###### 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 (R\({}^{2}\)), and correlation coefficient were 4.67\({}^{\circ}\), 3.41\({}^{\circ}\), 0.94, and 0.97; 4.63\({}^{\circ}\), 3.41\({}^{\circ}\), 0.95, and 0.98; and 5.69\({}^{\circ}\), 4.17\({}^{\circ}\), 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. Author 20251529617744251  
is that although typically measured in only two dimensions, the thumb can move in three dimensions. The carompactacarpal (CMC) joint of the thumb, with its saddle-shaped configuration, enables biaxial movement [6; 7; 8; 9; 10], while rotational movement is generally considered negligible [7; 11; 12]. Consequently, three-dimensional evaluations are more appropriate as they account for the thumb's biaxial movement and rotational components, which are not fully captured using two-dimensional methods [6; 10; 13]. As an evaluation method, measuring thumb movement by considering it a cone centered on the CMC joint is optimal [14; 15; 16; 17; 18]. A notable advantage of this approach is the ability to simultaneously evaluate radial and palmar abduction. While previous studies have reported assessing thumb range of motion using markers and electromagnetic sensors, research on markerless methods remains limited [19]. In 2020, Google introduced MediaPipe Hand (MediaPipe), a system capable of estimating and detecting coordinates in real time from still or moving images of hands and bodies without the use of markers [20; 21]. However, the accuracy of range-of-motion evaluations using only MediaPipe has been reported to be unsatisfactory [22]. Similarly, previous studies in which MediaPipe alone was used to measure finger range of motion also demonstrated limited precision, with the mean difference reported to be \(-2.21\pm 9.29^{\circ}\) in the angle measurement between MediaPipe and a protractor [23]. Machine learning methods are increasingly being applied in medical research to enhance the predictive accuracy of parameters that were previously difficult to estimate [24; 25; 26; 27]. In our previous research, we adopted a conical model and used a combination of a protractor, MediaPipe, and machine learning to carry out markerless measurements [19]. This approach was used to evaluate thumb rotation angles and measure radial and palmar abduction in a unified manner and demonstrated high estimation accuracy. However, because the true values were measured using a protractor, the data collection process was time-consuming and posed challenges related to reproducibility. To address these limitations, in this study, we introduced an angle sensor to replace the protractor for measuring true values.