Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation
This work addresses data scarcity and quality issues for deep learning applications in physiotherapeutic exercise evaluation, offering a practical solution to enhance automated feedback systems.
The paper tackles the problem of limited and imbalanced IMU data for automated movement evaluation in physiotherapy and sports by introducing a musculoskeletal simulation-based data augmentation method that generates realistic, biomechanically plausible data. The result is a significant improvement in classification accuracy and generalization for neural network models, with benefits shown for patient-specific fine-tuning when data is scarce.
Automated evaluation of movement quality holds significant potential for enhancing physiotherapeutic treatments and sports training by providing objective, real-time feedback. However, the effectiveness of deep learning models in assessing movements captured by inertial measurement units (IMUs) is often hampered by limited data availability, class imbalance, and label ambiguity. In this work, we present a novel data augmentation method that generates realistic IMU data using musculoskeletal simulations integrated with systematic modifications of movement trajectories. Crucially, our approach ensures biomechanical plausibility and allows for automatic, reliable labeling by combining inverse kinematic parameters with a knowledge-based evaluation strategy. Extensive evaluations demonstrate that augmented variants closely resembles real-world data, significantly improving the classification accuracy and generalization capability of neural network models. Additionally, we highlight the benefits of augmented data for patient-specific fine-tuning scenarios, particularly when only limited subject-specific training examples are available. Our findings underline the practicality and efficacy of this augmentation method in overcoming common challenges faced by deep learning applications in physiotherapeutic exercise evaluation.