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Development of a Web-Based Information System for Real-Time Fainting Detection Using YOLO in Smart Healthcare
Corresponding Author(s) : Wiwit Agus Triyanto
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 4, November 2025
Abstract
Loss of consciousness (fainting) is a critical condition that requires prompt treatment, especially in the context of elderly health services and independent patient care. This research aims to develop a web-based information system that is able to detect fainting events in real-time using the You Only Look Once (YOLO) algorithm version 11, which is one of the latest approaches in deep learning-based object detection. The system is designed to monitor video from the surveillance camera directly, make visual inferences of the patient's posture, and provide automatic notifications if a loss of consciousness condition is detected. The dataset was obtained from the Roboflow platform and consists of 9,081 annotated images representing the fainting position. The YOLOv11 model was trained and tested using training data sharing, validation, and testing methods. The test results showed that the model achieved mAP, precision, recall and F1-score values of 98.70%, 98.00%, 97.30% and 97.65%, respectively. The developed information system is able to display the detection visually through the bounding box on the dashboard and record the time of the incident. With this performance, this system shows great potential in improving patient safety through intelligent monitoring and automated response in hospital, nursing home, and residential environments. This research also opens up opportunities for the development of more adaptive AI-based health monitoring systems and computer vision in the future.
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- R. Shinta, J. Jasril, M. Irsyad, F. Yanto, and S. Sanjaya, 'Classification of Leaf Disease Images of Rice Plants Using CNN with VGG-19 Architecture', J. Science and Inform., vol. 9, no. 1, pp. 37–45, 2023. http://doi.org/10.22216/jsi.v9i1.2175
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- K. Vijayaprabakaran, K. Sathiyamurthy, and M. Ponniamma, 'Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network', Int. J. Secur. Priv. Pervasive Comput., vol. 12, no. 1, pp. 36–48, 2020. http://doi.org/10.4018/ijsppc.2020010104
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- S. Shen, Z. Wu, and P. Zhang, 'Research on target detection method of distracted driving behavior based on improved YOLOv8', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2407.01864
- J. H. Yousif, 'Fuzzy logic approach for detecting drivers' drowsiness based on image processing and video streaming', 2021. http://doi.org/10.36227/techrxiv.16777927
- N. Adiuku, N. P. Avdelidis, G. Tang, and A. Plastropoulos, 'Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review', Sensors, vol. 24, no. 5, p. 1377, 2024. http://doi.org/10.3390/s24051377
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- A. Brankovic et al., 'Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment', Sci. Rep., vol. 12, no. 1, 2022. http://doi.org/10.1038/s41598-022-15877-1
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- K. Kumar and K. M. B. A. Safwan, 'Accelerating Object Detection with YOLOv4 for Real-Time Applications', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2410.16320
- A. Rasheed and M. Zarkoosh, 'YOLOv11 Optimization for Efficient Resource Utilization', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2412.14790
- M. Mao and M. Hong, 'YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11', Sensors, vol. 25, no. 7. Multidisciplinary Digital Publishing Institute, p. 2270, 2025. http://doi.org/10.3390/s25072270
- T. E. Lockhart et al., 'Prediction of fall risk among community-dwelling older adults using a wearable system', Sci. Rep., vol. 11, no. 1, 2021. http://doi.org/10.1038/s41598-021-00458-5
- W. Gong, 'Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2403.01736
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, 'You only look once: Unified, real-time object detection', in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788. http://dx.doi.org/10.1109/CVPR.2016.91
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- W. A. Triyanto, K. Adi, and J. E. Suseno, 'Indoor Location Mapping of Lameness Chickens with Multi Cameras and Perspective Transform Using Convolutional Neural Networks', Math. Model. Eng. Probl., vol. 11, no. 2, pp. 539–548, 2024. http://doi.org/10.18280/mmep.110227
- A. T. Y. Chen, J. Fan, M. Biglari-Abhari, and K. I. K. Wang, 'A computationally efficient pipeline for camera-based indoor person tracking', Int. Conf. Image Vis. Comput. New Zeal., vol. 2017-Decem, pp. 1–6, 2018. http://doi.org/10.1109/IVCNZ.2017.8402479
- M. Yaseen, 'What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2408.15857
