A Human Motor Behavior Model for Direct Pointing at a Distance

Models of human motor behavior are well known as an aid in the design of user interfaces (UIs). Most current models apply primarily to desktop interaction, but with the development of non-desktop UIs, new types of motor behaviors need to be modeled. Direct Pointing at a Distance is such a motor behavior. A model of direct pointing at a distance would be particularly useful in the comparison of different interaction techniques, because the performance of such techniques is highly dependent on user strategy, making controlled studies difficult to perform. Inspired by Fitts’ law, we studied four possible models and concluded that movement time for a direct pointing task is best described as a function of the angular amplitude of movement and the angular size of the target. Contrary to Fitts’ law, our model shows that the angular size has a much larger effect on movement time than the angular amplitude and that the growth in the difficulty of the tasks is quadratic, rather then linear. We estimated the model’s parameters experimentally with a correlation coefficient of 96%

Towards Unification for Pointing Task Evaluation in 3D Desktop Virtual Environment

International audienceNew visualization systems for large and complex datasets are emerging and 3D Virtual Environments turn out to be a relevant solution. Interaction tasks in these 3D VE have been defined, especially to support evaluation of these applications. Nevertheless there is a lack of unified protocol to assess these elementary tasks in this context. Moreover it can be complex to determine the appropriate technique to perform these tasks as there is a lack of reference data. A standard is available for 2D pointing task, but there is no equivalence in 3D. In this paper, we propose an adaptation of this standard to a pointing task in a 3D VE. We detail our protocol and an instrumentation, which aims at assessing performance, comfort of techniques and satisfaction of users. We also present results of a user experimentation conducted according to this standard’s adaptation

Pointing all around you : selection performance of mouse and ray-cast pointing in full-coverage displays

Funding: SurfNet (NSERC, Canada), EPSRC (Small Equipment Grant).As display environments become larger and more diverse - now often encompassing multiple walls and room surfaces - it is becoming more common that users must find and manipulate digital artifacts not directly in front of them. There is little understanding, however, about what techniques and devices are best for carrying out basic operations above, behind, or to the side of the user. We conducted an empirical study comparing two main techniques that are suitable for full-coverage display environments: mouse-based pointing, and ray-cast `laser' pointing. Participants completed search and pointing tasks on the walls and ceiling, and we measured completion time, path lengths and perceived effort. Our study showed a strong interaction between performance and target location: when the target position was not known a priori the mouse was fastest for targets on the front wall, but ray-casting was faster for targets behind the user. Our findings provide new empirical evidence that can help designers choose pointing techniques for full-coverage spaces.Postprin

The Challenges in Modeling Human Performance in 3D Space with Fitts’ Law

With the rapid growth in virtual reality technologies, object interaction is becoming increasingly more immersive, elucidating human perception and leading to promising directions towards evaluating human performance under different settings. This spike in technological growth exponentially increased the need for a human performance metric in 3D space. Fitts' law is perhaps the most widely used human prediction model in HCI history attempting to capture human movement in lower dimensions. Despite the collective effort towards deriving an advanced extension of a 3D human performance model based on Fitts' law, a standardized metric is still missing. Moreover, most of the extensions to date assume or limit their findings to certain settings, effectively disregarding important variables that are fundamental to 3D object interaction. In this review, we investigate and analyze the most prominent extensions of Fitts' law and compare their characteristics pinpointing to potentially important aspects for deriving a higher-dimensional performance model. Lastly, we mention the complexities, frontiers as well as potential challenges that may lay ahead.Comment: Accepted at ACM CHI 2021 Conference on Human Factors in Computing Systems (CHI '21 Extended Abstracts

