Abstract
The rapid growth of the global elderly population has led to an increased need for efficient and reliable fall detection systems to ensure timely medical assistance. Traditional monitoring methods, such as wearable devices and environmental sensors, often face challenges in accuracy, reliability, and user compliance. This study proposes an indoor fall detection system for the elderly, integrating Internet of Things (IoT) and computer vision technologies, specifically utilizing YOLOv5 and OpenPose algorithms. YOLOv5 is employed for rapid and accurate human body detection, providing position information by computing the center of objects in frames or videos. OpenPose is then used for detailed real-time pose estimation, detecting 135 key points on the human body—including hands, face, and feet—to assess whether a fall has occurred based on posture analysis. The system was tested using two datasets, GMDCSA and URFD, achieving sensitivity values of 0.9412 and 0.9583, respectively, indicating high accuracy in fall detection. The false positive rates were 0.0588 and 0.2857, while the false negative rates were 0.0588 and 0.0417, demonstrating the system's reliability in minimizing detection errors. The integration of YOLOv5 and OpenPose leverages their combined strengths in object detection and pose estimation, resulting in a robust solution suitable for real-time applications.
Published Version
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