PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos.
Falls pose a significant health risk for elderly populations, necessitating advanced monitoring technologies. This study introduces a novel two-stage fall detection system that combines computer vision and machine learning to accurately identify fall events. The system uses the YOLOv11 object detect...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0325253 |
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| _version_ | 1850208818295734272 |
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| author | Vungsovanreach Kong Saravit Soeng Munirot Thon Wan-Sup Cho Anand Nayyar Tae-Kyung Kim |
| author_facet | Vungsovanreach Kong Saravit Soeng Munirot Thon Wan-Sup Cho Anand Nayyar Tae-Kyung Kim |
| author_sort | Vungsovanreach Kong |
| collection | DOAJ |
| description | Falls pose a significant health risk for elderly populations, necessitating advanced monitoring technologies. This study introduces a novel two-stage fall detection system that combines computer vision and machine learning to accurately identify fall events. The system uses the YOLOv11 object detection model to track individuals and estimate their body pose by identifying 17 key body points across video frames. The proposed approach extracts nine critical geometric features, including the center of mass and various body angles. These features are used to train a support vector machine (SVM) model for binary classification, distinguishing between standing and lying with high precision. The system's temporal validation method analyzes sequential frame changes, ensuring robust fall detection. Experimental results, evaluated on the University of Rzeszow Fall Detection (URFD) dataset and the Multiple Cameras Fall Dataset (MCFD), demonstrate exceptional performance, achieving 88.8% precision, 94.1% recall, an F1-score of 91.4%, and a specificity of 95.6%. The method outperforms existing approaches by effectively capturing complex geometric changes during fall events. The system is applicable to smart homes, wearable devices, and healthcare monitoring platforms, offering a scalable, reliable, and efficient solution to enhance safety and independence for elderly individuals, thereby contributing to advancements in health-monitoring technology. |
| format | Article |
| id | doaj-art-4f61eaef8b024adf8c20187a2a6af2ed |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-4f61eaef8b024adf8c20187a2a6af2ed2025-08-20T02:10:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032525310.1371/journal.pone.0325253PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos.Vungsovanreach KongSaravit SoengMunirot ThonWan-Sup ChoAnand NayyarTae-Kyung KimFalls pose a significant health risk for elderly populations, necessitating advanced monitoring technologies. This study introduces a novel two-stage fall detection system that combines computer vision and machine learning to accurately identify fall events. The system uses the YOLOv11 object detection model to track individuals and estimate their body pose by identifying 17 key body points across video frames. The proposed approach extracts nine critical geometric features, including the center of mass and various body angles. These features are used to train a support vector machine (SVM) model for binary classification, distinguishing between standing and lying with high precision. The system's temporal validation method analyzes sequential frame changes, ensuring robust fall detection. Experimental results, evaluated on the University of Rzeszow Fall Detection (URFD) dataset and the Multiple Cameras Fall Dataset (MCFD), demonstrate exceptional performance, achieving 88.8% precision, 94.1% recall, an F1-score of 91.4%, and a specificity of 95.6%. The method outperforms existing approaches by effectively capturing complex geometric changes during fall events. The system is applicable to smart homes, wearable devices, and healthcare monitoring platforms, offering a scalable, reliable, and efficient solution to enhance safety and independence for elderly individuals, thereby contributing to advancements in health-monitoring technology.https://doi.org/10.1371/journal.pone.0325253 |
| spellingShingle | Vungsovanreach Kong Saravit Soeng Munirot Thon Wan-Sup Cho Anand Nayyar Tae-Kyung Kim PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos. PLoS ONE |
| title | PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos. |
| title_full | PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos. |
| title_fullStr | PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos. |
| title_full_unstemmed | PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos. |
| title_short | PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos. |
| title_sort | pifr a novel approach for analyzing pose angle based human activity to automate fall detection in videos |
| url | https://doi.org/10.1371/journal.pone.0325253 |
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