Analysis of Kinect-Based Human Motion Capture Accuracy Using Skeletal Cosine Similarity Metrics

Abstract

Kinect, with its intrinsic and accessible human motion capture capabilities, found widespread application in real-world scenarios such as rehabilitation therapy and robot control. Consequently, a thorough analysis of its previously under-examined motion capture accuracy is of paramount importance to mitigate the risks potentially arising from recognition errors in practical applications. This study employs a high-precision, marker-based motion capture system to generate ground truth human pose data, enabling an evaluation of Azure Kinect's performance across a spectrum of tasks, which include both static postures and dynamic movement behaviors. Specifically, the cosine similarity for skeletal representation is employed to assess pose estimation accuracy from an application-centric perspective. Experimental results reveal that factors such as the subject's distance and orientation relative to the Kinect, as well as self-occlusion, exert a significant influence on the fidelity of Azure Kinect's human posture recognition. Optimal testing recommendations are derived based on the observed trends. Furthermore, a linear fitting analysis between the ground truth data and Azure Kinect's output suggests the potential for performance optimization under specific conditions. This research provides valuable insights for the informed deployment of Kinect in applications demanding high-precision motion recognition.