Work-in-Progress, PupilWare-M: Cognitive Load Estimation Using Unmodified Smartphone Cameras

Cognitive load refers to the amount of informationa person can process or hold in working memory. Historically, the psychology community has estimated this quantity objectively by monitoring the involuntary dilations and constrictions of the pupil using medical grade equipment known as pupillometers. At the same time, researchers in the HCI and Ubi Comp communities have hypothesized how cognitive load sensing might be integrated into context aware computing systems, but limitations of sensing cognitive load ubiquitously and reliably prevent the mass integration of such a technology. Our system, Pupil Ware-M, seeks to begin bridging this sensing gap. We build upon a recent platform, Pupil Ware, which measures a user's sub-millimeter pupil dilation from an unmodified camera. We update the Pupil Ware sensing system with a calibration protocol that brings pupillary responses of a diverse range of people and lighting conditions onto a single 0.0-1.0 scale called Cog Point. Furthermore, we update and optimize the algorithms employed to run in real time from a smartphone. We validate the calibration process using eight users in a controlled experiment where cognitive load is simple to determine from its situational context. Discussion of future work and remaining challenges is then described.