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- All Subjects: Facial Emotion Recognition
- Creators: Bryan, Chris
Description
While significant qualitative, user study-focused research has been done on augmented reality, relatively few studies have been conducted on multiple, co-located synchronously collaborating users in augmented reality. Recognizing the need for more collaborative user studies in augmented reality and the value such studies present, a user study is conducted of collaborative decision-making in augmented reality to investigate the following research question: “Does presenting data visualizations in augmented reality influence the collaborative decision-making behaviors of a team?” This user study evaluates how viewing data visualizations with augmented reality headsets impacts collaboration in small teams compared to viewing together on a single 2D desktop monitor as a baseline. Teams of two participants performed closed and open-ended evaluation tasks to collaboratively analyze data visualized in both augmented reality and on a desktop monitor. Multiple means of collecting and analyzing data were employed to develop a well-rounded context for results and conclusions, including software logging of participant interactions, qualitative analysis of video recordings of participant sessions, and pre- and post-study participant questionnaires. The results indicate that augmented reality doesn’t significantly change the quantity of team member communication but does impact the means and strategies participants use to collaborate.
ContributorsKintscher, Michael (Author) / Bryan, Chris (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Hansford, Dianne (Committee member) / Johnson, Erik (Committee member) / Arizona State University (Publisher)
Created2020
Description
Augmented Reality (AR) has progressively demonstrated its helpfulness for novicesto learn highly complex and abstract concepts by visualizing details in an immersive
environment. However, some studies show that similar results could also be obtained
in environments that do not involve AR. To explore the potential of AR in advancing
transformative engagement in education, I propose modeling facial expressions
as implicit feedback when one is being immersed in the environment. I developed a
Unity application to record and log the users' application operations and facial images.
A neural network-based model, Visual Geometry Group 19 (VGG19, Simonyan
and Zisserman (2014)), is adopted to recognize emotions from the captured facial
images. A within-subject user study was designed and conducted to assess the sentiment
and user engagement differences in AR and non-AR tasks. To analyze the
collected data, Dynamic Time Warping (DTW) was applied to identify the emotional
similarities between AR and non-AR environments. The results indicate that users
showed an increase in emotion patterns and application operations throughout the
AR tasks in comparison to non-AR tasks. The emotion patterns observed in the
analysis show that non-AR provides less implicit feedback compared to AR tasks.
The DTW analysis reveals that users' emotion change patterns appear to be more
distant from neutral emotions in AR than non-AR tasks. Succinctly put, the users
in the AR task demonstrated more active use of the application and yielded ranges
of emotions while operating it.
ContributorsPapakannu, Kushal Reddy (Author) / Hsiao, Ihan (Thesis advisor) / Bryan, Chris (Committee member) / Glenberg, Mina Johnson (Committee member) / Arizona State University (Publisher)
Created2021