Evaluating 3D pointing techniques

"This dissertation investigates various issues related to the empirical evaluation of 3D pointing interfaces. In this context, the term ""3D pointing"" is appropriated from analogous 2D pointing literature to refer to 3D point selection tasks, i.e., specifying a target in three-dimensional space. Such pointing interfaces are required for interaction with virtual 3D environments, e.g., in computer games and virtual reality. Researchers have developed and empirically evaluated many such techniques. Yet, several technical issues and human factors complicate evaluation. Moreover, results tend not to be directly comparable between experiments, as these experiments usually use different methodologies and measures. Based on well-established methods for comparing 2D pointing interfaces this dissertation investigates different aspects of 3D pointing. The main objective of this work is to establish methods for the direct and fair comparisons between 2D and 3D pointing interfaces. This dissertation proposes and then validates an experimental paradigm for evaluating 3D interaction techniques that rely on pointing. It also investigates some technical considerations such as latency and device noise. Results show that the mouse outperforms (between 10% and 60%) other 3D input techniques in all tested conditions. Moreover, a monoscopic cursor tends to perform better than a stereo cursor when using stereo display, by as much as 30% for deep targets. Results suggest that common 3D pointing techniques are best modelled by first projecting target parameters (i.e., distance and size) to the screen plane.

Exploring Users' Pointing Performance on Virtual and Physical Large Curved Displays

Large curved displays have emerged as a powerful platform for collaboration, data visualization, and entertainment. These displays provide highly immersive experiences, a wider field of view, and higher satisfaction levels. Yet, large curved displays are not commonly available due to their high costs. With the recent advancement of Head Mounted Displays (HMDs), large curved displays can be simulated in Virtual Reality (VR) with minimal cost and space requirements. However, to consider the virtual display as an alternative to the physical display, it is necessary to uncover user performance differences (e.g., pointing speed and accuracy) between these two platforms. In this paper, we explored users' pointing performance on both physical and virtual large curved displays. Specifically, with two studies, we investigate users' performance between the two platforms for standard pointing factors such as target width, target amplitude as well as users' position relative to the screen. Results from user studies reveal no significant difference in pointing performance between the two platforms when users are located at the same position relative to the screen. In addition, we observe users' pointing performance improves when they are located at the center of a semi-circular display compared to off-centered positions. We conclude by outlining design implications for pointing on large curved virtual displays. These findings show that large curved virtual displays are a viable alternative to physical displays for pointing tasks.Comment: In 29th ACM Symposium on Virtual Reality Software and Technology (VRST 2023

Prediction of user action in moving-target selection tasks

Selection of moving targets is a common task in human–computer interaction (HCI), and more specifically in virtual reality (VR). In spite of the increased number of applications involving moving–target selection, HCI and VR studies have largely focused on static-target selection. Compared to its static-target counterpart, however, moving-target selection poses special challenges, including the need to continuously and simultaneously track the target and plan to reach for it, which may be difficult depending on the user’s reactiveness and the target’s movement. Action prediction has proven to be the most comprehensive enhancement to address moving-target selection challenges. Current predictive techniques, however, heavily rely on continuous tracking of user actions, without considering the possibility that target-reaching actions may have a dominant pre-programmed component—this theory is known as the pre-programmed control theory. Thus, based on the pre-programmed control theory, this research explores the possibility of predicting moving-target selection prior to action execution. Specifically, three levels of action prediction are investigated: action performance, prospective action difficulty, and intention. The proposed performance models predict the movement time (MT) required to reach for a moving target in 2-D and 3-D space, and are useful to compare users and interfaces objectively. The prospective difficulty (PD) models predict the subjective effort required to reach for a moving target, without actually executing the action, and can therefore be measured when performance can not. Finally, the intention models predict the target that the user plans to select, and can therefore be used to facilitate the selection of the intended target. Intention prediction models are developed using decision trees and scoring functions, and evaluated in two VR studies: the first investigates undirected selection (i.e., tasks in which the users are free to select an object among multiple others), and the second directed selection (i.e., the more common experimental task in which users are instructed to select a specific object). PD models for 1-D, and 2-D moving-target selection tasks are developed based on Fitts’ Law, and evaluated in an online experiment. Finally, MT models with the same structural form of the aforementioned PD models are evaluated in a 3-D moving-target selection experiment deployed in VR. Aside from intention predictions on directed selection, all of the explored models yield relatively high accuracies—up to ~78% predicting intended targets in undirected tasks, R^2 = .97 predicting PD, and R^2 = .93 predicting MT