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Uploaded byGavindya Jayawardena
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Eye Tracking for Predicting ADHD

Gavindya Jayawardena, a PhD student at Old Dominion University, is researching eye tracking technology to predict ADHD by analyzing eye gaze metrics. The study involves comparing eye movement data from individuals diagnosed with ADHD to those without, using machine learning techniques to identify patterns related to working memory capacity. Results indicate significant differences in eye tracking features between the two groups, with high accuracy achieved in classification tasks related to attention levels.

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Eye Tracking for Predicting ADHD Gavindya Jayawardena PhD Student Neuro Information Retrieval and Data Science Lab Department of Computer Science Old Dominion University @Gavindya2, @WebSciDL Eye Tracker use in Computer Science Research Work
Overview ● Eye Tracking ● Eye Movements ● Eye Tracker Demonstration ● Research Work ○ Predicting ADHD using Eye Gaze Metrics ○ Results ○ Publications ● WEKA Demonstration 2Eye Tracker use in Computer Science Research Work
About Me ● 1st Year PhD Student @ODU ● GPA - 4.0/4.0 ● Joined ODU Spring 2019 ● Dominion Scholar ● Research Assistant @ODUCS @WSDL ● http://www.cs.odu.edu/~gavindya 3Eye Tracker use in Computer Science Research Work ● BSc Degree @UOM, Sri Lanka ● GPA - 3.85/4.20 ● Originally from Sri Lanka
My Research Area ● Research in Eye tracking for targeted population - Current work: Predicting ADHD using Eye Tracking 4Eye Tracker use in Computer Science Research Work
What is Eye Tracking? ● Measuring the gaze point (where one is looking) 5Eye Tracker use in Computer Science Research Work Source- https://help.tobii.com/hc/en-us/articles/115003295025 -Eye-tracking-in-gaming-how-does-it-work- ● Measuring the motion of an eye Source - Eye-Tracking-Krankenkassen-Vergleich-Google- Search-Heat-Map.jpg
Human Eye 6Eye Tracker use in Computer Science Research Work
Basic Elements of Eye Movement 7Eye Tracker use in Computer Science Research Work • Fixations • High acuity vision • Eye is stable in regard to the object of interest • Saccades • No vision • Move eyes between eye fixations very rapidly • Smooth pursuits • Eyes follow an object
Why is Eye Tracking Important? ● We move eyes to bring a particular portion of the visible field of view into high resolution ● We divert our attention to that point so that we can concentrate ● Eye movements follow along the path of attention 8Eye Tracker use in Computer Science Research Work
Eye Trackers ● A device for measuring eye positions and eye movement. 9Eye Tracker use in Computer Science Research Work Tobii 4C 90 Hz $169.00 PupilLabs 200 Hz $2740.00 Gazepoint 150 Hz $1,995.00
Eye Tracking Applications 10Eye Tracker use in Computer Science Research Work Gaming
Eye Tracking Applications 11Eye Tracker use in Computer Science Research Work Market ResearchUsability Research Source - https://imotions.com/blog/top-8-applications-eye-tracking-research/
Eye Tracking Applications 12Eye Tracker use in Computer Science Research Work Medical Research Source - https://www.tobiipro.com/imagevault/publishedmedia/t2pps4lriuxsx2jv8sn2/TobiiPro-Spectrum-FoU-banners_BiometricEEG-3_1.jpg
Demo of Eye Trackers 13 Eye Tracker use in Computer Science Research Work
Tobii 4C Eye Tracker ● Used for - Gaming ● Sampling Rate - 90 Hz ● Price - $169 in Amazon 14Eye Tracker use in Computer Science Research Work
PupilLabs Core Eye Tracker ● Used for - Research ● Sampling Rate ○ World camera: 120 Hz ○ Eye cameras: 200 Hz ● Price - $2740 15Eye Tracker use in Computer Science Research Work Source - https://pupil-labs.com/products/core/configure/
