The Development and Validation of an Artificial Intelligence Model for Estimating Thumb Range of Motion Using Angle Sensors and Machine Learning: Targeting Radial Abduction, Palmar Abduction, and Pronation Angles

Abstract

An accurate assessment of thumb range of motion is crucial for diagnosing musculoskeletal conditions, evaluating functional impairments, and planning effective rehabilitation strategies. In this study, we aimed to enhance the accuracy of estimating thumb range of motion using a combination of MediaPipe, which is an AI-based posture estimation library, and machine learning methods, taking the values obtained using angle sensors to be the true values. Radial abduction, palmar abduction, and pronation angles were estimated using MediaPipe based on coordinates detected from videos of 18 healthy participants (nine males and nine females with an age range of 30–49 years) selected to reflect a balanced distribution of height and other physical characteristics. A conical thumb movement model was constructed, and parameters were generated based on the coordinate data. Five machine learning models were evaluated, with LightGBM achieving the highest accuracy across all metrics. Specifically, for radial abduction, palmar abduction, and supination, the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and correlation coefficient were 4.67°, 3.41°, 0.94, and 0.97; 4.63°, 3.41°, 0.95, and 0.98; and 5.69°, 4.17°, 0.88, and 0.94, respectively. These results demonstrate that when estimating thumb range of motion, the AI model trained using angle sensor data and LightGBM achieved accuracy that was high and comparable to that of prior methods involving the use of MediaPipe and a protractor.

Keywords

• thumb movement
• range of motion (ROM)
• machine learning (ML)
• artificial intelligence (AI)