Description of field of research:

Understanding emotion and automating its recognition is important in diverse domains and applications. Virtual Reality (VR) has become a promising technology for studying emotions due to its ability to generate an immersive and realistic emotional experience in laboratory settings. However, measuring facial expressions with external cameras is challenging due to the large occlusion of the face by the VR headset. Therefore, hardware interfaces that can be integrated into VR headsets: emteqPRO, and PhysioHMD and VR headsets that measure biosignals: emteqGO and HP Reverb G2 Omnicept are being developed. But few are commercially available and have limited verification in emotional studies. Therefore, we aim to study the feasibility of the HP Reverb G2 Omnicept headset to study the relationship between biosignals (heart rate, eye-tracking, facial features) and emotions. The project will be conducted in collaboration with a CSE HDR student who conducts parallel research. These findings can integrate into adaptive human-computer interfaces, game designs, education, and many more to provide a richer user understanding and experience. The project involves: formulating a feasible data collection framework, collecting data, and investigating the efficacy of the HP Reverb G2 Omnicept headset in emotional studies.


Computer Science and Engineering

Research areas

Affective Computing, Virtual Reality, Artificial Intelligence

The successful candidate will work under the supervision of Dr. Gelareh Mohammadi, A/Prof Tomasz Bednarz, and HDR student Rukshani Somarathna. Data collection conducts at the EPICentre, UNSW Art & Design ( and will provide access to the required devices. 

This project involves formulating a research plan to organize and conduct a data collection from participants and investigating the efficacy of the HP Reverb G2 Omnicept headset in emotional studies. Publishing high-quality outcomes can be possible while optional.

  • HP Reverb G2 Omnicept Virtual Reality headset ( 
  • emteqPRO: Face-mounted Mask for Emotion Recognition and Affective Computing (  
  • PhysioHMD: a conformable, modular toolkit for collecting physiological data from head-mounted displays (