Azure Kinect is a popular low-cost markerless Motion Capture (MoCap) system, showing promising results in clinical applications. However, during concurrent validation studies with a marker-based gold standard, reflective markers produce passive infrared (IR) noise, which significantly interferes with its tracking accuracy. In this study, we collected motion data from 15 healthy participants performing upper and lower limb exercises, concurrently recorded by Azure Kinect and the Vicon system. We found that Kinect's skeletal tracking primarily relies on IR images rather than depth images. Therefore, we developed a simple yet effective algorithm to mitigate noise in IR images. Our method significantly improved Kinect's skeletal tracking reliability, reducing missed poses from 10\% to negligible levels and decreasing bone length variability across frames. Additionally, joint angle measurements improved, with lower Mean Absolute Error (MAE) in Range of Motion (ROM) and higher Intraclass Correlation Coefficient (ICC) of ROM. The code developed for this study is available at https://github.com/spongebobbe/pyKinectAzureImageManipulation.