EyeGrip: Detecting Targets in a Series of Uni-directional Moving Objects Using Optokinetic Nystagmus Eye Movements
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The EyeGrip project at the University of Copenhagen aims to detect targets among uni-directionally moving images by monitoring optokinetic nystagmus eye movements. The system tracks user attention through eye movements to identify images of interest and has been tested under various scrolling conditions with participants. Results indicate that EyeGrip is effective in detecting moving objects, suggesting potential applications in user interface design and data interaction.
![IT UNIVERSITY OF COPENHAGEN
RELATED WORK
• Pursuit [1]: is a calibration-free
technique to detect limited number
of moving objects on the screen
using smooth pursuit eye
movements.
• EyeGrip detects unlimited number
of unidirectional moving objects.
[1] M´elodie Vidal, Andreas Bulling, and Hans Gellersen. 2013. Pursuits: Spontaneous
Interaction with Displays Based on Smooth Pursuit Eye Movement and Moving Targets. In
Proceedings of UbiComp ’13. ACM, 439–448.](https://image.slidesharecdn.com/eyegrip-slides-170409145101/85/EyeGrip-Detecting-Targets-in-a-Series-of-Uni-directional-Moving-Objects-Using-Optokinetic-Nystagmus-Eye-Movements-19-320.jpg)
EyeGrip: Detecting Targets in a Series of Uni-directional Moving Objects Using Optokinetic Nystagmus Eye Movements
- 1. IT UNIVERSITY OF COPENHAGEN EyeGrip: Detecting Targets in a Series of Uni-directional Moving Objects Using Optokinetic Nystagmus Eye Movements Shahram Jalaliniya - Diako Mardanbegi IT University of Copenhagen Pervasive Interaction Technology Lab
- 2. IT UNIVERSITY OF COPENHAGEN MOTIVATION • Information age & overwhelming users with data • Data is getting more visual (e.g. Web, Facebook) • Scrolling among visual data is becoming more popular
- 3. IT UNIVERSITY OF COPENHAGEN • Scrolling includes: - Scrolling - Stopping the page - Bringing the desired content back (not always an easy task)
- 4. IT UNIVERSITY OF COPENHAGEN EYEGRIP EyeGrip automatically detects moving images that seem to be interesting for a user among other scrolling images by monitoring and analyzing user’s eye movements.
- 5. IT UNIVERSITY OF COPENHAGEN EYEGRIP WORKS BASED ON OKN EYE MOVEMENTS • OKN is an eye movement that tend to track the motion of one element at a time in a set of unidirectional moving stimuli • OKN: Optokinetic nystagmus is a combination of saccadic and smooth pursuit eye movements.
- 6. IT UNIVERSITY OF COPENHAGEN HOW DOES EYEGRIP WORK? When one of the images grabs our attention we follow that image for a longer time that creates a peak in the OKN signal. 180$ 200$ 220$ 240$ 260$ 280$ 300$ 320$ 340$ 360$ 380$ 6000$ 7500$ 9000$ 10500$12000$13500$15000$16500$18000$19500$21000$22500$24000$25500$27000$28 Original$data$ 180$ 200$ 220$ 240$ 260$ 280$ 300$ 320$ 340$ 360$ 380$ 6000$ 7500$ 9000$ 10500$ 12000$13500$ 15000$ 16500$ 18000$19500$21 Original$data$
- 7. IT UNIVERSITY OF COPENHAGEN EXPERIMENT GOAL Testing the feasibility of EyeGrip in different scrolling conditions: - Different speeds - Maximum number of visible images on the screen (manipulated by changing image width)
- 8. IT UNIVERSITY OF COPENHAGEN EXPERIMENTAL DESIGN (3 × 2 ) • 20 participants • 3 speeds 26.5, 37.5, and 49 °⁄sec • 2 image widths 18°( 𝑊 𝑖𝑚𝑎𝑔𝑒 𝑊 𝑑𝑖𝑠𝑝𝑙𝑎𝑦 = 0.6) and 9°( 𝑊 𝑖𝑚𝑎𝑔𝑒 𝑊 𝑑𝑖𝑠𝑝𝑙𝑎𝑦 = 0.3)
- 9. IT UNIVERSITY OF COPENHAGEN APPARATUS • Head-mounted eye tracker with the Haytham open source gaze tracker (20 Hz sampling rate) • Laptop to display the scrolling images & collect eye data 34.5 cm 19.5cm Small width condi ons: 1, 3,and 5 (a) 34.5 cm 19.5cm Big width condi on: 2, 4, and 6 (b)
- 10. IT UNIVERSITY OF COPENHAGEN EXPERIMENT TASK • Visual search among faces: Participants should press space key as soon as they see Bill Clinton’s picture among other faces • Participants repeated the task for all 6 conditions • 40 random images of famous people is displayed in each condition where 7 was Bill Clinton’s photos
- 11. USER ERROR The error rate: total missing target images by participants divided by total number of targets Precision(NoEvent)" n"6" Window=10" Window=16" Window=20" Window=30" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" Condi<on" 1" Condi<on" 2" Condi<on" 3" Condi<on" 4" Condi<on" 5" Condi<on" 6" Error$rate$(%)$ (b)$ Usererrorrate(%) Cond. 1 2 3 4 5 6 Speed slow slow med med fast fast Image width small big small big small big
