Care-Yolo11: Efficient Multi-Scale Representation 
Learning for Fall Detection

Yuewen Hua; Jiayu Zhao; Wenyi Zhang; Wushun Wei; Qi Li*

doi:10.70711/aitr.v3i6.8601

Care-Yolo11: Efficient Multi-Scale Representation Learning for Fall Detection

Yuewen Hua, Jiayu Zhao, Wenyi Zhang, Wushun Wei, Qi Li*

Abstract

This paper proposes Care-YOLO11, a lightweight fall detection model based on YOLO11 for elderly-care scenarios. By integrating EfficientViT-M3, Ghost-BiFPN, an Enhanced Multi-scale Attention module (EMA), the model improves robustness to complex postures
and small targets under real-time constraints. Experimental results show that Care-YOLO11 outperforms YOLOv8, YOLOv10, and YOLO11
while maintaining real-time inference with only 2.9M parameters, demonstrating its suitability for practical deployment.

Keywords

Fall detection; YOLO11; EfficientViT; Multi-scale attention; Ghost-BiFPN; Lightweight detection; Elderly-care monitoring

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References

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DOI: http://dx.doi.org/10.70711/aitr.v3i6.8601

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