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Accurate motor function assessment of post-stroke patients plays a critical role in their rehabilitation interventions. In this paper, we propose an approach to use wearable inertial sensing technology to quantitatively evaluate the patients' motor behavior. Different from existing wearable motor function assessment techniques that focus on building mapping functions that correlate sensed movement signals to the standard clinical rating scales, our approach provides a fine-grained assessment by capturing detailed patterns contained in the patients' movements. We collected data on three subjects including two post-stroke patients who have varying degrees of upper extremity hemiparesis. Our experimental results validate our approach and demonstrate that the captured patterns can be used to complement the standard clinical scores to provide fine-grained motor function assessment and help clinicians to track patients' gradual progress during rehabilitation.