Abstract

This paper presents a human-centered methodology for designing and developing Virtual Reality Exposure Therapy (VRET) systems. By following the steps proposed by the methodology – Users analysis, Domain Analysis, Task Analysis and Representational Analysis, we developed a system for acrophobia therapy composed of 9 functional, interrelated modules which are responsible for patients, scenes, audio and graphics management, as well as with physiological monitoring and event triggering. The therapist visualizes in real time the patient’s biophysical signals and adapts the exposure scenario accordingly, as. he can lower or increase the level of exposure. There are 3 scenes in the game, depicting a ride by cable car, one by ski lift and a walk by foot in a mountain landscape. A reward system is implemented and emotion dimension ratings are collected at predefined points in the scenario. They will be stored and later used for constructing an automatic machine learning emotion recognition and exposure adaptation module

Recommended Citation

Bălan, O., Cristea, Ș., Moldoveanu, A., Moise, G., Leordeanu, M. & Moldoveanu, F. (2019). Towards a Human-Centered Approach for VRET Systems: Case Study for Acrophobia. In A. Siarheyeva, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Information Systems Beyond 2020 (ISD2019 Proceedings). Toulon, France: ISEN Yncréa Méditerranée.

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Towards a Human-Centered Approach for VRET Systems: Case Study for Acrophobia

This paper presents a human-centered methodology for designing and developing Virtual Reality Exposure Therapy (VRET) systems. By following the steps proposed by the methodology – Users analysis, Domain Analysis, Task Analysis and Representational Analysis, we developed a system for acrophobia therapy composed of 9 functional, interrelated modules which are responsible for patients, scenes, audio and graphics management, as well as with physiological monitoring and event triggering. The therapist visualizes in real time the patient’s biophysical signals and adapts the exposure scenario accordingly, as. he can lower or increase the level of exposure. There are 3 scenes in the game, depicting a ride by cable car, one by ski lift and a walk by foot in a mountain landscape. A reward system is implemented and emotion dimension ratings are collected at predefined points in the scenario. They will be stored and later used for constructing an automatic machine learning emotion recognition and exposure adaptation module