Researchers frequently collect position and orientation data from tracking hardware during virtual reality research studies. These data capture important information about participants' movements during experiments, but often result in large and complex datasets that can be challenging to analyze and interpret. We explore the potential of standard statistical measures that could be used to interpret position and orientation tracking data, and discuss advantages and uses of each measure. We further present three new measurement techniques - 95% range, time spent at mode, and coefficient of determination. We then evaluate the effectiveness of each statistical measure to detect differences in position and orientation on two existing data sets. We find that all of the tested measures are effective in discerning known main effects in position and orientation with varying degrees of sensitivity. This work is intended to guide researchers on future analysis and interpretation of motion tracking data sets.

Social virtual reality is an emerging medium of communication. In this medium, a user’s avatar (virtual representation) is controlled by the tracked motion of the user’s headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments.

In networked virtual reality (VR), user behaviors, individual differences, and group dynamics can serve as important signals for future speech behaviors, such as who the next speaker will be and the timing of turn-taking behaviors. The ability to predict and understand these behaviors offers opportunities to provide adaptive and personalized assistance, for example helping users with varying sensory abilities navigate complex social scenes and instantiating virtual moderators with natural behaviors. In this work, we predict turn-taking behaviors using features extracted based on social dynamics literature. We discuss results from a large-scale VR classroom dataset consisting of 77 sessions and 1660 minutes of small-group social interactions collected over four weeks. In our evaluation, gradient boosting classifiers achieved the best performance, with accuracies of 0.71-0.78 AUC (area under the ROC curve) across three tasks concerning the ''what'', ''who'', and ''when'' of turn-taking behaviors. In interpreting these models, we found that group size, listener personality, speech-related behavior (e.g., time elapsed since the listener's last speech event), group visual attention (e.g., the group's head orientation towards the speaker), and the listener and previous speaker's head pitch, head y-axis position, and left hand y-axis position more saliently influenced predictions. Results suggested that these features remain reliable indicators in novel social VR settings, as prediction performance is robust over time and with groups and activities not used in the training dataset. We discuss theoretical and practical implications of the work.

Scholars who study nonverbal behavior have focused an incredible amount of work on proxemics, how close people stand to one another, and mutual gaze, whether or not they are looking at one another. Moreover, many studies have demonstrated a correlation between gaze and distance, and so-called equilibrium theory posits that people modulate gaze and distance to maintain proper levels of nonverbal intimacy. Virtual reality scholars have also focused on these two constructs, both for theoretical reasons, as distance and gaze are often used as proxies for psychological constructs such as social presence, and for methodological reasons, as head orientation and body position are automatically produced by most VR tracking systems. However, to date, the studies of distance and gaze in VR have largely been conducted in laboratory settings, observing behavior of a small number of participants for short periods of time. In this experimental field study, we analyze the proxemics and gaze of 232 participants over two experimental studies who each contributed up to about 240 minutes of tracking data during eight weekly 30-minute social virtual reality sessions. Participants' non-verbal behaviors changed in conjunction with context manipulations and over time. Interpersonal distance increased with the size of the virtual room; and both mutual gaze and interpersonal distance increased over time. Overall, participants oriented their heads toward the center of walls rather than to corners of rectangularly-aligned environments. Finally, statistical models demonstrated that individual differences matter, with pairs and groups maintaining more consistent differences over time than would be predicted by chance. Implications for theory and practice are discussed.