A hybrid human fall detection method based on modified YOLOv8s and AlphaPose

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 d...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 2636 - 17
Hauptverfasser: Liu, Lei, Sun, Yeguo, Li, Yinyin, Liu, Yihong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 21.01.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/117
639/705/794
Accidental Falls - prevention & control
Accuracy
Algorithms
Competitiveness
Computer vision
Fall detection
Human pose estimation
Humanities and Social Sciences
Humans
multidisciplinary
Object detection
Science
Science (multidisciplinary)
Computer vision
Human pose estimation
Fall detection
Object detection
ISSN:2045-2322, 2045-2322
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Zusammenfassung: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-86429-6