Today's technology is becoming more interactive with voice assistants like Siri. However, interactive systems such as Siri make mistakes. The purpose of this thesis is to explore using affect as an implicit feedback channel so that such mistakes would be easily corrected in real time. The CAfFEINE Framework, which was created by Dr. Saha, is a context-aware affective feedback loop in an intelligent environment. For the research described in this thesis, the focus will be on analyzing a user's physiological response to the service provided by an intelligent environment. To test this feedback loop, an experiment was constructed using an on-screen, step-by-step assembly guide for a Tangram puzzle. To categorize the user's response to the experiment, baseline readings were gathered for a user's stressed and non-stressed state. The Paced Stroop Test and two other baseline tests were conducted to gather these two states. The data gathered in the baseline tests was then used to train a support vector machine to predict the user's response to the Tangram experiment.
During the data analysis phase of the research, the results for the predictions on the Tangram experiment were not as expected. Multiple trials of training data for the support vector machine were explored, but the data gathered throughout this research was not enough to draw proper conclusions. More focus was then given to analyzing the pre-processed data of the baseline tests in an attempt to find a factor or group of factors to determine if the user's physiological responses would be useful to train the Support Vector Machine. There were trends found when comparing the area under the curves of the Paced Stroop Test phasic driver plots. It was found that these comparison factors might be a useful approach for differentiating users based upon their physiological responses during the Paced Stroop Test. / Master of Science / The purpose of this thesis was to use the CAfFEINE Framework, proposed by Dr. Saha, in a real-world environment. Dr. Saha's Framework utilizes a user's physical responses, i.e. heart rate, in a smart environment to give information to the smart devices. For example, if Siri were to give a user directions to someone's home and told that user to turn right when the user knew they needed to turn left. That user would have a physical reaction as in their heart rate would increase. If the user were wearing a smart watch, Siri would be able to see the heart rate increase and realize, from past experiences with that user, that the information she gave to the user was incorrect. Then she would be able to correct herself.
My research focused on measuring user reaction to a smart service provided in a real-world situation using a Tangram puzzle as a mock version of an industrial assembly situation. The users were asked to follow on-screen instructions to assemble the Tangram puzzle. Their reactions were recorded through a smart watch and analyzed post-experiment. Based on the results of a Paced Stroop Test they took before the experiment, a computer algorithm would predict their stress levels for each service provided by the step-by-step instruction guide. However, the results did not turn out as expected. Therefore, the rest of the research focused more on why the results did not support Dr. Saha's previous Framework results.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/96609 |
| Date | 29 January 2020 |
| Creators | Ketchum, Devin Kyle |
| Contributors | Electrical and Computer Engineering, Martin, Thomas L., Knapp, R. Benjamin, Gracanin, Denis |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.1127 seconds