Explainable Unsupervised Anomaly Detection for Identifying Compensatory Motor Behavior in Stroke Rehabilitation

Abstract

Occurrence of compensatory motor behaviors is common after stroke and other movement disorders, and can hinder patient rehabilitation. Detection of such behaviors is essential but traditionally relies on subjective, labor-intensive manual assessment. While supervised learning has been explored for automation, its effectiveness is constrained by inter-individual variability in compensatory strategies and the difficulty of obtaining detailed labels. To address these challenges, we present an unsupervised anomaly detection framework that models healthy motion distributions using Probabilistic Movement Primitives and quantifies deviations to detect potential compensations. The transparent structure of the proposed framework enables localization of compensation sources and differentiation between compensating and inhibited degrees of freedom, thereby enhancing its explainability and providing deeper insight into its outcomes. The framework was validated on a reaching-motion dataset, and achieved high accuracy in detecting compensation among stroke patients (F1 = 0.95). Trials from the evaluation dataset were further annotated with one or more compensation sources by multiple raters, yielding a rich dataset with quantifiable label uncertainties. Evaluation indicated that the framework achieved high performance in isolating compensation sources, particularly when annotator agreement is high.