Detection and motion analysis of knee joints in three-dimensional point cloud data measured using a depth camera

Because patients are often dissatisfied after total knee arthroplasty (TKA), we offer an improved protocol with a three dimensional (3D) joint instability analysis system that generates basic data for navigation during TKA. The system detects knee joint instability on images and analyzes it using a weight/moment ratio provided by a six-axis force sensor. We used an Intel RealSense depth camera to acquire 3D template data of the knee joint. We then searched for similar data in a 3D point cloud of data of the entire leg, thereby locating the knee joint in that cloud. We established a template shape using two algorithms, RANSAC and ICP, and then measured knee joint angles. By setting the knee joint detection result as the center position of the 3D point cloud of the entire leg, the point cloud data could be divided into thigh and shin portions. The thigh and shin axes created by each obtained point cloud were then determined, and an angle with two degrees of freedom formed by the two axial directions was calculated. This angle was considered the measurement result of the knee joint angle. Experiments using real images confirmed that our method has sufficient accuracy for this navigation system.

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