Muscular dystrophy patients in different stages of the disease have trouble in communicating with their environment. In most muscular dystrophy diseases and ALS in particular, the brain functions normally and so do the patient’s eyes.
Today there are systems that allow the patient to communicate using equipment, such as identifying a gaze towards a monitor and creating words and sentences using the identification. These systems cost a lot of money and burden the patients and their families who are faced with a situation where they wish to maintain the ability to communicate with their loved one.
In this project we explored several ways to enable communication between the patient and his environment and in addition to create a system that would be cheap and accessible. We first investigated the possibility of detecting brain waves using existing equipment, but due to the limitations of the equipment and the lack of accuracy, the decision was made to use a web camera.
The system we developed is a cheap system compared to the systems that exist in the market because it uses only a computer and a web camera that can, through rapid calibration and machine learning, allow the patient to communicate with his environment. The use of machine learning allows the system to be versatile in that there is no need to meet preconditions such as the position of the camera in relation to the patient or the quality of the camera, thus the system can be accessible and simple to operate.