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  3. Vol. 10, No. 4, November 2025
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Vol. 10, No. 4, November 2025

Issue Published : Oct 16, 2025
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Development of a Web-Based Information System for Real-Time Fainting Detection Using YOLO in Smart Healthcare

https://doi.org/10.22219/kinetik.v10i4.2407
Wiwit Agus Triyanto
Universitas Muria Kudus

Corresponding Author(s) : Wiwit Agus Triyanto

at.wiwit@umk.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 4, November 2025
Article Published : Oct 16, 2025

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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 used comes from the Roboflow platform with a total 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, precison, recall and F1-Score values of 98.70%, 98.00%, 97.30% and 97.65%. 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 automatic 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.

Keywords

Web-based Information System Fainting YOLOv12 Computer Vision Smart Healtcare
Triyanto, W. A. (2025). Development of a Web-Based Information System for Real-Time Fainting Detection Using YOLO in Smart Healthcare. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(4). https://doi.org/10.22219/kinetik.v10i4.2407
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References
  1. Adiuku, N., Avdelidis, N. P., Tang, G., and Plastropoulos, A. (2024). Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review. Sensors, 24(5), 1377. https://doi.org/10.3390/s24051377
  2. Aini, Q., Lutfiani, N., Kusumah, H., and Zahran, M. S. (2021). Object Detection and Recognition With Machine Learning Models: Yolo Models. CESS (Journal of Computer Engineering Systems and Science), 6(2), 192. https://doi.org/10.24114/cess.v6i2.25840
  3. Brankovic, A., Hassanzadeh, H., Good, N., Mann, K., Khanna, S., Abdel‐Hafez, A., and Cook, D. (2022). Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-15877-1
  4. Chen, A. T. Y., Fan, J., Biglari-Abhari, M., and Wang, K. I. K. (2018). A computationally efficient pipeline for camera-based indoor person tracking. International Conference Image and Vision Computing New Zealand, 2017-Decem, 1–6. https://doi.org/10.1109/IVCNZ.2017.8402479
  5. Erickson, B. J., Korfiatis, P., Akkus, Z., and Kline, T. L. (2017). Machine Learning for Medical Imaging. In Radiographics (Vol. 37, Issue 2, pp. 505–515). Radiological Society of North America. https://doi.org/10.1148/rg.2017160130
  6. Geetha, A. S. (2024). Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.12550
  7. Gong, W. (2024). Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.01736
  8. Huang, R., Pedoeem, J., and Chen, C. (2018). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. 2021 IEEE International Conference on Big Data (Big Data), 2503–2510. https://doi.org/10.1109/bigdata.2018.8621865
  9. Jegham, N., Koh, C. Y., Abdelatti, M., and Hendawi, A. (2024). Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.00201
  10. Kumar, K., and Safwan, K. M. B. A. (2024). Accelerating Object Detection with YOLOv4 for Real-Time Applications. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2410.16320
  11. Li, Y.-H., Li, Y., Wei, M.-Y., and Li, G. (2024). Innovation and challenges of artificial intelligence technology in personalized healthcare. In Scientific Reports (Vol. 14, Issue 1). Nature Portfolio. https://doi.org/10.1038/s41598-024-70073-7
  12. Liu, J., Guo, J., and Zhang, S. (2025). YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection. Agronomy, 15(5), 1026. https://doi.org/10.3390/agronomy15051026
  13. Lockhart, T. E., Soangra, R., Yoon, H., Wu, T., Frames, C., Weaver, R., and Roberto, K. A. (2021). Prediction of fall risk among community-dwelling older adults using a wearable system. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-00458-5
  14. Mao, M., and Hong, M. (2025). YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11. In Sensors (Vol. 25, Issue 7, p. 2270). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/s25072270
  15. Pereira, G. A. (2024). Fall Detection for Industrial Setups Using YOLOv8 Variants. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.04605
  16. Rasheed, A., and Zarkoosh, M. (2024). YOLOv11 Optimization for Efficient Resource Utilization. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.14790
  17. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/http://dx.doi.org/10.1109/CVPR.2016.91
  18. Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., and Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 13(2). https://doi.org/10.1002/widm.1485
  19. Shen, S., Wu, Z., and Zhang, P. (2024). Research on target detection method of distracted driving behavior based on improved YOLOv8. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2407.01864
  20. Shinta, R., Jasril, J., Irsyad, M., Yanto, F., and Sanjaya, S. (2023). Classification of Leaf Disease Images of Rice Plants Using CNN with VGG-19 Architecture. Journal of Science and Informatics, 9(1), 37–45. https://doi.org/10.22216/jsi.v9i1.2175
  21. Terven, J., and Cordova-Esparza, D. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2304.00501
  22. Triyanto, W. A., Adi, K., and Suseno, J. E. (2024). Indoor Location Mapping of Lameness Chickens with Multi Cameras and Perspective Transform Using Convolutional Neural Networks. Mathematical Modelling of Engineering Problems, 11(2), 539–548. https://doi.org/10.18280/mmep.110227
  23. Vijayaprabakaran, K., Sathiyamurthy, K., and Ponniamma, M. (2020). Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network. International Journal of Security and Privacy in Pervasive Computing, 12(1), 36–48. https://doi.org/10.4018/ijsppc.2020010104
  24. Yaseen, M. (2024). What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.15857
  25. Yousif, J. H. (2021). Fuzzy logic approach for detecting drivers' drowsiness based on image processing and Video streaming. https://doi.org/10.36227/techrxiv.16777927
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References


