Description

Physiological computing is a multidisciplinary research field in HCI wherein the interaction depends on measuring and responding to the user's physiological activity in real-time (Fairclough, 2009). Physiological computing allows for implicit interaction; by monitoring the physiological signals of the user, the computer can infer, e.g., if the task demands are either too challenging or easy, and either adapt the difficulty level or when users are getting distracted from the task, the system could give them a notification. Measuring the psychological state of the user creates intriguing possibilities for Social VR scenarios as we can either adapt the number of displayed avatars, their form or even their proxemic distance. This thesis aims the develop an adaptive Social VR environment designed to support users' performance when engaged in a cognitive task using a measure of physiological state (electrodermal activity: EDA) as input for adaptation. The research consists of three main stages: (1) validation of the psychophysiological inference underpinning the physiological loop (2) implementation of a working VR prototype, and (3) an evaluation of the adaptive environment.

You will

• Perform a literature review
• Modify an existing VR environment
• Implement an online biocybernetic loop using EDA
• Collect and analyze EEG, electrodermal activity (EDA) and electrocardiography (ECG) data
• Summarize your findings in a thesis and present them to an audience
• (Optional) co-writing a research paper

You need

• Strong communication skills in English
• Good knowledge of Unity
• Good knowledge of Python libraries for scientific computing (e.g. Scipy, Neurokit).

References

• Biocybernetics Loops and Physiological Computing
• Adapting task complexity of a Social VR environment based on skin conductance