MIT Department: Media Arts and Sciences
Undergraduate Institution: Wichita State University
Faculty Mentor: Cynthia Breazeal
Research Supervisor: Hae Won Park
My name is Louis Adedapo Gomez and I am senior at Wichita State University originally from Lagos, Nigeria. I study electrical engineering with minors in computer science. In my downtime, I enjoy listening to science and economics-oriented podcasts and expanding my worldview through documentaries. I am passionate about leadership as I serve on executive boards of my schools’ National Society of Black Engineers (NSBE) and IEEE Eta Kappa Nu (IEEE- HKN) chapters My current research interests lie in the application of physiological signals such as EEG waves and Electrodermal Activity signals in the development of health-centered use cases ranging on a spectrum from computer-aided diagnoses to understanding human behavior or emotion through human-centered computing.
2018 Research Abstract
Exploring Electrodermal Activity as a Means to Detecting Engagement in Child-Robot Learning Interactions
Louis Gomez1, Hae Won Park2 and Cynthia Breazeal3
1Department of Electrical Engineering, Wichita State University
2, 3Media Arts and Sciences, Massachusetts Institute of Technology
Social robots such as Tega serve as learning aids in children’s education. A merit Tega provides is the ability to personalize curriculums to fit the current learning capability of the child which contributes to better learning outcomes. Tega uses emotional facial recognition platforms to detect engagement but this method provides only a one-dimensional view of engagement and does not give insight to the child’s cognitive state. Physiological signals such as electrodermal activity (EDA) could be leveraged as a complementary tool to detect engagement because it reflects changes in internal activity such as cognitive load. To understand how EDA signals can be used as inputs in machine learning models to detect engagement, I analyzed data recorded while young children interacted with a Tega during an educational storytelling activity. I applied signal processing techniques such as noise reduction and filtering to clean the EDA data and created a series of visualizations of the data in different storytelling interaction states to enable effective data analysis. To further explore the data, I performed a statistical test such as the student Ttest on a set of conditions to quantify EDA mean values across two independent groups of kids. Then, I used the questions asked during the storytelling sessions and matched them to the recorded raw data to observe changes in the EDA signal. The ability to identify factors that provide a viable method of tracking when children are engaged and creating predictive models based on these identified factors can lead to better learning outcomes in the child to social robot learning scenarios.