What Do We Do @NIRDS Lab? ● What do we have? ○ Eye Trackers ○ Wearable EEG Caps ○ Empatica E4 ● What do we really do? ○ Study Eye Tracking Data of ADHD and Non-ADHD adults ○ Study EEG Signals of Autistic and healthy kids ● What kind of Research? ○ Predict a diagnosis of ADHD using Eye Tracking Data ○ Analyze relationship of brain activities and ASD using EEG 16Eye Tracker use in Computer Science Research Work Emotiv EEG Empatica E4
Predicting ADHD using Eye Gaze Metrics Indexing Working Memory Capacity In Collaboration with Dr. Anne Michalek from the Department of Communication Disorders & Special Education, Old Dominion University 17 Eye Tracker use in Computer Science Research Work Eye Tracking Research
Attention-Deficit/Hyperactivity Disorder (ADHD) ● Three distinct behavioral symptoms ○ Difficulty in paying attention ○ Difficulty in controlling impulsive behaviours ○ Being overactive ● Researchers have recognized that ADHD persists through adulthood, with an estimation of 10.2% in 2016 in the U.S. 18Eye Tracker use in Computer Science Research Work
Eye Tracker Specification ● Tobii Pro X2-60 computer screen-based eye tracker ● Tobii Studio analysis software ● Sampling rate - 60 Hz (approximately once every 16.23 milliseconds) ● Each participant was calibrated using Tobii's standard calibration methods 19Eye Tracker use in Computer Science Research Work Tobii X2-60 Eye Tracker Tobii Studio analysis software
Participants ● Adult participants: ○ 7 participants diagnosed with ADHD by medical practitioners and confirmed through verified documentation (6 F, 1 M) ○ 7 participants without a diagnosis (4 F, 3 M) ● All of the participants fulfilled the following inclusion criteria: ○ Between 18 and 65 years of age ○ Spoke English as their first language ○ No vision impairments ○ No history of psychotic symptoms ○ No documented learning / reading disabilities 20Eye Tracker use in Computer Science Research Work
The Experiment ● Purpose ○ To differentiate between ADHD and Non-ADHD ● Hypothesis ○ There is a significant difference in attention level between ADHD and Non-ADHD subjects ● Measured Using ○ Working Memory Capacity (WMC) 21Eye Tracker use in Computer Science Research Work
Working Memory Capacity (WMC) 22Eye Tracker use in Computer Science Research Work ● Hold and manipulate information simultaneously ● Greater WMC ⇒ ■ More attention ■ Less distraction
ADHD and WMC ● Researchers have shown that adults with ADHD have reduced WMC compared to their peers. ● Differences in WMC is because of differences in attention control ● Differences in WMC affects performance during cognitively demanding tasks ○ Example - In Education ■ Kids, young adults, and adults suffer because of the bad grades ■ Can we use Eye Tracking to identify people in classroom setting? 23Eye Tracker use in Computer Science Research Work
Paul is afraid of heights and refuses to fly on a plane. ? R Whenever I drink the newspaper, I always get depressed. ? M How do we measure WMC? Reading Span (RSPAN) task ● Read a sentence and tell whether it makes sense (yes/no) ● Remember the letter at the end of the sentence ● Recall all the letters they can remember from sentences ● Based on the performance of RSPAN task, the WMC score is calculated for each participant 24Eye Tracker use in Computer Science Research Work
Research Interests ★ Using RSPAN task as the context of study, Identify ADHD and Non-ADHD 1. Using WMC scores No significant difference seen in WMC scores of ADHD and Non-ADHD 2. Using Machine Learning on Eye Tracking Data We created 3 Feature sets which includes saccades and fixations 25Eye Tracker use in Computer Science Research Work
Comparison of Eye Fixations for ADHD and Non- ADHD Participant 26Eye Tracker use in Computer Science Predicting ADHD using Eye Gaze Metrics Screen Capture of Tobii Studio Analysis Software during WMC Task as Generated during the Replay Mode ADHD Participant Non-ADHD Participant
