Cognitive Load Detection on Drivers by Pupillary Analysis

The pupillary analysis gives a better insight into the cognitive load on drivers while driving. The algorithm has been developed to detect the cognitive load by analysing the synthesized pupillary data. The synthesized pupil diameter values are subjected to smoothing filter analysis for the extraction of cognitive spikes. Later bandpower metric is used to quantify the extracted cognitive spikes and conclude the presence of cognitive load on synthesized data. The developed algorithm is validated with two existing algorithms by comparing the results and Root Mean Square Error (RMSE) values. Also, the hardware setup is developed using Arbitrary Function Generator (AFG), Raspberry pi 3B+, and Arduino for detecting cognitive spikes in the synthesized data.