EyeMove: Using Electrooculography to Provide Mobility for the Disabled

This engineering project investigated and developed an affordable system that is capable of controlling an electric wheelchair using a person’s eye movement. Such a system can be of significant help in providing mobility to quadriplegics and people with other immobilizing disabilities. The developed system is based on ocular tracking methods through the use of electrooculographic (EOG) signals. Electrooculography is a technique for measuring bio-potentials around the eyes resulting from eye movement. They are caused by a dipole effect created between the cornea and the retina. EOG signals were captured by Ag-AgCl electrodes placed in the vicinity of the eyes–around canthi (the points where the eyelids meet)–to detect horizontal movement and above the eyebrow and below the eye to capture vertical movement. Based on the movement of the eyes, these electrodes captured a positive or negative potential. An overview of the available literature finds that quadriplegics and individuals with severe paralysis lose almost all muscular control and frequently find themselves unable to have proper motor control of body parts connected to the spine. Thus, the eyes are often the only part of the body that people with such conditions can, in fact, control. In these cases, an EOG based wheelchair system provides the possibility of independent mobility, something victims, unfortunately, are not capable of. Ultimately, this results in an overall improvement in the quality of their lives. In this project, a scaled prototype using off-the-shelf components was created using two independently controlled motors on a chassis. The developmental process consisted of the design, implementation, and verification of an EOG signal acquisition system to work across several different conditions, including gender and various age groups (i.e. skin conditions). The system was composed of a robust front-end signal acquisition and processing circuit that used several filter and gain stages to acquire, filter out noise, and digitize very low-amplitude EOG signals. The digital signals were subsequently used as inputs to a motor controller that was implemented and programmed using the Arduino UNO microcontroller. Upon completing the project, a cost analysis of the system indicated that it is affordable and can be implemented across the world for a lower cost than most existing independent mobility systems. Lastly, with respect to applicability, it is a simple process to retrofit an existing electric wheelchair with the EOG based system developed within this project, which drastically reduces the cost of the system because an entirely new electric wheelchair is not required. Thus, this project developed an economical system and proved that it is feasible to control a wheelchair for the disabled with the movement of a user’s eyes.