Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems

The eye’s muscles are difficult to model to build an eye prototype or an interface between the eye’s movements and computers; they require complex mechanical equations for describing their movements and the generated voltage signals from the eye are not always adequate for classification. However, they are very important for developing human machine interfaces based on eye movements. Previously, these interfaces have been developed for people with disabilities or they have been used for teaching the anatomy and movements of the eye’s muscles. However, the eye’s electrical signals have low amplitude and sometimes high levels of noise. Hence, artificial neural networks and fuzzy logic systems are implemented using an ANFIS topology to perform this classification. This paper shows how the eye’s muscles can be modeled and implemented in a concept prototype using an ANFIS topology that is trained using experimental signals from an end user of the eye prototype. The results show excellent performance for prototype when the ANFIS topology is deployed.