Estimation of Forearm Pronation–Supination Angles Using MediaPipe and IMU Sensors: Performance Comparison and Interpretability Analysis of Machine Learning Models

Abstract

This study aimed to develop a non-contact, marker-less machine learning model to estimate forearm pronation–supination angles using 2D hand landmarks derived from MediaPipe Hands, with inertial measurement unit sensor angles used as reference values. Twenty healthy adults were recorded under two camera conditions: medial (in-camera) and lateral (out-camera) viewpoints. Five regression models were trained and evaluated: Linear Regression, ElasticNet, Support Vector machine (SVM), Random Forest, and Light Gradient Boosting Machine (LightGBM). Among them, LightGBM achieved the highest accuracy, with a mean absolute error of 5.61° in the in-camera setting and 4.65° in the out-camera setting. The corresponding R2 values were 0.973 and 0.976, respectively. The SHAP analysis identified geometric variations in the palmar triangle as the primary contributors, whereas elbow joint landmarks had a limited effect on model predictions. These results suggest that forearm rotational angles can be reliably estimated from 2D images, with an accuracy comparable to that of conventional goniometers. This technique offers a promising alternative for functional evaluation in clinical settings without requiring physical contact or markers and may facilitate real-time assessment in remote rehabilitation or outpatient care.