Cross-Dataset Evaluation of Sit-to-Stand Movement Classifiers for Post-Stroke Rehabilitation

Abstract

Automated assessment could support at-home post-stroke rehabilitation, yet ensuring cross-dataset generalizability is critical for real-world adoption. This study evaluates rule-based and Random Forest classifiers, trained on XSense IMU data, against the independent CeTI-Locomotion dataset. Zero-shot evaluation demonstrated the robustness of the rule-based approach (71.3% accuracy) compared to Random Forest (17.8%), which significantly improved to 58.2% with one-shot calibration. These findings indicate that generalizability is achievable through biomechanically grounded or adaptive strategies, marking a key step toward robust, clinically deployable rehabilitation systems.