• Hyun Seok Kim, So Young Kim, Young Ho Kim, and Kwang Suk Park.
• Interdisciplinary Program of Bioengineering, Seoul National University, Seoul 03080, Korea. khs0330kr@bmsil.snu.ac.kr.
• Sensors (Basel). 2015 Oct 21; 15 (10): 26756-68.

AbstractFacial nerve palsy induces a weakness or loss of facial expression through damage of the facial nerve. A quantitative and reliable assessment system for facial nerve palsy is required for both patients and clinicians. In this study, we propose a rapid and portable smartphone-based automatic diagnosis system that discriminates facial nerve palsy from normal subjects. Facial landmarks are localized and tracked by an incremental parallel cascade of the linear regression method. An asymmetry index is computed using the displacement ratio between the left and right side of the forehead and mouth regions during three motions: resting, raising eye-brow and smiling. To classify facial nerve palsy, we used Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), and Leave-one-out Cross Validation (LOOCV) with 36 subjects. The classification accuracy rate was 88.9%.

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