Research Interests (contd…) 3. Using Eye Tracking data within Areas Of Interest (AOIs) based on sentences ○ We created a Feature set which includes saccades and fixations within the AOIs of all the sentences ○ Areas Of Interest (AOIs): ■ AOI 1 - Stimulus (the whole sentence) ■ AOI 2 - Critical word (critical word when determining the coherency of the sentence) ■ AOI 3 - Determiner (the decision point with the letter to be remembered) 27Eye Tracker use in Computer Science Research Work
Machine Learning and Features ● Machine learning (ML) = Development of computer programs that can access data and learn patterns or classifications without explicitly programming ● ML need a dataset which consist of different feature values ● Feature = An attribute being observed ○ Fixation Duration = 100 milliseconds ○ Saccade Duration = 10 milliseconds ● Feature set = A set of all the attributes that you're interested in ○ Eg: Fixation Duration & Saccade Duration 28Eye Tracker use in Computer Science Research Work
Eye Gaze Metrics Feature Set Fixation features ● Number of fixations ● Fixation Duration (ms) ● Average Fixation duration (ms) ● Pupil diameter of left eye (mm) ● Pupil diameter of right eye (mm) 29Eye Tracker use in Computer Science Research Work Normal Pupils vs. Dilated Pupils Source - https://www.allaboutvision.com/en-in/conditions/dilated-pupils/
Eye Gaze Metrics Feature Set Saccade features ● Saccade Duration ● Saccade Amplitude ● Saccade Peak velocities 30Eye Tracker use in Computer Science Research Work
Sample Eye Gaze Metrics Feature Set 31Eye Tracker use in Computer Science Research Work Part of Fixation Feature Set
What do we use for Machine Learning? 32Eye Tracker use in Computer Science Research Work
Evaluation of Results ● We chose standard information retrieval evaluation measures ○ Precision ○ Recall ○ Accuracy 33Eye Tracker use in Computer Science Research Work
Results (I) Classify ADHD and Non-ADHD subjects using Machine Learning on Eye Tracking data 34Eye Tracker use in Computer Science Research Work Feature Set Accuracy Fixation Features 78.48% Saccade Features 91.14% Both Fixation and Saccade Features 91.11%
Results (II) Classify ADHD and Non-ADHD subjects using Machine Learning on Eye Tracking data within Areas Of Interest (AOIs) based on sentences 35Eye Tracker use in Computer Science Research Work Feature Set Accuracy Sentence-based Feature set 86.20%
Publications ● 1 Book Chapter ○ Anne M. P. Michalek, *Gavindya Jayawardena, and Sampath Jayarathna. "Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity", Computational Models for Biomedical Reasoning and Problem Solving, IGI Global, pp. 66-88. 2019 ● 1 Conference Paper ○ *Gavindya Jayawardena, Anne M. P. Michalek, and Sampath Jayarathna. "Eye Tracking Area of Interest in the Context of Working Memory Capacity Tasks", In Proceedings of the 2014 IEEE 20th International Conference on Information Reuse and Integration (IEEE IRI 2019) (In Press) ● 1 Tech report ○ Eye Gaze Metrics and Analysis of AOI for Indexing Working Memory towards Predicting ADHD - https://arxiv.org/abs/1906.07183 ● 1 Blog Post ○ Use of Cognitive Memory to Improve the Accessibility of Digital Collections - https://ws- dl.blogspot.com/2019/06/2019-06-19-use-of-cognitive-memory-to.html 36Eye Tracker use in Computer Science Research Work
WEKA ● Is a collection of machine learning algorithms for data mining tasks ● Contains tools for data classification ● Facilitates to visualize how different algorithms perform for the same data set 37Eye Tracker use in Computer Science Research Work
Demo of Weka Knowledge Flow 38Eye Tracker use in Computer Science Research Work
Questions? 39Eye Tracker use in Computer Science Research Work Gavindya Jayawardena Twitter Handle - @Gavindya2 Email - hjaya001@odu.edu
Thank You! 40Eye Tracker use in Computer Science Research Work