An Intelligent Rehabilitation Assessment Method for Small-Sample Scenarios: Machine Learning Validation Based on Rehabilitation Matching Value

Post-stroke finger dysfunction severely impacts patients’ daily living abilities and quality of life. Traditional rehabilitation assessment methods face challenges such as high subjectivity, insufficient precision, and difficulty in capturing subtle changes. These challenges are particularly pronounced in small-sample data scenarios, where the accuracy and robustness of assessment models are limited. This study proposes an intelligent rehabilitation assessment method tailored for small-sample scenarios, combining the rehabilitation matching value (RMV) with machine learning to address the challenges of rehabilitation assessment in such contexts. A rehabilitation matching value calculation model is constructed based on existing data, and interpolation methods are employed to expand the small-sample dataset. Machine learning models are then utilized for validation. Experimental results demonstrate that the proposed method effectively captures subtle changes in finger function, significantly improving the sensitivity and accuracy of rehabilitation assessments. This provides a scientific basis for the development of personalized rehabilitation training plans. Compared to traditional methods, the proposed approach exhibits significant advantages in flexibility, practicality, and adaptability to small-sample scenarios.