Prototype of system to identify shape of figure from contour drawn by line of sight

Abstract

Some diseases, such as amyotrophic lateral sclerosis, make it impossible to move only a limited number of body parts. Patients with these diseases have normal thinking ability and will, but their means of expressing their intentions are diminished. For instance, patients who cannot move their mouths cannot speak, and patients who cannot move their fingers cannot write or operate a PC or smartphone. Eye gaze-based input systems are developed as a new input method in PCs and smartphones. Current gaze input systems often operate the computer by varying the duration of gazing and blinking. This system has two types of operations. Here, consider operating a smartphone with eye gaze-based input. In this case, selection of an icon and tap operations can be performed, but there are not enough types of eye gaze-based input to perform flick, pinch-in or pinch-out operations. In addition to gazing and blinking, Kosaka et al. propose drawing figures such as circles and rectangles on the screen with the line of sight. For instance, if a user surrounds the icon with the line of sight and the trajectory of the line is square, the user flicks it. This allows for more types of operations using eye tracking-based input. Therefore, Kosaka et al. developed a prototype system to estimate the type of figure drawn from the trajectory data of the line of sight on the screen using a probabilistic Hough transform, but the system did not perform well in recognizing figures. In this study, we developed and evaluated the prototype machine learning system to recognize the type of figure drawn from eye tracking data. We expect that the system recognizes shapes correctly even when gaze trajectory data contains a lot of noise or relatively complex shape data by using machine learning. The prototype system using transition learning correctly identified relatively large shapes 97% of the time and relatively small shapes 78% of the time.

Keywords: Figure Identification, Line of Sight, Gaze Input, Machine Learning

DOI: 10.54941/ahfe1006015