Good Day every one, I’m Sunggeun Ahn from Human computer interaction Lab at KAIST and
Chatham Labs.
We conducted this work as the summer internship program of Chatham Labs
And I appreciate to all colleagues involved in this project.
In this work, We present practical design guidelines for designing gaze based marking menu
and also present scale-invariant gaze marking menu interface, named StickyPie.
GUI menu interfaces offer help to novice users because they enable users to input a command
or select an item by navigating though the GUI interface and don’t require the user
to have prior knowledge about the structure of the menu.
However, for expert users, navigating these GUIs can be cumbersome and slow.
Thus, many applications also provide hotkey functionality,
to offer expert users, greater input efficiency.
Marking Menu technique presented to use advantages of both GUI menu for novice users and
Hot key input for expert users.
It also offers facilitate gradual transitions towards expert user.
Our aim on this work is optimizing the marking menu interface for eye gaze input modality.
There is an early adaptation of gaze-based marking menu, named pEye technique,
The same with early marking menu design, pEye technique uses hierarchical pie menu.
To select an item on pEye menu, A user could move their eyes from the inner circle of the
pie to a point outside the border.
When a user moves their eyes out to the border, the crossed wedge is selected then the next
pie appears at the center position of the crossed wedge.
We first tested traditional pie menu interface with various size and layouts,
We then found that the overshoot error was the highest proportion among the various types of errors.
Overshoot error refers to unintended selections caused when a user’s eye crosses the border
of the next pie unintentionally.
It is caused by the scale variance feature of the regular pie menu.
Therefore, we designed scale-invariant gaze marking menu, named StickyPie.
With RegularPie, a selection is done once the eye cursor is crossing the border.
Otherwise, With StickyPie, a selection is done once the saccadic eye movement is done
after the eye cursor is crossing the border.
Therefore, RegularPie could invoke multiple border crossing actions if the saccade distance
is too long to over the multiple borders.
Otherwise, StickyPie could invoke only one border crossing action per single saccade
stroke because the next pie render at the saccade landing position regardless of the
distance of the saccade eye movement.
This is the demonstration video of the StickyPie.
The moving trail on the video is showing a user’s eye movement.
We expected that the scale-invariant nature of StickyPie has the advantage of less overshoot
errors and better support of the transition from novice user to expert user.
To demonstrate those advantages, we compared StickyPie versus RegularPie.
Building knowledge expertise of the menu structure is the important to support of the transition
from novice user to expert user, So We designed a practical marking menu using
32 commands set for universal VR applications.
To design the menu, we follow our design guidelines derived from previous experiments in this paper.
This experiments conducted with 8 participants and each participants performed total of 288
tasks per each technique over 3 days.
As a result, In terms of information transfer rate,
StickyPie shows significantly better input efficiency than RegularPie.
In terms of The number of overshoot errors, StickyPie generated the errors 73% less than
that of RegularPie.
We then surveyed perceived expertise.
It is based on how many commands can be executed by gesture input rather navigating the GUI menu.
At the first day, we found that participants felt more confident to use gesture input with StickyPie
Finally, we surveyed which one is better to use.
6 of 8 participants preferred StickyPie,
because, It is Less concerned
about the position of the eye cursor after a border crossing.
And, It is Easier to input "gestures" using StickyPie and that they were more confdent than with RegularPie.
1 participant preferred RegularPie because he sometimes felt increasing eye fatigue
when a pie of StickyPie appeared in the periphery region.
Our contribution of this paper is three fold.
First, we present Design guideline for the Gaze Marking Menu for AR/VR
Second, from the design guideline,
we present Practical example of designing the Gaze Marking Menu
for general VR application.
This example could help how our design guideline apply for designing practical gaze marking menu.
Lastly, we present StickyPie and demonstrate better usability over RegularPie.