Welfare robots, as a category of robotics, seeks to improve the quality of life of the elderly and patients by availing a control mechanism to enable the participants to be self-dependent. This is achieved by using man-machine interfaces that manipulate certain external processes like feeding or communicating. This research aims to realize a man-machine interface using brainwave combined with object recognition applicable to patients with locked-in syndrome. The system utilizes a camera with pretrained object-detection system that recognizes the environment and displays the contents in an interface to solicit a choice using P300 signals. Being a camera-based system, field of view and luminance level were identified as possible influences. We designed six experiments by adapting the arrangement of stimuli (triangular or horizontal) and brightness/colour levels. The results showed that the horizontal arrangement had better accuracy than the triangular method. Further, colour was identified as a key parameter for the successful discrimination of target stimuli. From the paper, the precision of discrimination can be improved by adopting a harmonized arrangement and selecting the appropriate saturation/brightness of the interface.


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    Titel :

    Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave


    Beteiligte:

    Erscheinungsdatum :

    2022-09-30


    Anmerkungen:

    doi:10.30630/joiv.6.3.949
    JOIV : International Journal on Informatics Visualization; Vol 6, No 3 (2022); 736-742 ; 2549-9904 ; 2549-9610



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629