VIRTUAL REALITY EXPOSURE THERAPY (VRET)
1.0 INTRODUCTION
Within five months we sought to develop a virtual reality exposure therapy (VRET) environment that is adaptable to the biofeedback of patients with the help of a computational model. Exposure therapy has shown to be the most effective treatment for fear [1], [2] that evokes sympathetic arousal changes in the interbeat interval (IBI) timings of the heart [3], [4]. Most anxieties revolve around a particular situation relying on context-dependent memory, but specific phobias can be more generalized and treated in VR. We believe that our design of triggering and adapting the fearful stimulus individually in VR is an efficient way to treat specific phobias.
5.0 FUNCTIONALITY
Our research follows a multidisciplinary approach, combining fields of design thinking, psychology, neuroscience, and most importantly affective computing with the help of Design Method Toolkit and expert consultations. Our final concept is a user case in an underwater environment focused on a shark phobia. The whole project is a potential solution to a challenge assigned to us by Triple and MediaLAB Amsterdam.
ES G
PH Y
OGICAL CHA L O N SI
E TATI O N
The screen for the therapist so she can adjust the environment according to the patient’s behavior
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SP
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RPR
B E H AVI O R A L
EMOTION
ON
SES
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V ITI
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The generic model of emotion recognition
2.0 RESEARCH QUESTION “How can 15-24-year-old women who have underwater related phobia be helped by a computational model for exposure therapy in a virtual reality environment that adapts to their emotional state?”
The computational model in the prototype must be able to interpret individual biofeedback and adjust the VR environment accordingly.
The VR experience should help the users cope with their phobia/anxiety and not make it worse.
The concept should give feedback to the user about their emotional state. We will use aura (colored lights around the user) as an esthetic feature to enhance the experience.
The concept could benefit from music to enhance the immersive experience. The binaural sounds are applied to calm the user before, during and after the exposure trial.
3.0 TARGET GROUP
We chose women because they are more sensitive to physiological changes [5]. Women are also twice as likely as men to experience anxiety [6].
6.0 DESIGN CRITERIA
COMPUTATIONAL MODEL
OVERALL
GENDER: FEMALE AGE: 15-24
PHOBIA: SHARKS
The prototype of the computational model would be easily transferable to different phobias and environments.
4.0 COMPUTATIONAL MODEL CALIBRATION BASELINE (BPM)
EXPOSURE RANGE
GAZE HR (BPM)
BLUETOOTH ANT+
DATABASE
THERAPIST SCREEN
DATABASE
EXPOSURE THERAPY IN VIRTUAL REALITY
UNITY
VR ENVIRONMENTAL CHANGE
Tracker: Project Owner: Main Researcher: SCREAM Master: Lead Programmer: Team Coach:
Agnetha Mortensen Christiaan van Leeuwen Janina Saarnio Yujie Shan Danny Dorstijn Tamara Pinos Cisneros
7.0 FUTURE WORK The prototype needs to be tested and validated with the people from our target group in a therapeutic session. After multiple user testings, the computational model could be enhanced to adapt more accurately to the individual biofeedback and phobia treatment. The computational model should be built to adapt to the fact that even though heart rate does not necessarily decline across the occasions of exposure trials, the self-reported fear level does [7]. We could highlight the importance of fear toleration instead of a fear reduction as an indicator of successful exposure therapy and take it into account in our future research. A healthy heart is not a metronome but a “mathematical chaos” [8] therefore, heart rate variability (HRV) indicating the changes of time between IBIs might offer a better solution to track the biofeedback instead of the traditional BPMs recording. Unfortunately, the more advanced technology to track the activity of our amygdala (such as infrared thermal imaging) is not available on a global market, nor yet compatible with VR headsets. More accurate measurements will enable for better estimates of the user’s emotions with a cost of a more complex design. To validate the measured arousal level we could design new techniques for self-reporting and track the behavioral responses of the users in VR. The interactive environment also allow us to design for the illusion of competence and self-efficacy of the user that might help to enhance the successful outcome of VRET.
8.0 REFERENCES 1. Ourgin D. (2011). “Efficacy of exposure versus cognitive therapy in anxiety disorders: systematic review and meta-analysis”. BMC Psychiatry, Vol. 11: 200.
5. Kring A., and Gordon A. (1998). “Sex differences in emotion: expression, experience and physiology”. Journal of Personality and Social Psychology, Vol. 74(3)
2. Lang A. and Craske M. (2000). “Manipulations of exposure-based therapy to reduce return of fear: A replication”. Behaviour Research & Therapy, Vol. 38(1): 1-12.
6. Remes O., Brayne C., Linde van der R., Lafortune L. (2016). “A systematic review of reviews on the prevalence of anxiety disorders in adult populations”. Brain and Behavior, Vol. 6(7)
3. Peperkorn H., Diemer J. and Muhlberger A. (2015). “Temporal dynamics in the relation between presence and fear in virtual reality”. Computers in Human Behavior, Vol. 48: 542-547.
7. Craske M., Kricanski K., Zelikowsky M., Mystkowski J., Chowdhury N. and Baker A. (2008). “Optimizing inhibitory learning during exposure therapy”. Behaviour Research and Therapy, Vol. 46: 5-27
4. Ekman P. and Davidson R. (1994). The nature of emotion. Oxford University Press.
8. Shaffer F. and Ginsberg J. (2017). “An overview of heart rate v ariability metrics and norms”. Frontiers in Public Health, Vol. 5