  Assessing the quality of movements for post-stroke patients during the
rehabilitation phase is vital given that there is no standard stroke
rehabilitation plan for all the patients. In fact, it depends basically on the
patient's functional independence and its progress along the rehabilitation
sessions. To tackle this challenge and make neurorehabilitation more agile, we
propose an automatic assessment pipeline that starts by recognizing patients'
movements by means of a shallow deep learning architecture, then measuring the
movement quality using jerk measure and related measures. A particularity of
this work is that the dataset used is clinically relevant, since it represents
movements inspired from Fugl-Meyer a well common upper-limb clinical stroke
assessment scale for stroke patients. We show that it is possible to detect the
contrast between healthy and patients movements in terms of smoothness, besides
achieving conclusions about the patients' progress during the rehabilitation
sessions that correspond to the clinicians' findings about each case.
