Accurate assessment of joint range of motion (ROM) is essential in both clinical and athletic contexts for monitoring mobility, guiding rehabilitation, and optimizing performance. Traditional tools such as goniometers are widely used but limited by operator dependency and static measurement constraints. Depth-sensing technologies offer a markerless alternative that may enhance practicality in field-based settings. This study examined the validity and reliability of the Microsoft Azure Kinect system compared with a digital goniometer for assessing hip and knee ROM in 30 elite female weightlifters from the Turkish National Team. Participants performed hip abduction, adduction, flexion, extension, and knee flexion, each measured using both tools. Agreement between systems was assessed using Pearson’s correlations, intraclass correlation coefficients ICC (3,1), and Bland-Altman analyses. No significant differences were found between methods (p > 0.05), with strong correlations (r = 0.974–0.997) and high intraclass correlation coefficients (ICC = 0.994–0.998), indicating excellent consistency. Bland-Altman analyses showed minimal bias and narrow limits of agreement. These findings support the Azure Kinect as a valid and reliable tool for lower-extremity ROM assessment and a viable alternative to traditional goniometers in field applications.
Işık et al. (Wed,) studied this question.