Mimetic Muscle Rehabilitation Analysis Using Clustering of Low Dimensional 3D Kinect Data
This work addresses the need for an objective assessment tool for clinicians to reduce workload in rehabilitating patients with post-surgery facial paralysis, though it is incremental as it builds on existing clustering methods.
The paper tackled the problem of objectively monitoring facial paralysis rehabilitation by applying clustering algorithms to low-dimensional 3D Kinect data from 85 patients, effectively distinguishing between extreme cases but facing challenges with adjacent degrees of paralysis.
Facial nerve paresis is a severe complication that arises post-head and neck surgery; This results in articulation problems, facial asymmetry, and severe problems in non-verbal communication. To overcome the side effects of post-surgery facial paralysis, rehabilitation requires which last for several weeks. This paper discusses an unsupervised approach to rehabilitating patients who have temporary facial paralysis due to damage in mimetic muscles. The work aims to make the rehabilitation process objective compared to the current subjective approach, such as House-Brackmann (HB) scale. Also, the approach will assist clinicians by reducing their workload in assessing the improvement during rehabilitation. This paper focuses on the clustering approach to monitor the rehabilitation process. We compare the results obtained from different clustering algorithms on various forms of the same data set, namely dynamic form, data expressed as functional data using B-spline basis expansion, and by finding the functional principal components of the functional data. The study contains data set of 85 distinct patients with 120 measurements obtained using a Kinect stereo-vision camera. The method distinguish effectively between patients with the least and greatest degree of facial paralysis, however patients with adjacent degrees of paralysis provide some challenges. In addition, we compared the cluster results to the HB scale outputs.