Skills based evaluation of alternative input methods to command a semi-autonomous electric wheelchair
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© 2016 IEEE. This paper presents the evaluation, under standardized metrics, of alternative input methods to steer and maneuver a semi-autonomous electric wheelchair. The Human-Machine Interface (HMI), which includes a virtual joystick, head movements and speech recognition controls, was designed to facilitate mobility skills for severely disabled people. Thirteen tasks, which are common to all the wheelchair users, were attempted five times by controlling it with the virtual joystick and the hands-free interfaces in different areas for disabled and non-disabled people. Even though the prototype has an intelligent navigation control, based on fuzzy logic and ultrasonic sensors, the evaluation was done without assistance. The scored values showed that both controls, the head movements and the virtual joystick have similar capabilities, 92.3% and 100%, respectively. However, the 54.6% capacity score obtained for the speech control interface indicates the needs of the navigation assistance to accomplish some of the goals. Furthermore, the evaluation time indicates those skills which require more user's training with the interface and specifications to improve the total performance of the wheelchair.