ANFIS fusion algorithm for eye movement recognition via soft multi-functional electronic skin

Abstract

Eye movement detection has attracted increasing attention in the fields of safety driving, eye motion tracking, psychological assessment and telemedicine. Soft multi-functional electronic skin (SMFES) is designed to collect electrooculogram (EOG), skin temperature and sweat signals simultaneously for eye movement detection. Serpentine structure is adopted to ensure the stretchability of SMFES for satisfying large deformation (>30%) of the soft skin surface. The paper demonstrates that EOG, skin temperature and sweat signals are successfully collected under different eye movements. The feature data from EOG, skin temperature and sweat signals are extracted with different eye movements, and the principal component analysis (PCA) method is adopted to reduce the dimensionality of the feature space. The paper also proposes an intelligent data fusion algorithm for eye movement classification whose input vector is represented by the first three principal components. Adaptive neuro fuzzy inference system (ANFIS) is built to classify and recognize the eye movements (Up, Down, Left, and Right). Furthermore, experiments have demonstrated that ANFIS algorithm achieves 90% recognition accuracy of such eye movements. This work demonstrates that SMFES integrated with data fusion algorithm can successfully solve the eye movement tracking problem, with significant impact in safety driving and wearable electronics.