A hybrid human fall detection method based on modified YOLOv8s and AlphaPose
Abstract To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86429-6 |
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| author | Lei Liu Yeguo Sun Yinyin Li Yihong Liu |
| author_facet | Lei Liu Yeguo Sun Yinyin Li Yihong Liu |
| author_sort | Lei Liu |
| collection | DOAJ |
| description | Abstract To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as object detector. It uses SPD-Conv to preserve small object features and adds a small object detection layer, while using BCIOU as the loss function. These methods can effectively improve the accuracy of small object detection and significantly reduce the model size. Secondly, we improve the fall recognition accuracy by introducing a hybrid fall detection algorithm based on human skeletal nodes. Lastly, we build a multi-person fall detection dataset (MPFDD) to test the model’s effectiveness in multi-person scenarios. Validated on datasets Le2i and MPFDD, our method improves accuracy by 4.30%, F1 by 4.57%, and FPS by 37.50% faster than the AlphaPose. Compared with other models, our model improves accuracy by 5.33% on average, F1 by 5.51%, and FPS by 43.05% faster on average. Therefore, HFDMIA-Pose has significantly improved performance compared to the original model and it also demonstrates strong competitiveness over other advanced human fall detection models. Furthermore, it has the advantages of high detection accuracy, fewer model size, and fast speed, which makes it more suitable for resource constrained edge environments and can meet industrial and daily scenarios. |
| format | Article |
| id | doaj-art-eb4b51765cd5406389110864d8fc1701 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-eb4b51765cd5406389110864d8fc17012025-01-26T12:27:09ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-86429-6A hybrid human fall detection method based on modified YOLOv8s and AlphaPoseLei Liu0Yeguo Sun1Yinyin Li2Yihong Liu3Human-Computer Collaborative Robot Joint Laboratory of Anhui ProvinceSchool of Finance and Mathematics, Huainan Normal UniversitySchool of Computer Science, Huainan Normal UniversityHuman-Computer Collaborative Robot Joint Laboratory of Anhui ProvinceAbstract To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as object detector. It uses SPD-Conv to preserve small object features and adds a small object detection layer, while using BCIOU as the loss function. These methods can effectively improve the accuracy of small object detection and significantly reduce the model size. Secondly, we improve the fall recognition accuracy by introducing a hybrid fall detection algorithm based on human skeletal nodes. Lastly, we build a multi-person fall detection dataset (MPFDD) to test the model’s effectiveness in multi-person scenarios. Validated on datasets Le2i and MPFDD, our method improves accuracy by 4.30%, F1 by 4.57%, and FPS by 37.50% faster than the AlphaPose. Compared with other models, our model improves accuracy by 5.33% on average, F1 by 5.51%, and FPS by 43.05% faster on average. Therefore, HFDMIA-Pose has significantly improved performance compared to the original model and it also demonstrates strong competitiveness over other advanced human fall detection models. Furthermore, it has the advantages of high detection accuracy, fewer model size, and fast speed, which makes it more suitable for resource constrained edge environments and can meet industrial and daily scenarios.https://doi.org/10.1038/s41598-025-86429-6Fall detectionHuman pose estimationObject detectionComputer vision |
| spellingShingle | Lei Liu Yeguo Sun Yinyin Li Yihong Liu A hybrid human fall detection method based on modified YOLOv8s and AlphaPose Scientific Reports Fall detection Human pose estimation Object detection Computer vision |
| title | A hybrid human fall detection method based on modified YOLOv8s and AlphaPose |
| title_full | A hybrid human fall detection method based on modified YOLOv8s and AlphaPose |
| title_fullStr | A hybrid human fall detection method based on modified YOLOv8s and AlphaPose |
| title_full_unstemmed | A hybrid human fall detection method based on modified YOLOv8s and AlphaPose |
| title_short | A hybrid human fall detection method based on modified YOLOv8s and AlphaPose |
| title_sort | hybrid human fall detection method based on modified yolov8s and alphapose |
| topic | Fall detection Human pose estimation Object detection Computer vision |
| url | https://doi.org/10.1038/s41598-025-86429-6 |
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