Interactive Feedforward in High Intensity VR Exergaming

Abstract

VR exergaming is a promising motivational tool to incentivise exercise. It has
been widely applied to low to moderate intensity exercise protocols; however, its
effectiveness in implementing high intensity protocols that require lesser time commitment remains unknown. This thesis 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, in contrast to how feedforward has typically been used, individuals were not merely passive recipients of a self-model but interacted with it in real-time in a VR experience. Interactive feedforward led to improved performance while maintaining intrinsic motivation. This thesis further explores interactive feedforward in a social context. Players competed with enhanced models of themselves, their friend, and a stranger moving at the same enhanced pace as their friend. Results show that competing with an enhanced model of a friend improves the performance and player experience the most. The main limitation of social interactive feedforward is that it is only suitable for players who have similar fitness levels as their friends to avoid underwhelming or overwhelming exergaming experiences. This limitation can be addressed by adapting the exergame intensity according to the player experience reflected by their affective state. Player experience estimation of VR exergame players by recognising their affective state could enable us to personalise and optimise their experience. Affect recognition based on psychophysiological measurements for high intensity VR exergames pose challenges as the effects of exercise and VR headsets interfere with those measurements. This thesis presents affective predictors based on gaze fixations, eye blinks, pupil diameter, and skin conductivity for affect recognition in high intensity VR exergaming. The findings of this thesis provide guidelines for interactive feedforward and affect recognition in high intensity VR exergames. In light of the findings, the research challenges that need to be overcome to implement affectively adaptive interactive feedforward in high intensity VR exergaming have been discussed.

Date of Award	16 Sept 2020
Original language	English
Awarding Institution	University of Bath
Sponsors	EPSRC Centre for Doctoral Training in Digital Entertainment (CDE) & EU - Horizon 2020
Supervisor	Eamonn O'Neill (Supervisor), Christof Lutteroth (Supervisor) & Michael Proulx (Supervisor)