Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation
arXiv:2505.24415v2 Announce Type: replace-cross 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 that generates IMU data using musculoskeletal simulations integrated with systematic modifications of movement trajectories. The approach enforces anatomically plausible kinematic constra
The increasing availability of IMU sensors and the maturity of deep learning models for motion analysis are driving the need for more robust data augmentation techniques to overcome data scarcity challenges.
This development could significantly advance the accuracy and reliability of AI models used for objective and real-time movement evaluation in fields like physiotherapy and sports, reducing reliance on manual assessment.
The ability to generate anatomically plausible synthetic IMU data through musculoskeletal simulations will alleviate data scarcity for movement analysis, leading to more effective and personalized AI-driven interventions.
- · Physiotherapy clinics
- · Sports training facilities
- · AI model developers
- · Wearable sensor manufacturers
- · Traditional manual assessment methods
- · Low-quality exercise applications
Improved accuracy and widespread adoption of AI-powered movement assessment tools.
Personalized and data-driven rehabilitation and training programs becoming more accessible and effective.
Potential for early detection and prevention of musculoskeletal issues, extending healthy lifespans.
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Read at arXiv cs.AI