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When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists' evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error …