arXiv subjects

Logan Lane

Publications and source records attributed to Logan Lane.

4 recordsLinked to original sources

arXiv2026

Evaluating the Viability of Additive Models to Predict Task Completion Time for 3D Interactions in Augmented Reality

Additive models of interaction performance, such as the Keystroke-Level Model (KLM), are tools that allow designers to compare and optimize the performance of user interfaces by summing the predicted times for the atomic components of a specific interaction to predict the total time it would take to complete that interaction. There has been extensive work in creating such additive models for 2D interfaces, but this approach has rarely been explored for 3D user interfaces. We propose a KLM-style additive model, based on existing atomic task models in the literature, to predict task completion time for 3D interaction tasks. We performed two studies to evaluate the feasibility of this approach across multiple input modalities, with one study using a simple menu selection task and the other a more complex manipulation task. We found that several of the models from the literature predicted actual task performance with less than 20% error in both the menu selection and manipulation study. Overall, we found that additive models can predict both absolute and relative performance of input modalities with reasonable accuracy.

cs.HC↗

arXiv2025

Revisiting Performance Models of Distal Pointing Tasks in Virtual Reality

Performance models of interaction, such as Fitts Law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the 3D interaction task of distal pointing, in which the user points their hand or a device at a distant target in order to select it. However, there is no consensus on how to compute the index of difficulty for distal pointing tasks. We present a preliminary study suggesting that existing models may not be sufficient to model distal pointing performance with current virtual reality technologies. Based on these results, we hypothesized that both the form of the model and the standard method for collecting empirical data for pointing tasks might need to change in order to achieve a more accurate and valid distal pointing model. In our main study, we used a new methodology to collect distal pointing data and evaluated traditional models, purely ballistic models, and two-part models. Ultimately, we found that the best model used a simple Fitts-Law-style index of difficulty with angular measures of amplitude and width.

cs.HC↗

arXiv2025

Exploring the Effects of Level of Control in the Initialization of Shared Whiteboarding Sessions in Collaborative Augmented Reality

Augmented Reality (AR) collaboration can benefit from a shared 2D surface, such as a whiteboard. However, many features of each collaborators physical environment must be considered in order to determine the best placement and shape of the shared surface. We explored the effects of three methods for beginning a collaborative whiteboarding session with varying levels of user control: MANUAL, DISCRETE CHOICE, and AUTOMATIC by conducting a simulated AR study within Virtual Reality (VR). In the MANUAL method, users draw their own surfaces directly in the environment until they agree on the placement; in the DISCRETE CHOICE method, the system provides three options for whiteboard size and location; and in the AUTOMATIC method, the system automatically creates a whiteboard that fits within each collaborators environment. We evaluate these three conditions in a study in which two collaborators used each method to begin collaboration sessions. After establishing a session, the users worked together to complete an affinity diagramming task using the shared whiteboard. We found that the majority of participants preferred to have direct control during the initialization of a new collaboration session, despite the additional workload induced by the Manual method.

cs.HC↗

arXiv2024

A digital twin based approach to smart lighting design

Lighting has a critical impact on user mood and behavior, especially in architectural settings. Consequently, smart lighting design is a rapidly growing research area. We describe a digital twin-based approach to smart lighting design that uses an immersive virtual reality digital twin equivalent (virtual environment) of the real world, physical architectural space to explore the visual impact of light configurations. The CLIP neural network is used to obtain a similarity measure between a photo of the physical space with the corresponding rendering in the virtual environment. A case study was used to evaluate the proposed design process. The obtained similarity value of over 87% demonstrates the utility of the proposed approach.

cs.HC↗