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OBJECTIVE: In this study, we combine a wheelchair and an intelligent robotic arm based on an electrooculogram (EOG) signal to help patients with spinal cord injuries (SCIs) accomplish a self-drinking task. The main challenge is to accurately control the wheelchair to ensure that the randomly located object is within a limited reachable space of the robotic arm (length: 0.8 m; width: 0.4 m; height: 0.6 m), which requires decimeter-level precision, and is still undemonstrated for EOG-based systems as well as EEG-based systems. APPROACH: A novel high-precision EOG-based human machine interface (HMI) is proposed which can effectively translate two kinds of eye movements (i.e. blinking and eyebrow raising) into various commands. For the wheelchair, positional precision can reach decimeter-level and the minimal steering angle is [Formula: see text]. For the intelligent robotic arm, shared control is implemented based on an EOG-based HMI, two cameras and the arm's own intelligence. MAIN RESULTS: After brief training, five healthy subjects and five paralyzed patients with severe SCIs successfully completed three experiments. For the healthy subjects/patients with SCIs, the system achieved an average accuracy of 99.3%/97.3%, an average response time of 1.91 s/2.02 s per command and an average stop-response time of 1.30 s/1.36 s with a 0 false operation rate. SIGNIFICANCE: The EOG-based HMI can provide sufficient precision control to integrate a wheelchair and a robotic arm into a system which can help patients with SCIs to accomplish a self-drinking task. (ChiCTR1800019764).

Original publication




Journal article


J Neural Eng

Publication Date





Adolescent, Adult, Blinking, Electrooculography, Eye Movements, Female, Humans, Male, Man-Machine Systems, Psychomotor Performance, Robotics, Spinal Cord Injuries, User-Computer Interface, Wheelchairs, Young Adult