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<!DOCTYPE html>
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<title>Human-Machine Interaction @ IIIT-D</title>
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<h3 class="text-black h1 site-section-heading">Attention evalution using Eye tracking and machine learning of ADHD students</h3>
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<p class="text-primary lead" > <strong>Abstract</strong></p>
<p class="lead" style="text-align:justify;">Attention Deficit hyperactivity disorder (ADHD) is a medical condition portraying symptoms of aggression, hyperactivity, and an inattention. It prevails among 7.8% of Children all over the world. This project aims to measure the engagement of children with ADHD. In this study, the bio-signal parameters of an individual are recorded in an online one to one tutorial session with an educator. Previous studies have established modalities like EEG, Eye-tracking, GSR have shown significant results in investigating and monitoring ADHD related experiments.EEG research has attempted to characterize and quantify the neurophysiology of attention-deficit/hyperactivity disorder (ADHD), most consistently associating it with increased frontocentral theta band activity and increased theta to beta (θ/β) power ratio during rest compared to non-ADHD controls. Past studies have demonstrated EEG brainwave feedback as an efficient method for assessing attention. We are using EEG Emotiv EPOC+ for measuring the brain activity by 14 electrodes. The EEG device provides the data in the form of motion, performance data including stress, anxiety, signal strength, and contact quality of 14 electrodes. Eye movements can be informative of the underlying mechanisms of complex disorders like ADHD. Eye-tracking provides researchers with many benefits, often without requiring deep expertise to implement. It is a well-established method for measuring visual attention, cognitive “load,” goal pursuit, and implicit preferences. Variations in eye movements, including speed of movement, duration of fixations, patterns and frequency of blinks, and patterns of visual searching behavior, are all relevant to how a person is responding to a visual stimulus. We are using the Tobii 4c eye-tracker, which operates at a sampling frequency of 90 Hz. The third modality is the Galvanic Skin Response (GSR), which estimates the skin conductivity data, gyroscope data, accelerometer data, and PPG data. Shimmer 3 is the device used for GSR data collection and evaluation. The first phase involves data collection with synchronization, for which we have designed a protocol of 55 minutes comprising of a couple of 20 minutes sessions with the educator both preceded and followed by a 5 minutes relaxation session. The focus is to combine multiple modalities mainly for better evaluation of engagement using statistical machine learning and deep learning techniques.</p>
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<p class="text-primary lead inline" class="inline"> <strong>Student Collaborator</strong></p> <p class="lead inline">: <a href="team.html#harshit-chauhan">Harshit Chauhan (MSIT Delhi)</a>, Prachi Arora (IIIT Delhi), <a href="team.html#satvika-anand">Satvika Anand (IIIT Delhi)</a>, <a href="team.html#pramil-panjawani">Pramil Panjawani (IIIT Delhi)</a></p></div>
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<!-- <p class="text-primary lead inline" class="inline"> <strong>Faculty Collaborator</strong></p> <p class="lead inline">: Pramil Panjawani (IIIT Delhi)</p> -->
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<!-- <p class="text-primary lead inline" class="inline"> <strong>External Collaborator</strong></p> <p class="lead inline">: Satvika Anand (IIIT Delhi)</p> -->
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<!-- <p class="text-primary lead" > <strong>Publication</strong></p>
<p class="lead" style="text-align:justify;">Kumar Singh, D., Sharma, S., Shukla, J., & Eden, G. (2020, March).<a href="https://dl.acm.org/doi/abs/10.1145/3371382.3378315"><strong> <i> Toy, Tutor, Peer, or Pet? Preliminary Findings from Child-Robot Interactions in a Community School.</i></strong></a> In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 325-327).<a href="https://www.youtube.com/watch?v=hMyGznAFYMk"><i><strong>[Video]</strong></i></a></p>
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<p style="text-align: justify;">At Human Machine Interaction (HMI) lab, we investigate novel interaction mechanisms to empower human-machine cooperation. Our research is supported by the Intramural Start-up Grant from the <a href="https://www.iiitd.ac.in/">Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)</a>,<a href="https://cai.iiitd.ac.in/"> Infosys Center for Artificial Intelligence</a>, and <a href="https://cdnm.iiitd.ac.in/">TCS – Centre for Design and New Media.</p>
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