Here we introduce the design and preliminary validation of a general-purpose architecture for affective-driven procedural content generation in Virtual Reality (VR) applications in mental health and wellbeing. The architecture supports seven commercial physiological sensing technologies and can be deployed in immersive and non-immersive VR systems. To demonstrate the concept, we developed the "The Emotional Labyrinth", a non-linear scenario in which navigation in a procedurally-generated 3D maze is entirely decided by the user, and whose features are dynamically adapted according to a set of emotional states. During navigation, affective states are dynamically represented through pictures, music, and animated visual metaphors chosen to represent and induce affective states. The underlying hypothesis is that exposing users to multimodal representations of their affective states can create a feedback loop that supports emotional self-awareness and fosters more effective emotional regulation strategies. We carried out a first study to (i) assess the effectiveness of the selected metaphors in inducing target emotions, and (ii) identify relevant psycho-physiological markers of the emotional experience generated by the labyrinth. Results show that the Emotional Labyrinth is overall a pleasant experience in which the proposed procedural content generation can induce distinctive psycho-physiological patterns, generally coherent with the meaning of the metaphors used in the labyrinth design. Further, collected psycho-physiological responses such as electrocardiography, respiration, electrodermal activity, and electromyography are used to generate computational models of users' reported experience. These models enable the future implementation of the closed loop mechanism to adapt the Labyrinth procedurally to the users' affective state.

Toward Emotionally-Adaptive Virtual Reality for Mental Health Applications

Cipresso, Pietro;
2018-01-01

Abstract

Here we introduce the design and preliminary validation of a general-purpose architecture for affective-driven procedural content generation in Virtual Reality (VR) applications in mental health and wellbeing. The architecture supports seven commercial physiological sensing technologies and can be deployed in immersive and non-immersive VR systems. To demonstrate the concept, we developed the "The Emotional Labyrinth", a non-linear scenario in which navigation in a procedurally-generated 3D maze is entirely decided by the user, and whose features are dynamically adapted according to a set of emotional states. During navigation, affective states are dynamically represented through pictures, music, and animated visual metaphors chosen to represent and induce affective states. The underlying hypothesis is that exposing users to multimodal representations of their affective states can create a feedback loop that supports emotional self-awareness and fosters more effective emotional regulation strategies. We carried out a first study to (i) assess the effectiveness of the selected metaphors in inducing target emotions, and (ii) identify relevant psycho-physiological markers of the emotional experience generated by the labyrinth. Results show that the Emotional Labyrinth is overall a pleasant experience in which the proposed procedural content generation can induce distinctive psycho-physiological patterns, generally coherent with the meaning of the metaphors used in the labyrinth design. Further, collected psycho-physiological responses such as electrocardiography, respiration, electrodermal activity, and electromyography are used to generate computational models of users' reported experience. These models enable the future implementation of the closed loop mechanism to adapt the Labyrinth procedurally to the users' affective state.
2018
n/a
1
11
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221020
Biological control systems; Biomedical monitoring; Computer architecture; Emotion regulation; Games; Heart rate; physiological computing; Physiology; physiology-driven VR; procedural content generation; Stress; Biotechnology; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic Engineering; Health Information Management
Bermudez i Badia, Sergi; Velez Quintero, Luis; Cameirao, Monica S.; Chirico, Alice; Triberti, Stefano; Cipresso, Pietro; Gaggioli, Andrea
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1842271
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