Work

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

Year: 2025

Type: article

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.

Source: Infinite Science GmbH

DOI: 10.18416/automed.2026.2545

Authors: Palumbi, Lucia, Kuhlgatz, Timo, Caruso, Matteo, Seel, Thomas, Budde, Leon

Institutions: Leibniz University Hannover, Consejo Superior de Investigaciones Científicas, Centre for Automation and Robotics

Language: English

Topic: Stroke Rehabilitation and Recovery

Subfield: Rehabilitation

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7135400513

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