Dr Soumya Barathi

Publication

• Date
• Type

Research

My research publications: 

Emotionally Challenging Games Can Satisfy Older Adults' Psychological Needs: From Empirical Study to Design Guidelines;  CHI 2025: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

Older adults often struggle to meet their psychological needs due to retirement and living alone. Recent studies suggest that games featuring emotional challenge (EC) can help fulfill basic psychological needs such as autonomy, competence, and relatedness by facilitating emotional exploration. However, it remains unclear whether older adults can benefit from EC games, whether they find this genre enjoyable, and how these games should be designed to better meet their needs. This work explores older adults’ experiences and perceptions of playing EC games through two studies. The first study involved playing Detroit: Become Human, revealing that older adults derived multifaceted psychological experiences from playing the game. The second study involved a custom-designed game scenario tailored to older adults, demonstrating that meaningful choices significantly influenced autonomy need satisfaction. Based on these findings, this paper offers five design guidelines for developing EC games that satisfy psychological needs of older adults.

https://dl.acm.org/doi/full/10.1145/3706598.3713899 

Advantages of friend-modelled social interactive feedforward for VR exergaming. Presented at: CHI PLAY 2024, Tampere, Finland, 14-17 October 2024, Vol. 8., (10.1145/3677103)

This paper presents a novel VR exergaming method called social interactive feedforward. The player competes with an 'enhanced model' of one of their friends in a real-time VR environment, showing improved performance levels in a way the player can relate to. Social interactive feedforward was tested in a cycling-based VR exergame and players competed with enhanced models of themselves, their friend, and a stranger moving at the same enhanced pace. Results show that friend-modelled social interactive feedforward improves performance and intrinsic motivation the most. This indicates that the mere association of the enhanced model with their friend results in a rapid improvement in performance and motivation which implies that social feedforward was successfully elicited by using an enhanced friend's model. This widens the application of self-modelled feedforward to a wide range of social options which enables players to also reap the benefits of socialising in addition to feedforward benefits.

https://dl.acm.org/doi/abs/10.1145/3677103

Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems;

Recognising the affective state of VR exergame players could enable us to personalise and optimise their experience. However, affect recognition based on psychophysiological measurements for high intensity VR exergames presents challenges as the effects of exercise and VR headsets interfere with typical measurements. This paper presents novel predictors of affect based on gaze fixations, eye blinks, pupil diameter, and skin conductivity for affect recognition in high intensity VR exergaming.

https://dl.acm.org/doi/abs/10.1145/3313831.3376596

Guidelines for Affect Elicitation and Tracking in High Intensity VR Exergaming; Momentary Emotion Elicitation and Capture Workshop at CHI 2020 Conference

This position paper on VR exergaming provides an overview of advances made in affect elicitation and tracking. It outlines guidelines for evoking underwhelming, overwhelming, and optimal affective states and tracking the affective state using psychophysiological measurements in high intensity VR exergaming. It discusses the research challenges that need
to be addressed to implement affectively adaptive high intensity VR exergaming.

https://purehost.bath.ac.uk/ws/portalfiles/portal/204322852/CHI2020_Position_Paper.pdf 

Interactive Feedforward for Improving Performance and Maintaining Intrinsic Motivation in VR Exergaming
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems;

This paper presents a novel method called interactive feedforward, which is an interactive
adaptation of the psychophysical feedforward training method where rapid improvements in
performance are achieved by creating self-models showing previously unachieved performance
levels. Interactive feedforward was evaluated in a cycling-based VR exergame where players
interacted and competed with their self-model at real-time in a VR experience. Interactive
feedforward led to improved exercise performance while maintaining intrinsic motivation.

http://dx.doi.org/10.1145/3173574.3173982

Teaching

I am the module leader of CM6121 AND CM6621, Fundamentals of Computing with Java.

Biography

I was a Marie Curie Fellow with Industrial Research Enhancement (FIRE) researcher in the Department of Computer Science at the University of Bath. I was awarded the FIRE fellowship via a competitive international process. I was also an active member of the CAMERA (Centre for the Analysis of Motion, Entertainment Research & Applications) team and I have worked alongside health researchers, computer scientists, and psychologists. I hold a PhD in human computer interaction and my research was on VR active gaming. My PhD research focused on improving exercise performance, maintaining motivation, and tracking user experience while using VR active games. 

I developed innovative Virtual Reality Exercise controlled gaming applications including novel instrumentation techniques to monitor the user’s experience reflected by their emotional state (affective state) while using them. I was accepted into the highly competitive Entrepreneur First (EF) programme which has an acceptance rate of less than 3%. I am now a lecturer at Cardiff University.

Supervisions

My primary research interests are:

Active Gaming;
Immersive Media Applications;
Affective Computing.