Recently, much work has been done to research personal identifiability of extended reality (XR) users. Many of these prior studies are task-specific and involve identifying users completing a specific XR task. On the other hand, some studies have been domainspecific and focus on identifying users completing different XR tasks from the same domain, such as watching 360° videos or assembling structures. In this paper, we present one of the few studies to investigate cross-domain identification (i.e., identifying users completing XR tasks from different domains). To facilitate our investigation, we used open-source datasets from two different virtual reality (VR) studies-one from an assembly domain and one from a gaming domain-to investigate the feasibility of cross-domain gender identification, as personal identification is not possible between these datasets. The results of our machine learning experiments clearly demonstrate that cross-domain gender identification is more difficult than domain-specific gender identification. Furthermore, our results indicate that head position is important for gender identification and demonstrate that the k-nearest neighbors (kNN) algorithm is not suitable for cross-domain gender identification, which future researchers should be aware of.

Recently, there has been much interest in using virtual reality (VR) tracking data to authenticate or identify users. Most prior research has relied on task-specific characteristics but newer studies have begun investigating task-agnostic, domain-specific approaches. In this paper, we present one of the first systematic investigations of how different combinations of VR tracked devices (i.e., the headset, dominant hand controller, and non-dominant hand controller) and their spatial representations (i.e., position and/or rotation as Euler angles, quaternions, or 6D) affect identification accuracy for domain-specific approaches. We conducted a user study $( n =45)$ involving participants learning how to assemble two distinct full-scale constructions. Our results indicate that more tracked devices improve identification accuracies for the same assembly task, but only headset features afford the best accuracies across the domain-specific tasks. Our results also indicate that spatial features involving position and any rotation yield better accuracies than either alone.

Recent research indicates that user tracking data from virtual reality (VR) experiences can be used to personally identify users with degrees of accuracy as high as 95%. However, these results indicating that VR tracking data should be understood as personally identifying data were based on observing 360° videos. In this paper, we present results based on sessions of user tracking data from an ecologically valid VR training application, which indicate that the prior claims may not be as applicable for identifying users beyond the context of observing 360° videos. Our results indicate that the degree of identification accuracy notably decreases between VR sessions. Furthermore, we present results indicating that user tracking data can be obfuscated by encoding positional data as velocity data, which has been successfully used to predict other user experience outcomes like simulator sickness and knowledge acquisition. These results, which show identification accuracies were reduced by more than half, indicate that velocity-based encoding can be used to reduce identifiability and help protect personal identifying data.

Many research studies have investigated avatar embodiment and its effects on self-location, agency, and body ownership. Researchers have also investigated the effects of various external stimuli and avatar appearances during embodiment. However, the effects of body tracking fidelity while embodying an inverse-kinematic avatar are relatively unexplored. In this paper, we present two studies using a set of six trackers that investigate four levels of body tracking fidelity during avatar embodiment for male participants only: Complete (head, hands, feet, and pelvis trackers), Head-and-Extremities (head, hands, and feet trackers), Head-and-Hands (head and hands trackers), and No-Avatar (head and hands trackers; only controllers visible). Our results indicate that tracking the head, hands, and feet significantly increases the sense of embodiment and the sense of spatial presence when embodying an inverse-kinematic avatar for male participants.

Virtual Reality (VR) for training and education of real-world tasks has been researched extensively and has growing use in industry. The data generated by trainees in VR could be leveraged to improve the ability to evaluate learning beyond that which is possible in traditional training scenarios. In this paper, we present a machine learning approach that is able to classify users into participants with low-learning (LL) and high-learning (HL) gains, based on a knowledge test, using only the linear and angular velocities of the head-mounted display (HMD) and handheld controllers. To collect this data, we conduct a VR training user study. We demonstrate that even with a limited data set, it is possible to train a machine learning classifier to predict a trainee's learning performance for a given task with high degrees of accuracy and confidence. We investigate three different sets of velocity-based input features and two feature representations in a machine learning experiment. Our results indicate that all feature combinations resulted in high degrees of accuracy and confidence for predicting learning gains in our testing data. By employing a novel visualization technique, we were able to determine that participants with HL gains moved with greater velocities and fewer changes in direction than those with LL gains. These results indicate that it may be feasible to create VR training applications that can predict a user's learning gains and dynamically adapt the training to better support the user's learning, based on commonly available tracking data.

Some of the most widely used selection techniques for extended reality (XR) are based on virtual hand interactions. Many existing XR frameworks provide this functionality by default; however, their implementation can differ in slight, but important ways. When preparing to make a selection with a virtual hand technique, a user's desired selection can potentially be ambiguous due to multiple intersections. Systems with varying underlying virtual hand implementations may yield contrasting selections due to resolving multiple intersections differently. This is particularly an issue when objects are smaller in size than the virtual hand representation and in dense environments. To demonstrate the importance of these differences, we present a virtual hand selection study comparing three methods that are currently used in popular XR frameworks for disambiguating selections: Closest Intersected, First Intersected, and Last Intersected. The results of our study show that the Closest Intersected method affords significantly faster selections, significantly fewer incorrect and missed selections, and yields significantly better effective throughput than the other two methods. These results show that using a framework's built-in selection technique can significantly affect an XR application's usability.

Context-dependent memory studies have indicated that olfaction, the sense of smell, has a special odor memory that can significantly improve recall in some cases. Virtual reality (VR), which has been investigated as a training tool, could feasibly benefit from odor memory by incorporating olfactory stimuli. There have been a few studies on this concept for semantic learning, but not for procedural training. To address this gap in knowledge, we investigated the effects of olfaction on the transfer of knowledge from training to next-day execution for building a complex LEGO jet-plane model. Our results indicate that the pleasantness of an odor significantly affects training transfer more than whether the encoding and recall contexts match.