Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based Augmentation of IMU-Derived Orientation Data

Abstract

Automated evaluation of movement quality can enhance physiotherapeutic treatment and sports training by providing objective, real-time feedback. However, deep learning models that assess movements captured by inertial measurement units (IMUs) are often limited by data scarcity, class imbalance, and label ambiguity. We present a data augmentation method for IMU-derived orientation data that generates additional examples by systematically modifying movement trajectories and passing them through a musculoskeletal simulation. The approach enforces the joint-range limits of a musculoskeletal model and enables automatic labeling by combining inverse kinematic parameters with a knowledge-based evaluation strategy. Across four datasets of varying complexity, augmented variants closely resemble real-world data and contribute to gains in classification accuracy, generalization to unseen subjects, and patient-specific fine-tuning from few examples. The magnitude of these gains varies with dataset properties, in particular class balance and label ambiguity. These findings indicate that musculoskeletal simulation-based augmentation can address common challenges faced by deep learning applications in physiotherapeutic exercise evaluation.