A review,” Procedia Computer Science, vol. 241, pp. 203–211, 2024.
- L. Zhu, Z. Chen, and C. Tian, “Review of fall detection methods based on wearable devices,” Computer Engineering and Applications, vol. 55, no. 18, pp. 8–14, 2019.
- L. Ren and Y. Peng, “Research of fall detection and fall prevention technologies: A systematic review,” IEEE Access, vol. 7, pp. 77702–77722, 2019.
- C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2023, pp. 7464–7475.
- X. Huang et al., “SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments,” Applied Sciences, vol. 15, no. 1, p. 2026, 2025.
- D. Zhao, T. Song, J. Gao, D. Li, and Y. Niu, “YOLO-fall: A novel convolutional neural network model for fall detection in open spaces,” IEEE Access, vol. 12, pp. 26137–26149, 2024.
- H. Hwang, D. Kim, and H. Kim, “FD-YOLO: A YOLO network optimized for fall detection,” Applied Sciences, vol. 15, no. 1, p. 453, 2025.
- K.-L. Lu and E. T.-H. Chu, “An image-based fall detection system for the elderly,” Sensors, vol. 8, no. 10, p. 1995, 2018.
- A. Priadana et al., “HFD-YOLO: Improved YOLO network using efficient attention modules for real-time one-stage human fall detection,” 2025.
- X. Kan, S. Zhu, Y. Zhang, and C. Qian, “A lightweight human fall detection network,” Sensors, vol. 23, no. 22, p. 9069, 2023.
- X. Zheng, J. Cao, C. Wang, and P. Ma, “A high-precision human fall detection model based on FasterNet and deformable convolution,” Electronics, vol. 13, no. 14, p. 2798, 2024.
- H. Chen, W. Gu, Q. Zhang, X. Li, and X. Jiang, “Integrating attention mechanism and multi-scale feature extraction for fall detection,” Healthcare, vol. 10, no. 10, 2024.
- H. Khan et al., “Visionary vigilance: Optimized YOLOv8 for fallen person detection with large-scale benchmark dataset,” Measurement, vol. 149, p. 105195, 2024.
- Y. Qin, W. Miao, and C. Qian, “A high-precision fall detection model based on dynamic convolution in complex scenes,” Electronics, vol. 13, no. 6, p. 1141, 2024.
- L. Liu, A. Kurban, Y. Liu, and J. Ma, “Improved YOLOv11pose for posture estimation of Xinjiang Bactrian camels,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 12, 2024.
- R. Khanam and M. Hussain, “YOLOv11: An overview of the key architectural enhancements,” 2024.
- J. Bento, T. Paixão, and A. B. Alvarez, “Performance evaluation of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for stamp detection in scanned documents,” Applied Sciences, vol. 15, no. 6, p. 3154, 2025.
- Ultralytics, “YOLOv11 pose estimation guide,” May 2024. [Online]. Available: https://docs.ultralytics.com.
- L. He, Y. Zhou, L. Liu, and J. Ma, “Research and application of YOLOv11-based object segmentation in intelligent recognition at construction sites,” Buildings, vol. 14, no. 12, p. 3777, 2024.
- A. T. Khalaf and S. K. Abdulateef, “Ophthalmic diseases classification based on YOLOv8,” Journal of Robotics and Control, vol. 5, no. 2, pp. 408–415, 2024.
- M. T. Abdulhadi, Recognition and Tracking System for Human Action Using Deep Learning, University of Technology, 2023.
- E. Tîrziu, A.-M. Vasilevschi, A. Alexandru, and E. Tudora, “Enhanced fall detection using YOLOv7-W6-Pose for real-time elderly monitoring,” Sensors, vol. 16, no. 12, p. 472, 2024.
- Z. Luo et al., “Elderly fall detection algorithm based on improved YOLOv5s,” Multimedia Tools and Applications, vol. 53, no. 2, pp. 601–618, 2024.
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