Real-Time Pupillary Analysis By An Intelligent Embedded System

Abstract: With no online pupillary analysis methods today, both the medical and the research fields are left to carry out a lengthy, manual and often faulty examination. A real-time, intelligent, embedded systems solution to pupillary analysis would help reduce faulty diagnosis, speed-up the analysis procedure by eliminating the human expert operator and in general, provide a versatile and highly adaptable research tool. Therefore, this thesis has sought to investigate, develop and test possible system designs for pupillary analysis, with the aim for caffeine detection. A pair of LED manipulator glasses have been designed to standardize the illumination method across testing. A data analysis method of the raw pupillary data has been established offline and then adapted to a real-time platform. ANN was chosen as classification algorithm. The accuracy of the ANN from the offline analysis was 94% while for the online classification the obtained accuracy was 17%. A realtime data communication and synchronization method has been developed. The resulting system showed reliable and fast execution times. Data analysis and classification took no longer than 2ms, faulty data detection showed consistent results. Data communication suffered no message loss. In conclusion, it is reported that a real-time, intelligent, embedded solution is feasible for pupillary analysis.