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.

翻译：面部神经麻痹是头颈部术后出现的严重并发症，会导致发音障碍、面部不对称及非语言交流的严重问题。为克服术后面瘫的副作用，患者需进行持续数周的康复训练。本文探讨了一种非监督式方法，用于治疗因模仿肌损伤导致暂时性面瘫的患者。研究旨在使康复过程从当前主观评估方式（如House-Brackmann量表）转向客观化。同时，该方法通过减少临床医生评估康复进展的工作量来辅助诊疗。本文重点采用聚类方法监测康复过程，比较了不同聚类算法在同一数据集多种形式（动态形式、基于B样条基展开的函数型数据、函数型主成分分析结果）下的表现。研究包含85名患者的120组测量数据，通过Kinect立体视觉相机采集。该方法能有效区分面瘫程度最轻和最重的患者，但对相邻瘫痪程度的区分存在一定挑战。此外，我们将聚类结果与HB量表输出进行了对比。