Keypoint detection and functional evaluation of human lower limbs based on YOLOv8 and HRNet.
TL;DR
The correlation between the visual algorithm output and clinical functional status was further evaluated using established clinical indicators, including the Knee injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Timed Up and Go (TUG) test, and the 30-Second Sit-to-Stand Test.
Abstract
With the development of artificial intelligence and deep learning technology, human pose estimation has been widely applied in fields such as medical rehabilitation and motion analysis. To meet the needs of objective assessment of lower limb function in patients with knee joint diseases, we propose a method for detecting and assessing the function of human lower limb keypoints by integrating YOLOv8 with an improved HRNet. Firstly, the YOLOv8s model is used to locate the human region in the image. Then, the HRNet-W32 model, enhanced with a Gating Unit and Keypoint Attention Unit, is employed to detect six lower limb keypoints: left/right hip, left/right knee, and left/right ankle. Finally, based on the geometric relationship of hip-knee-ankle, the flexion-extension angle of the knee joint is calculated, and the sitting-to-standing action is recognized through a threshold state machine. Experimental results show that the improved HRNet achieves a 3.9% and 1.3% increase in mAP@0.5 and PCK@0.3 metrics, respectively, compared to the original HRNet-W32. In 200 sets of 30-Second Sit-to-Stand Test videos, the system achieves an average counting accuracy of 97.84% and an average absolute error of 0.18 times. The correlation between the visual algorithm output and clinical functional status was further evaluated using established clinical indicators, including the Knee injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Timed Up and Go (TUG) test, and the 30-Second Sit-to-Stand Test.