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Pubmed for Handhelds

PUBMED FOR HANDHELDS

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  • Title: An EOG-Based Human-Machine Interface for Wheelchair Control.
    Author: Huang Q, He S, Wang Q, Gu Z, Peng N, Li K, Zhang Y, Shao M, Li Y.
    Journal: IEEE Trans Biomed Eng; 2018 Sep; 65(9):2023-2032. PubMed ID: 28767359.
    Abstract:
    OBJECTIVE: Nonmanual human-machine interfaces (HMIs) have been studied for wheelchair control with the aim of helping severely paralyzed individuals regain some mobility. The challenge is to rapidly, accurately, and sufficiently produce control commands, such as left and right turns, forward and backward motions, acceleration, deceleration, and stopping. In this paper, a novel electrooculogram (EOG) based HMI is proposed for wheelchair control. METHODS: A total of 13 flashing buttons, each of which corresponds to a command, are presented in the graphical user interface. These buttons flash on a one-by-one manner in a predefined sequence. The user can select a button by blinking in sync with its flashes. The algorithm detects the eye blinks from a channel of vertical EOG data and determines the user's target button based on the synchronization between the detected blinks and the button's flashes. RESULTS: For healthy subjects/patients with spinal cord injuries, the proposed HMI achieved an average accuracy of 96.7% / 91.7% and a response time of 3.53 s/3.67 s with 0 false positive rates (FPRs). CONCLUSION: Using one channel of vertical EOG signals associated with eye blinks, the proposed HMI can accurately provide sufficient commands with a satisfactory response time. SIGNIFICANCE: The proposed HMI provides a novel nonmanual approach for severely paralyzed individuals to control a wheelchair. Compared with a newly established EOG-based HMI, the proposed HMI can generate more commands with higher accuracy, lower FPR, and fewer electrodes.
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