Tracking eye movement is integral for constructing virtual reality headsets, and in the healthcare sector, eye movement tracking is useful for diagnosing sleep disorders. Current iterations of commercial eye trackers mainly rely on visually tracking the wearer’s retina using head-mounted cameras. This approach suffers from many analytical limitations, in addition to the fact that the resulting headsets are heavy, cumbersome, and constricting.
Aside from cameras, another method to track eye movement is electrooculography (EoG), in which the electric pulses created by the seven extraocular muscles are detected by a skin-mounted electrode. While EoG is the most sensitive and error-free approach to track eye motion, a fully-integrated and portable EoG headset with five electrode leads is not known.
Here, Professors Trisha Andrew and Deepak Ganesan create a lightweight garment that can record EoG signals and, therefore, track the eye motions of the wearer. The PIs decorate a lightweight molded-foam sleeping mask with dry electrodes, and integrate a power source and processing circuit onto the headband of the sleep mask. This creates a fully-integrated and sensitive eye tracking system that can be used to create next-generation VR headsets and track eye movement in patients suffering from sleep disorders.
