Physical and Physiological Data for Customizing Immersive VR Training

The assessment of Virtual reality (VR) applications and serious games often relies on measures of usability, engagement, motion sickness, and cognitive and user performance to determine how the experience was perceived and whether the learning outcomes were met. In addition, physical and physiological information is captured to develop an understanding of behavioral patterns that can help improve the user experience. However, the acquisition of physiological information requires high-end equipment typically exclusive to industry and research institutions, a scenario that is changing as consumer-level VR technology, open electronics, and Makerspace are becoming more readily available. However, VR technology remains exclusive as hardware and interactions assume a one-size- fits-all approach with little customization that accounts for the inherent high user variability. While efforts are currently underway to make VR more inclusive and accessible, the solutions focus on specific user needs. This paper presents the prototyping of a framework consisting of three subsystems for factoring of upper limb ergonomics, skin response, and muscle activity, and gaze tracking as metrics to assist in VR task completion. Due to the preliminary nature of this work, we present a discussion on what we have learned so far through the development of these subsystems applied in three use cases.