Real-Time Fall Detection with Fine-Grained Posture Segmentation and Environmental Context Awareness

Abstract

Falls are a leading cause of severe injuries and fatalities among the elderly, particularly those living alone. Timely and accurate fall detection is critical to ensuring rapid medical intervention and mitigating associated health risks. Traditional fall detection methods, such as wearable sensors and conventional computer vision techniques, often suffer from high false alarm rates and limited adaptability to real-world environments. These limitations stem from challenges like occlusions, lighting variations, and the inability to reliably distinguish between actual falls and non-harmful postures, such as sitting or lying on a bed. To address these challenges, we propose a novel fall detection system that integrates fine-grained human posture segmentation with environmental context awareness. Our approach leverages an enhanced YOLOv11-Seg model to perform real-time semantic segmentation, enabling precise differentiation between actual falls and similar but non-critical scenarios. By analyzing the spatial relationship between the detected individual and surrounding furniture, our method significantly reduces false alarms while maintaining high detection accuracy. Experimental evaluations conducted across multiple benchmark datasets demonstrate the robustness of our solution, achieving superior performance in terms of accuracy, precision, recall, and F1-score. The proposed system not only outperforms existing state-of-the-art methods but also proves highly effective for real-world fall detection applications, offering a reliable and scalable solution to enhance elderly care and safety.