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Facial nerve paresis is a severe complication that arises post-head and necksurgery; This results in articulation problems, facial asymmetry, and severeproblems in non-verbal communication. To overcome the side effects ofpost-surgery facial paralysis, rehabilitation requires which last for severalweeks. This paper discusses an unsupervised approach to rehabilitating patientswho have temporary facial paralysis due to damage in mimetic muscles. The workaims to make the rehabilitation process objective compared to the currentsubjective approach, such as House-Brackmann (HB) scale. Also, the approachwill assist clinicians by reducing their workload in assessing the improvementduring rehabilitation. This paper focuses on the clustering approach to monitorthe rehabilitation process. We compare the results obtained from differentclustering algorithms on various forms of the same data set, namely dynamicform, data expressed as functional data using B-spline basis expansion, and byfinding the functional principal components of the functional data. The studycontains data set of 85 distinct patients with 120 measurements obtained usinga Kinect stereo-vision camera. The method distinguish effectively betweenpatients with the least and greatest degree of facial paralysis, howeverpatients with adjacent degrees of paralysis provide some challenges. Inaddition, we compared the cluster results to the HB scale outputs.

Mimetic Muscle Rehabilitation Analysis Using Clustering of Low Dimensional 3D Kinect Data