Adiuku, N., Avdelidis, N. P., Tang, G., and Plastropoulos, A. (2024). Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review. Sensors, 24(5), 1377. https://doi.org/10.3390/s24051377

Aini, Q., Lutfiani, N., Kusumah, H., and Zahran, M. S. (2021). Object Detection and Recognition With Machine Learning Models: Yolo Models. CESS (Journal of Computer Engineering Systems and Science), 6(2), 192. https://doi.org/10.24114/cess.v6i2.25840

Brankovic, A., Hassanzadeh, H., Good, N., Mann, K., Khanna, S., Abdel‐Hafez, A., and Cook, D. (2022). Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-15877-1

Chen, A. T. Y., Fan, J., Biglari-Abhari, M., and Wang, K. I. K. (2018). A computationally efficient pipeline for camera-based indoor person tracking. International Conference Image and Vision Computing New Zealand, 2017-Decem, 1–6. https://doi.org/10.1109/IVCNZ.2017.8402479

Erickson, B. J., Korfiatis, P., Akkus, Z., and Kline, T. L. (2017). Machine Learning for Medical Imaging. In Radiographics (Vol. 37, Issue 2, pp. 505–515). Radiological Society of North America. https://doi.org/10.1148/rg.2017160130

Geetha, A. S. (2024). Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.12550

Gong, W. (2024). Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.01736

Huang, R., Pedoeem, J., and Chen, C. (2018). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. 2021 IEEE International Conference on Big Data (Big Data), 2503–2510. https://doi.org/10.1109/bigdata.2018.8621865

Jegham, N., Koh, C. Y., Abdelatti, M., and Hendawi, A. (2024). Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.00201

Kumar, K., and Safwan, K. M. B. A. (2024). Accelerating Object Detection with YOLOv4 for Real-Time Applications. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2410.16320

Li, Y.-H., Li, Y., Wei, M.-Y., and Li, G. (2024). Innovation and challenges of artificial intelligence technology in personalized healthcare. In Scientific Reports (Vol. 14, Issue 1). Nature Portfolio. https://doi.org/10.1038/s41598-024-70073-7

Liu, J., Guo, J., and Zhang, S. (2025). YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection. Agronomy, 15(5), 1026. https://doi.org/10.3390/agronomy15051026

Lockhart, T. E., Soangra, R., Yoon, H., Wu, T., Frames, C., Weaver, R., and Roberto, K. A. (2021). Prediction of fall risk among community-dwelling older adults using a wearable system. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-00458-5

Mao, M., and Hong, M. (2025). YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11. In Sensors (Vol. 25, Issue 7, p. 2270). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/s25072270

Pereira, G. A. (2024). Fall Detection for Industrial Setups Using YOLOv8 Variants. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.04605

Rasheed, A., and Zarkoosh, M. (2024). YOLOv11 Optimization for Efficient Resource Utilization. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.14790

Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/http://dx.doi.org/10.1109/CVPR.2016.91

Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., and Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 13(2). https://doi.org/10.1002/widm.1485

Shen, S., Wu, Z., and Zhang, P. (2024). Research on target detection method of distracted driving behavior based on improved YOLOv8. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2407.01864

Shinta, R., Jasril, J., Irsyad, M., Yanto, F., and Sanjaya, S. (2023). Classification of Leaf Disease Images of Rice Plants Using CNN with VGG-19 Architecture. Journal of Science and Informatics, 9(1), 37–45. https://doi.org/10.22216/jsi.v9i1.2175

Terven, J., and Cordova-Esparza, D. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2304.00501

Triyanto, W. A., Adi, K., and Suseno, J. E. (2024). Indoor Location Mapping of Lameness Chickens with Multi Cameras and Perspective Transform Using Convolutional Neural Networks. Mathematical Modelling of Engineering Problems, 11(2), 539–548. https://doi.org/10.18280/mmep.110227

Vijayaprabakaran, K., Sathiyamurthy, K., and Ponniamma, M. (2020). Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network. International Journal of Security and Privacy in Pervasive Computing, 12(1), 36–48. https://doi.org/10.4018/ijsppc.2020010104

Yaseen, M. (2024). What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.15857

Yousif, J. H. (2021). Fuzzy logic approach for detecting drivers' drowsiness based on image processing and Video streaming. https://doi.org/10.36227/techrxiv.16777927

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KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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