Evaluating rehabilitation progress using motion features identified by machine learning

BioRxiv : the Preprint Server for Biology
L. LuE. Zhao

Abstract

Evaluating progress throughout a patient's rehabilitation episode is critical for determining effectiveness of the selected treatments and contributing to the evidence-based practice. The evaluation process is complex due to the inherent large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper exploits unsupervised feature learning techniques to reduce the complexity of building the evaluation model of patients' progress. A new feature learning technique is developed to select the most significant features from a large amount of kinematic features measured from robotics, providing clinically useful information to health practitioners with reduction of modeling complexity. A novel indicator that can reflect monotonicity and trendability is proposed to evaluate the suitability of kinematic features, which are derived from the collected data of a population of stroke patients particip...Continue Reading

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