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Machine-learning models for shoulder rehabilitation exercises classification using a wearable system.
Martina Sassi1,2, Arianna Carnevale1, Matilde Mancuso1
1Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy.
Summary
Machine learning models accurately classify shoulder rehabilitation exercises using wearable sensors. The Random Forest classifier achieved 89.91% accuracy, showing potential for remote patient monitoring.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Machine Learning in Healthcare
Background:
- Shoulder rehabilitation is crucial for patients with rotator cuff tears.
- Accurate exercise classification is essential for effective rehabilitation and monitoring.
- Current methods may lack objective, real-time feedback.
Purpose of the Study:
- To train and evaluate machine learning models for automated classification of shoulder rehabilitation exercises.
- To assess the feasibility of using wearable sensors for this task.
Main Methods:
- Trained six supervised machine learning models (including Random Forest) using data from magneto-inertial sensors.
- Utilized data from 19 healthy subjects and 17 patients with rotator cuff tears performing six specific exercises.
- Evaluated classification performance using nested cross-validation.
Main Results:
- The Random Forest classifier achieved the highest performance, with 89.91% accuracy and an 89.89% F1-score.
- Demonstrated high accuracy in distinguishing between different shoulder rehabilitation exercises.
Conclusions:
- Wearable sensors combined with machine learning effectively classify shoulder rehabilitation exercises.
- The system shows promise for remote, home-based monitoring, reducing patient burden.
- The proposed system is feasible, effective, and user-friendly for patient-driven sensor placement.
