Validation of a Human Pose Tracking Algorithm for Measuring Upper Limb Joints: Comparison With Photography-based Goniometry

Background: Range of motion (ROM) measurements are essential for diagnosing and evaluating upper extremity conditions. Clinical goniometry is the most commonly used methods but it is time-consuming and skill-demanding. Recent advances in human tracking algorithm suggest potential for automatic angle measuring from RGB images. It provides an attractive alternative for at-distance measuring. However, the reliability of this method has not been fully established. The purpose of this study is to evaluate if the results of algorithm are as reliable as human raters in upper limb movements.Methods: Thirty healthy young adults (20 males, 10 females) participated in this study. Participants were asked to performed a 6-motion task including movement of shoulder, elbow and wrist. Images of movements were capture by commercial digital camera. Each movement was measured by a pose tracking algorithm and compared with the surgeon-measurement results. The mean differences between the two measurements were compared. Pearson correlation coefficients were used to determine the relationship. Reliability was investigated by the intra-class correlation coefficients.Results: Comparing this algorithm-based method with manual measurement, the mean differences were less than 3 degrees in 5 motions (shoulder abduction: 0.51; shoulder elevation: 2.87; elbow flexion:0.38; elbow extension:0.65; wrist extension: 0.78) except wrist flexion. All the intra-class correlation coefficients were larger than 0.60. The Pearson coefficients also showed high correlations between the two measurements (p<0.001). Conclusions: Our results indicated that pose estimation is a reliable method to measure the shoulder and elbow angles, supporting RGB images for measuring joint ROM. Our results proved the possibility that patients can assess their ROM by photos taken by a digital camera.Trial registration: This study was registered in the Clinical Trials Center of The First Affiliated Hospital, Sun Yat-sen University (2021-387).