Applying Decision Tree for Utility Control System on Patient Room using Eye Activity Command

Some quadriplegics are unable to move their limbs from hand to foot. They require an assistive gadget to help them to have a better life. An assistive device to control some apparatus in the patient’s room was created using eye activity signals. This study aims to implement a decision tree for a utility control system in the patient room by using the eye activity command. Based on the user eye movement command, the decision tree is proposed to be implemented on the patient’s room equipment control system. The eye movement electrode, data acquisition, noise removal, and signal pre-processing, and the decision model are involved to develop the system. In this study, four instructions are used, including the switch to the next option, switch to the previous option, activate an option, and start the systems command. These commands are represented by gaze to the left, gaze to the right, wink, and double wink. The holdout was utilized to validate the model by dividing data into 70 percent training and 30 percent testing sets. The decision model’s accuracy is greater than 95%, according to the test results. This is an excellent outcome, and the model may be used in the system.