Most smart glasses have a small and limited field of view. The head-mounted display often spreads between the human central and peripheral vision. In this paper, we exploit this characteristic to display information in the peripheral vision of the user. We introduce a mobile peripheral vision model, which can be used on any smart glasses with a head-mounted display without any additional hardware requirement. This model taps into the blocked peripheral vision of a user and simplifies multi-tasking when using smart glasses. To display the potential applications of this model, we implement an application for indoor and outdoor navigation. We conduct an experiment on 20 people on both smartphone and smart glass to evaluate our model on indoor and outdoor conditions. Users report to have spent at least 50% less time looking at the screen by exploiting their peripheral vision with smart glass. 90% of the users Agree that using the model for navigation is more practical than standard navigation applications.

1. Peripheral Vision: A New Killer App for Smart Glasses
Isha Chaturvedi
Hong Kong University of Science & Technology
Farshid Hassani Bijarbooneh
Hong Kong University of Science & Technology
Tristan Braud
Hong Kong University of Science & Technology
Pan Hui
University of Helsinki
Hong Kong University of Science & Technology
HKUST-DT System and Media Lab

2. We use this model to build a
navigation application for smart
glasses for both indoors and
outdoors scenarios.
We introduce a Mobile Peripheral Vision (MPV) model which can
be used on smart glasses with a head-mounted display without
any additional hardware requirement & simplifies multi-tasking
when using the glasses.

3. Angular Field of View of Human Eye
30° 60° 90°
central
near-peripheral
paracentral
far
peripheral
far
peripheral
mid-peripheral
Angular Feld of view (AFOV or
AOV) measures the angular
extent of a 360-degree circle that
is visible by the human eye.
The foveal system, responsible for
the foveal vision, lies within the
central and para-central area.
The area outside the foveal
system is responsible for the
peripheral vision.

4. Angular Field of View of Google Glass
The AFOV of Google Glass is
approximately 30 degrees which is
significantly smaller than the AFOV
of the human eye.
This limited FOV forces the user to
direct his central eye gaze towards
the small screen of the glass to
extract meaningful information.
AFOV~30°

5. Google Maps in Smart Glasses
with a head-mounted display

6. Blocked Peripheral Vision
30° 60° 90°
central
near-peripheral
paracentral
far
peripheral
far
peripheral
mid-peripheral

7. Mobile devices also limits cognitive
ability & restricts peripheral vision

8. What is been done so far?
Enhancing the FOV of smart-glasses
Exploring peripheral vision
Navigation on smart-glasses

9. How we differ from existing models?
Mobile Peripheral Vision
(MPV) Model
Near-Eye-Out-Of-Focus
Displays
Software Solution, Light (10 mm * 10mm) &
Simple
Hardware, Large screen, Bulky Setup (60
mm*60mm) & Complex
Mobile users Static users
Variable Luminosity & Backgrounds – both for
indoors and outdoors
Luminosity is fixed
Information is displayed within central & near
peripheral area of the Normal Human Eye
Display is located at Far Peripheral Area

10. Mobile Peripheral Vision
(MPV) Model

11. Contributions
Present an MPV Model using color and motion to display visual cues in
the peripheral vision of the user
Develop a high-fidelity peripheral vision-based navigation application
for both indoor and outdoor environment scenarios
Discuss two specific cases, namely strabismus and color-blindness, for
which our MPV model does not apply

12. Advantages
Software solution
Simplifies multi-tasking
Adaptable for any smart-glasses
with head-mounted display
Use of limited graphical language

13. Combination of 2 Theories
Color Detection in the Peripheral Area of the eye
Motion Detection through Peripheral Vision

14. 2 Types of Cells in the Normal Human Eye
Cones Cells – responsible for the color
Rod Cells – responsible for motion & night detection (at the periphery)

15. Color Sensitivity Zones for the Normal Human Eye
blue, it becomes physiologically impossible to experience
such combinations as reddish green or yellowish blue.
More common among those with color deficiencies are
individuals whose response functions to the photo-
Figure 6. The zones of color sensitivity
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16. Use of 3 Colors to Signal Information to the User
- Highest number of cones in the periphery
- High number of cones in the retina center
- High contrast with the other 2 cones

17. 3 Fundamental Concepts
Motion detection by the peripheral vision using simple shapes
Presence of blue cones at the periphery
Abundance of red and green cones at the center of the retina

