Faculty Supervisor

Richard Pyne

Date of Defense

Fall 12-2-2020

Program Name

Honours Bachelor of Computer Science (Mobile Computing)


Applied 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.

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.