  Rehabilitation assessment is critical to determine an adequate intervention
for a patient. However, the current practices of assessment mainly rely on
therapist's experience, and assessment is infrequently executed due to the
limited availability of a therapist. In this paper, we identified the needs of
therapists to assess patient's functional abilities (e.g. alternative
perspective on assessment with quantitative information on patient's exercise
motions). As a result, we developed an intelligent decision support system that
can identify salient features of assessment using reinforcement learning to
assess the quality of motion and summarize patient specific analysis. We
evaluated this system with seven therapists using the dataset from 15 patient
performing three exercises. The evaluation demonstrates that our system is
preferred over a traditional system without analysis while presenting more
useful information and significantly increasing the agreement over therapists'
evaluation from 0.6600 to 0.7108 F1-scores ($p <0.05$). We discuss the
importance of presenting contextually relevant and salient information and
adaptation to develop a human and machine collaborative decision making system.