- G. A. Pereira, 'Fall Detection for Industrial Setups Using YOLOv8 Variants', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2408.04605
- A. S. Geetha, 'Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2408.12550
References
R. Shinta, J. Jasril, M. Irsyad, F. Yanto, and S. Sanjaya, 'Classification of Leaf Disease Images of Rice Plants Using CNN with VGG-19 Architecture', J. Science and Inform., vol. 9, no. 1, pp. 37–45, 2023. http://doi.org/10.22216/jsi.v9i1.2175
J. Liu, J. Guo, and S. Zhang, 'YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection', Agronomy, vol. 15, no. 5, p. 1026, 2025. http://doi.org/10.3390/agronomy15051026
Q. Aini, N. Lutfiani, H. Kusumah, and M. S. Zahran, 'Object Detection and Recognition with Machine Learning Models: Yolo Model', CESS (Journal Comput. Eng. Syst. Sci., vol. 6, no. 2, p. 192, 2021. http://doi.org/10.24114/cess.v6i2.25840
K. Vijayaprabakaran, K. Sathiyamurthy, and M. Ponniamma, 'Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network', Int. J. Secur. Priv. Pervasive Comput., vol. 12, no. 1, pp. 36–48, 2020. http://doi.org/10.4018/ijsppc.2020010104
T. Shaik et al., 'Remote patient monitoring using artificial intelligence: Current state, applications, and challenges', Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 13, no. 2, 2023. http://doi.org/10.1002/widm.1485
S. Shen, Z. Wu, and P. Zhang, 'Research on target detection method of distracted driving behavior based on improved YOLOv8', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2407.01864
J. H. Yousif, 'Fuzzy logic approach for detecting drivers' drowsiness based on image processing and video streaming', 2021. http://doi.org/10.36227/techrxiv.16777927
N. Adiuku, N. P. Avdelidis, G. Tang, and A. Plastropoulos, 'Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review', Sensors, vol. 24, no. 5, p. 1377, 2024. http://doi.org/10.3390/s24051377
R. Huang, J. Pedoeem, and C. Chen, 'YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers', 2021 IEEE Int. Conf. Big Data (Big Data)Pp. 2503–2510, 2018. http://doi.org/10.1109/bigdata.2018.8621865
N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, 'Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2411.00201
Y.-H. Li, Y. Li, M.-Y. Wei, and G. Li, 'Innovation and challenges of artificial intelligence technology in personalized healthcare', Scientific Reports, vol. 14, no. 1. Nature Portfolio, 2024. http://doi.org/10.1038/s41598-024-70073-7
A. Brankovic et al., 'Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment', Sci. Rep., vol. 12, no. 1, 2022. http://doi.org/10.1038/s41598-022-15877-1
B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, 'Machine Learning for Medical Imaging', Radiographics, vol. 37, no. 2. Radiological Society of North America, pp. 505–515, 2017. http://doi.org/10.1148/rg.2017160130
K. Kumar and K. M. B. A. Safwan, 'Accelerating Object Detection with YOLOv4 for Real-Time Applications', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2410.16320
A. Rasheed and M. Zarkoosh, 'YOLOv11 Optimization for Efficient Resource Utilization', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2412.14790
M. Mao and M. Hong, 'YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11', Sensors, vol. 25, no. 7. Multidisciplinary Digital Publishing Institute, p. 2270, 2025. http://doi.org/10.3390/s25072270
T. E. Lockhart et al., 'Prediction of fall risk among community-dwelling older adults using a wearable system', Sci. Rep., vol. 11, no. 1, 2021. http://doi.org/10.1038/s41598-021-00458-5
W. Gong, 'Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2403.01736
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, 'You only look once: Unified, real-time object detection', in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788. http://dx.doi.org/10.1109/CVPR.2016.91
J. Terven and D. Cordova-Esparza, 'A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS', arXiv (Cornell University). Cornell University, 2023,. http://doi.org/10.48550/arxiv.2304.00501
W. A. Triyanto, K. Adi, and J. E. Suseno, 'Indoor Location Mapping of Lameness Chickens with Multi Cameras and Perspective Transform Using Convolutional Neural Networks', Math. Model. Eng. Probl., vol. 11, no. 2, pp. 539–548, 2024. http://doi.org/10.18280/mmep.110227
A. T. Y. Chen, J. Fan, M. Biglari-Abhari, and K. I. K. Wang, 'A computationally efficient pipeline for camera-based indoor person tracking', Int. Conf. Image Vis. Comput. New Zeal., vol. 2017-Decem, pp. 1–6, 2018. http://doi.org/10.1109/IVCNZ.2017.8402479
M. Yaseen, 'What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2408.15857
G. A. Pereira, 'Fall Detection for Industrial Setups Using YOLOv8 Variants', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2408.04605
A. S. Geetha, 'Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis', arXiv (Cornell Univ., 2024. http://doi.org/10.48550/arxiv.2408.12550