- 12. IT UNIVERSITY OF COPENHAGEN DATA ANALYSIS • Cleaning data: Removing 5 participants with less than 75% data • Normalization: finding left & right eye coordinates during the experiment by displaying 2 red circles a the beginning of each task. We used these coordinates to bring all the data in the same range (min-max normalization) • Aggregation: we aggregated the data from all 15 participants • Labeling data: we used the space key to label the collected data
- 13. IT UNIVERSITY OF COPENHAGEN EVENT DETECTION ALGORITHM • We used the default setting for the Multilayer perceptron algorithm in the WEKA with a single hidden layer • Horizontal coordinate of the pupil center was the only feature • Sliding window is selected based on maximum performance - 30 frames for conditions 1,2,4 - 20 frames for conditions 3,6 - 16 frames for condition 5
- 14. EFFECT OF SPEED & MAX NO. OF IMAGES • 10 cross-fold validation is used • Repeated measure ANOVA - Significant effect of image width F(1,14) = 34.9, p < :0001 - No significant effect for speed - But higher speed caused more error (missing targets) 0" 10" 20" 30" 40" 50" 60" 70" 80" 90" 100" Small"width" Big"width" Small"width" Big"width" Small"width" Big"width" Slow" Medium"speed" Fast" Accuracy'of'the'classifica1on'(%)' (a)' 70" 75" 80" 85" 90" 95" 100" Slow" Medium" speed" Fast" Accuracy'of'the'classifica1on(%)' (b)' Small"image"width" Big"image"width" 70 75 80 85 90 95 100 Accuracy'of'the'classifica1on'(%)' 0" 10" 20" 30" 40" 50" 60" 70" 80" 90" 100" Small"width" Big"width" Small"width" Big"width" Small"width" Big"width" Slow" Medium"speed" Fast" Accuracy'of'the'classifica1on'(%)' (a)' 70" 75" 80" 85" 90" 95" 100" Slow" Medium" speed" Fast" Accuracy'of'the'classifica1on(%)' (b)' Small"image"width" Big"image"width" " " " " " " " " " " " Small"width" Big"width" Small"width" Big"width" Small"width" Big"width" Slow" Medium"speed" Fast" (a)' 70" 75" 80" 85" 90" 95" 100" Slow" Medium" speed" Fast" Accuracy'of'the'classifica1on(%)' (b)' Small"image"width" Big"image"width" 70" 75" 80" 85" 90" 95" 100" Small"image" width" Big"image"width" Accuracy'of'the'classifica1on'(%)' (c)' Slow" Medium"speed" Fast"
- 15. IT UNIVERSITY OF COPENHAGEN DESIGN GUIDELINES • Images should move in a one direction at a certain speed • There should be a balance between moving speed & max number of images • The visual search task should not be very complex otherwise all images will draw equally high attention that increases falls positives
- 16. IT UNIVERSITY OF COPENHAGEN STUDY 1: A PICTURE SELECTION SYSTEM • 8 participants • Speed: 37 °⁄sec • Image width: 18 ° • Selecting Clinton pictures 0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# 80%# 90%# 100%# Accuracy# Precision# Recall# 1" 2" 3" 4" 5" Mental' Demand' Physical' Demand' Temporal' Demand' Performance' Effor t' Frustra: on' 5'lickert'scale'
- 17. IT UNIVERSITY OF COPENHAGEN STUDY 2: MIND READING GAME • 10 participants • Select 1 of 4 characters • Participants are asked to count repetitions of selected person • Accuracy:100%
- 18. IT UNIVERSITY OF COPENHAGEN OTHER SUGGESTED APPLICATIONS • Interaction with scrolling menus (e.g. scrolling cards on Google Glass) • EyeGrip for browsing Facebook page • Advertisement on public displays • Text reading assistant for small displays (slowing down text when a user has problem with a word) • Assistant for visual inspection in production lines (automatically detecting unqualified products)
- 19. IT UNIVERSITY OF COPENHAGEN RELATED WORK • Pursuit [1]: is a calibration-free technique to detect limited number of moving objects on the screen using smooth pursuit eye movements. • EyeGrip detects unlimited number of unidirectional moving objects. [1] M´elodie Vidal, Andreas Bulling, and Hans Gellersen. 2013. Pursuits: Spontaneous Interaction with Displays Based on Smooth Pursuit Eye Movement and Moving Targets. In Proceedings of UbiComp ’13. ACM, 439–448.
- 20. IT UNIVERSITY OF COPENHAGEN CONCLUSIONS • EyeGrip is a calibration-free and implicit eye interaction technique to select an object among other moving unidirectional objects (top-down attention) • EyeGrip can be used in gaze-contingent user interfaces to detect what draws users attention (bottom-up attention) • Simpler algorithms (e.g. threshold-based method) can be applied to detect the event in EyeGrip
- 21. IT UNIVERSITY OF COPENHAGEN