# Azure Kinect for joint range of motion analysis: A validity and reliability evaluation in elite female weightlifters

## TL;DR

This study shows that the Azure Kinect is a valid and reliable tool for measuring joint angles in elite female weightlifters, comparable to a digital goniometer.

## Contribution

The study provides empirical validation of Azure Kinect for joint range of motion analysis in elite athletes.

## Key findings

- Most joint angle measurements showed no significant differences between Azure Kinect and digital goniometer.
- High correlation and agreement were observed between the two tools (r = 0.82–0.99, ICC = 0.97–0.99).
- Bland–Altman plots showed minimal bias and narrow confidence intervals, indicating strong agreement.

## Abstract

Kinect is a markerless, portable, and affordable motion analysis tool used in clinical, rehabilitation, and sports settings. This study aimed to assess upper limb (shoulder and elbow) and cervical joint angles in elite female weightlifters using Azure Kinect and a digital goniometer, and to evaluate the Kinect’s validity and reliability. Joint angles were measured in elite female weightlifters (n = 21) using both a digital goniometer and Azure Kinect, with three repetitions per movement under standardized conditions (within a stabilization cage). Mean values were used for analysis. Statistical analysis included descriptive metrics and non-parametric tests. Tool comparisons were conducted using the Wilcoxon signed-rank test, Spearman’s correlation coefficient, and Bland–Altman plots. Reliability was evaluated through the Intraclass Correlation Coefficient (ICC), Coefficient of Variation (CV), and Coefficient of Repeatability (CR), with statistical significance set at p < 0.05. Most joint angle measurements showed no statistically significant differences between the digital goniometer and Azure Kinect (p > 0.05), although small discrepancies appeared in some movements. High correlation coefficients (r = 0.82–0.99) and strong agreement based on Intraclass Correlation Coefficient (ICC = 0.97–0.99) were observed. Bland–Altman analysis revealed minimal systematic bias and narrow confidence intervals. Comparable values in Coefficient of Variation (CV) and Coefficient of Repeatability (CR) indicated high stability and repeatability for both tools. Overall, results support Azure Kinect as a valid and reliable alternative to traditional digital goniometry for assessing cervical and upper extremity joint range of motion.