18. Motion Detection
Rate threshold or velocity for visual motion perception
increases as we go far from the central gaze

19. Basic Navigation Application using MPV Model
A blue color dot blinks on the left sign to indicate a point of interest on the left
(a). Once the user turns left, the entire screen turns blue to indicate that the user
is in the correct direction (b)

20. Other Applications of MPV
Video games
Outdoor AR games
Notifications

21. MPV based Demo Application
Indoor Application – using public Wi-Fi networks
(of less than -90 dB)
Outdoor Application – using GPS (precision of
at most 5 meters)
Location Detection in the App
WAP 1 WAP 3
WAP 4 WAP 5
WAP 2

22. MPV based Demo Application

23. MPV based Demo Application – User is Notified
to turn Right
Turn Right
The navigation hint bars stimulate peripheral vision through motion. The bars
movement covers the entire 30 deg of angular FOV of Google Glass.

24. MPV based Demo Application – User is Notified
to turn Right
Turn Right
The bars on the head-mounted display occupy 25% of the screen width. Each time the
bar appears, it blinks 4 times for 2 s from left to the right of the screen at a velocity of
15 deg/s.

25. MPV based Demo Application – User is Notified
to turn Right
Turn Right
This ensures that the velocity is far above the peripheral vision motion detection
threshold value of 2.15 deg/s at 90 degrees eccentricity.

26. MPV based Demo Application – User is Notified
to turn Right
Turn Right
Considering that the AFOV of Google Glass is 30 deg and that the bars are moving
at the rate of 15 deg/s, the total periphery stimulation time is 2 s

27. MPV based Demo Application – User is Notified
to turn Right
Turn Left
This ensures that the user receives the hint in a reasonable time to react as the
visual reaction time to rapid movements is at least 250ms.

28. MPV based Demo Application – User is Notified
to turn Right
Turn Left

29. MPV based Demo Application – User is Notified
to turn Right
Turn Left

30. MPV based Demo Application – User is Notified
to turn Right
Turn Left
The cue is activated within a radius of 3 meters from each turn.

31. MPV based Demo Application – 2 Tasks
Primary Task – Looking at Points of Interest
Secondary Task – Navigate through the path

32. Points of Interest (POI) – Primary Task
POI for Outdoor ApplicationPOI for Indoor Application

33. Experimental Methodology
Mental Demand
Frustration Level
Physical Demand
The survey is based on NASA Task Load Index (NASA TLX) assessment tool.
0 - Low
10 - High

34. Experiment Results – Mental Demand of 20 Users

35. Experiment Results – Mental Demand of 20 Users

36. Experiment Results – Physical Demand of 20 Users

37. Experiment Results – Physical Demand of 20 Users

38. Experiment Results – Frustration Level of 20 Users

39. Experiment Results – Frustration Level of 20 Users

40. Experiment Results
Indoor - The probability that the means of the two paired-sample data is
different is at least 84%, 91%, and 74% for the mental demand, physical
demand, and frustration level respectively.
Outdoor - The probability that the means of the two paired-sample data is
different is at least 86%, 92%, and 71% respectively.

41. Experiment Results
The users experience a low mental and physical demand when navigating
using our MPV App.
Users save 50% more time for their main activity using their peripheral
vision with smart glasses.
90% of the users Agree that using their peripheral vision for the navigation
application is more beneficial and efficient than looking into the screen.

42. Special Cases
Color Blindness – Achromatopsia (No Color Detection)
Strabismus - misalignment of the eyes when
looking at an object

43. Future Works
Extend our work and experiment on different scenarios such as
augmented reality and virtual reality games, etc.
Enhance our model with eye tracking features
Expand our panel of users to people suffering from
strabismus and color-blindness to precise the evaluation of
such conditions on our model.

44. Contributions – Repeat!!
Present an MPV Model using color and motion to display visual cues in
the peripheral vision of the user
Develop a high-fidelity peripheral vision-based navigation application
for both indoor and outdoor environment scenarios
Discuss two specific cases, namely strabismus and color-blindness, for
which our MPV model does not apply

45. Acknowledgements
The authors thank the anonymous reviewers for their in-sightful comments.
This research has been supported, inpart, by projects 26211515, 16214817, and
G-HKUST604/16 from the Research Grants Council of Hong Kong, as well as the
5GEAR project from the Academy of Finland ICT 2023 programme.