Date of Defense
Honours Bachelor of Computer Science (Mobile Computing)
machine learning, acrophobia, virtual reality
Faculty of Applied Science & Technology (FAST)
Using Machine Learning to Regulate Intensity of Immersion Therapy Treatment of Phobias Through Vital Feedback
The treatment of acrophobia has been trying to keep up with newer technology with the incorporation of virtual reality for exposure therapy, but that approach still lacks automation and still leaves a good portion for human error. The proposed method introduced in this paper is that a machine learning model could replace the need for continuous human intervention. With a few different models of bridges and buildings and the ability for a machine learning model to dynamically alter the height of these building we could theoretically put the patient in the exact situation that will maximize the efficiency of their treatment. The proposed solution will utilize a random forest classifier and with continuous access to the patient’s heart rate, blood pressure and galvanic skin response it can translate that information to fear levels. Using a second deep neural network it can determine what kind of environment will be most effective in treating the patient.
© Mark Beauchamp
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Beauchamp, Mark, "Using Machine Learning to Regulate Intensity of Immersion Therapy Treatment of Phobias Through Vital Feedback" (2020). Student Theses. 4.