This paper presents a novel generalized computational framework for quantitative kinematic evaluation of movement in a rehabilitation clinic setting. The framework integrates clinical knowledge and computational data-driven analysis together in a systematic manner. The framework provides three key benefits to rehabilitation: (a) the resulting continuous normalized measure allows the clinician to monitor movement quality on a fine scale and easily compare impairments across participants, (b) the framework reveals the effect of individual movement components on the composite movement performance helping the clinician decide the training foci, and (c) the evaluation runs in real-time, which allows the clinician to constantly track a patient's progress and make appropriate adaptations to the therapy protocol. The creation of such an evaluation is difficult because of the sparse amount of recorded clinical observations, the high dimensionality of movement and high variations in subject's performance. We address these issues by modeling the evaluation function as linear combination of multiple normalized kinematic attributes y=Σwiφi(xi) and estimating the attribute normalization function φi(·) by integrating distributions of idealized movement and deviated movement. The weights wi are derived from a therapist's pair-wise comparison using a modified RankSVM algorithm. We have applied this framework to evaluate upper limb movement for stroke survivors with excellent results - the evaluation results are highly correlated to the therapist's observations.