With the growth of wearable devices capable of measuring physiological signals, affective computing is becoming more popular than before that gradually will remove our cognitive approach. One of the physiological signals is the electrodermal activities (EDA) signal. We explore how video stimulus that might arouse fear affect the EDA signal. To better understand EDA signal, two different medians, a scene from a movie and a scene from a video game, were selected to arouse fear.
We conducted a user study with 20 participants and analyzed the differences between medians and proposed a method capable of detecting the highlights of the stimulus using only EDA signals. The study results show that there are no significant differences between two medians except that users are more engaged with the content of the video game. From gathered data, we propose a similarity measurement method for clustering different users based on how common they reacted to different highlights. The result shows for 300 seconds stimulus, using a window size of 10 seconds, our approach for detecting highlights of the stimulus has the precision of one for both medians, and F1 score of 0.85 and 0.84 for movie and video game respectively. / Master of Science / In this work, we explore different approaches to analyze and cluster EDA signals. Two different medians, a scene from a movie and a scene from a video game, were selected to arouse fear.
By conducting a user study with 20 participants, we analyzed the differences between two medians and proposed a method capable of detecting highlights of the video clip using only EDA signals. The result of the study, shows there are no significant differences between two medians except that users are more engaged to the content of the video game. From gathered data, we propose a similarity measurement method for clustering different user based on how common they reacted to different highlights.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/84509 |
| Date | 06 August 2018 |
| Creators | Tasooji, Reza |
| Contributors | Computer Science, Knapp, R. Benjamin, Gracanin, Denis, Martin, Thomas L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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