The prevalent evaluation methods used to estimate detection of redirected walking are based on methods from psychophysics that require users to know their virtual movements are being manipulated. However, this higher-than-normal level of attention toward their movements yields conservative detection thresholds. We find that participants who were unaware that redirected walking (translation gain) was applied detected the technique at a significantly higher gain than users who were aware (at gains of 1.73 and 1.38, respectively). We provide evidence that redirected walking-based navigation solutions may be able to leverage gain values that are larger than the current threshold guidelines would suggest.

Input remapping techniques have been widely explored to allow users in virtual reality to exceed both their own physical abilities, the limitations of physical space, or to facilitate interactions with real-world objects. Often considered is how these techniques can be applied to achieve maximum utility, but still be undetectable to users to maintain a sense of immersion and presence. Existing psychophysical methods used to determine these detection thresholds have known limitations: they are highly conservative lower bounds for detection and do not account for complex usage of the technique. Our work describes and evaluates a method for estimating detection that reduces these limitations and yields meaningful upper bounds. We present the findings of our work where we apply this method to a well-explored hand motion scaling technique. In wholly unaware cases, we determined that users may detect their hand speed as abnormal at around 3.37 times the normal speed, compared to a scale factor of 1.47 that was estimated using traditional methods when users knew the motion scaling was occurring. A considerable number of participants in unaware cases (12 of 56) never detected their hand speed increasing at all, even at the maximum scale factor of 5.0. The study demonstrates just how conservative the thresholds generated by traditional psychophysical methods can be compared to detection during naive usage, and our method can be modified and applied easily to other techniques.

Head tracking is commonly used in VR applications to allow users to naturally view 3D content using physical head movement, but many applications also support turning with hand-held controllers. Controller and joystick controls are convenient for practical settings where full 360-degree physical rotation is not possible, such as when the user is sitting at a desk. Though controller-based rotation provides the benefit of convenience, previous research has demonstrated that virtual or joystick-controlled view rotation to have drawbacks of sickness and disorientation compared to physical turning. To combat such issues, researchers have considered various techniques such as speed adjustments or reduced field of view, but data is limited on how different variations for joystick rotation influences sickness and orientation perception. Our studies include different variations of techniques such as joystick rotation, resetting, and field-of-view reduction. We investigate trade-offs among different techniques in terms of sickness and the ability to maintain spatial orientation. In two controlled experiments, participants traveled through a sequence of rooms and were tested on spatial orientation, and we also collected subjective measures of sickness and preference. Our findings indicate a preference by users towards directly-manipulated joystick-based rotations compared to user-initiated resetting and minimal effects of technique on spatial awareness.

Redirected walking can effectively utilize a user’s physical space when traversing larger virtual environments by using virtual self-motion gains for a user’s physical motions. In particular, curvature gain presents unique advantages in redirection but can lead to suboptimal orientations. To prevent this and add additional utility in redirected walking, we formally present strafing gain. Strafing gain seeks to add incremental lateral movements to a user’s position causing the user to walk along a diagonal trajectory while maintaining the original orientation of the user. In a study with 27 participants, we tested 11 values to determine the detection thresholds of strafing gain. The study, which was modeled on prior detection threshold studies, found that strafing gain could successfully redirect participants to walk along a 5.57° diagonal to the right and a 4.68° diagonal to the left. Furthermore, a supplementary study with 10 participants was conducted, verifying that orientation was maintained throughout redirection and validating the obtained detection thresholds. We discuss the implications of these findings and potential ways of improving these quantities in real-world applications.

Avatars are often used to provide representations of users in 3D environments, such as desktop games or VR applications. While full-body avatars are often sought to be used in applications, low visibility avatars (i.e., head and hands) are often used in a variety of contexts, either as intentional design choices, for simplicity in contexts where full-body avatars are not needed, or due to external limitations. Avatar style can also vary from more simplistic and abstract to highly realistic depending on application context and user choices. We present the results of two desktop experiments that examine avatar visibility, style, and observer view on accuracy in a pointing interpretation task. Significant effects of visibility were found, with effects varying between horizontal and vertical components of error, and error amounts not always worsening as a result of lowering visibility. Error due to avatar visibility was much smaller than error resulting from avatar style or observer view. Our findings suggest that humans are reasonably able to understand pointing gestures with a limited observable body.

Fitts' law has accurately modeled both children's and adults' pointing movements, but it is not as precise for modeling movement to small targets. To address this issue, prior work presented FFitts' law, which is more exact than Fitts' law for modeling adults' finger input on touchscreens. Since children's touch interactions are more variable than adults, it is unclear if FFitts' law should be applied to children. We conducted a 2D target acquisition task with 54 children (ages 5-10) to examine if FFitts' law can accurately model children's touchscreen movement time. We found that Fitts' law using nominal target widths is more accurate, with a R2 value of 0.93, than FFitts' law for modeling children's finger input on touchscreens. Our work contributes new understanding of how to accurately predict children's finger touch performance on touchscreens.

The future of smart environments is likely to involve both passive and active interactions on the part of users. Depending on what sensors are available in the space, users may make use of multimodal interaction modalities such as hand gestures or voice commands. There is a shortage of robust yet controlled multimodal interaction datasets for smart environment applications. One application domain of interest based on current state-of-the-art is authentication for sensitive or private tasks, such as banking and email. We present a novel, large multimodal dataset for authentication interactions in both gesture and voice, collected from 106 volunteers who each performed 10 examples of each of a set of hand gesture and spoken voice commands chosen from prior literature (10,600 gesture samples and 13,780 voice samples). We present the data collection method, raw data and common features extracted, and a case study illustrating how this dataset could be useful to researchers. Our goal is to provide a benchmark dataset for testing future multimodal authentication solutions, enabling comparison across approaches.

Virtual reality commonly makes use of tracked hand interactions for user input. Interaction techniques sometimes alter the mapping between the real and virtual coordinate systems to modify interaction possibilities. This paper studies fixed positional offsets applied to the location of the virtual hand. We present a controlled experiment in which users' hands were subject to fixed positional offsets of varying magnitudes while completing target-touching tasks. The study provides estimations for detection thresholds for positional hand offsets in six directions relative to the real-world location of the hand and provides evidence performance using offset virtual hands can vary based on offset parameters. Significant differences in offset detection were identified based on offset direction, indicating that positional adjustments made to virtual hands should consider directionality when limiting techniques rather than just a constant value. Hand offsets kept within the threshold value resulted in comparable performance to unmodified hand registration, while offsets beyond the threshold resulted in larger completion times.

In virtual reality (VR), natural physical hand interaction allows users to interact with virtual content using physical gestures. While the most straightforward use of tracked hand motion maintains a one-to-one mapping between the physical and virtual world, some cases might benefit from changing this mapping through scaled or redirected interactions that modify the mapping between user’s physical movements and the magnitude of corresponding virtual movements. However, large deviations in interaction fidelity may potentially provide distractions or a loss of perceived realism. Therefore, it is important to know the extent to which remapping techniques can be applied to scaled interactions in VR without users detecting the difference. In this paper, we extend prior research on redirected hand techniques by investigating user perception of scaled hand movements and estimating detection thresholds for different types of hand motion in VR. We conducted two experiments with a two-alternative forced-choice (2AFC) design to estimate the detection thresholds of remapped interaction. The first experiment tested the perception of motion scaling for simple hand movements, and the second experiment involved more complex reaching motions in a cognitively demanding game scenario. We present estimated detection thresholds for scale values that can be applied to virtual hand movements without users noticing the difference. Our findings show that detection thresholds differ significantly based on the type of hand movement (horizontal, vertical, and depth).