Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis
This work addresses the need for personalized and efficient neurorehabilitation assessment for stroke patients, though it is incremental as it builds on existing clinical scales and methods.
The authors tackled the problem of automatically assessing movement quality for post-stroke patients during rehabilitation by proposing a pipeline that uses a shallow deep learning architecture for movement recognition and jerk-based measures for smoothness analysis, showing it can detect differences between healthy and patient movements and align with clinicians' findings.
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.