Wednesday, 4 December 2013
Wheelchair Control System by Using Electrooculogram Signal Processing
Brain-machine interfaces (BMI) realize a direct communication between the human brain and the external environment by translating human intentions into control signals. BMIs allow an individual with severe motor disabilities or aphasia to have effective control over devices such as computers, wheelchairs, assistive appliances and neural prostheses. This study aims to propose electrooculogram signal processing method for voluntary eye blink detection and apply it to wheelchair control system. In this study, we defined double blink and wink as a voluntary eye blink, and normal blink as an involuntary blink. The proposed method can detect voluntary eye blinks in distinction from involuntary eye blinks with 98.28 percent accuracy. Additionally, we showed the effectiveness of wheelchair control system.The system uses accelerometers to detect the user's head tilt in order to direct Robot movement. The moving of the robot is activated by the user's eye blinking through a sensor. The keyboard function is implemented by allowing the user to move through letters with head tilt and with eye blinking as the selection mechanism.