This study presents a quantitative review of 49 empirical XR interaction experiments, examining whether task types are systematically related to the interaction techniques and evaluation metrics reported in prior studies. Understanding this relationship is important for informing XR interaction design and supporting more context-aware selection of interaction techniques across diverse task scenarios. A dataset of 230 task–technique pairs and 184 task–metric occurrences was constructed and analyzed across three task categories—discrete, spatial, and continuous. Major interaction techniques, including Direct Controller, hand tracking, eye tracking, raycasting, and other modalities, were examined alongside objective and subjective evaluation metrics using chi-square tests, standardized residuals, and correspondence analysis. The results indicate that XR interaction techniques are not strongly determined by task type, but are instead applied flexibly across different task contexts. Similarly, evaluation metrics are not strictly tied to specific task categories, but vary depending on study-specific goals and experimental settings. These findings suggest that commonly used XR interaction techniques function as general-purpose tools that can be adapted across diverse scenarios, rather than being inherently linked to particular task types. However, these findings are based on a filtered subset of empirical studies and should be interpreted within this scope. This work provides a structured statistical mapping of empirical XR interaction literature and outlines a reproducible analysis procedure for future investigations.

When motion capture data is applied to virtual characters, the applied motion often exhibits geometric and physical errors, which necessitates a cumbersome refinement process. We present a novel framework to efficiently obtain a corrected motion as well as its supporting contact information from multi-contact motion capture data. To this end, first, we present a projective dynamics-based method for optimizing character motions. By carefully defining objective functions and constraints using differential representation of motions, we develop a highly efficient motion optimizer that can create geometrically and dynamically adjusted motions given reference motion data and contact information. Second, we develop a contact optimizer that finds a set of contacts that allows the motion optimizer to generate a motion that best follows the reference motion under dynamic and geometric constraints. This is achieved by iteratively improving the hypothesis on the best set of contacts by getting feedback from the motion optimizer. We demonstrate that our method significantly improves the naturalness of a wide range of motion capture data, from walking to rolling.

Abstract In avatar‐mediated telepresence, remote users find it difficult to engage and maintain contact, such as a handshake, with each other without a haptic device. We address the problem of adjusting an avatar's pose to promote multifinger contact interaction between remote users. To this end, we first construct a contact point database for nine types of contact interactions between hands through contact experiments with human subjects. We then develop an optimization‐based framework to compute the avatar's pose that realizes the desired contact learned from the experiment while maintaining the naturalness of the hand pose. We show that our method improves the quality of hand interaction for the predefined set of social interactions.

The Information Visualization community has begun to pay attention to visualization literacy; however, researchers still lack instruments for measuring the visualization literacy of users. In order to address this gap, we systematically developed a visualization literacy assessment test (VLAT), especially for non-expert users in data visualization, by following the established procedure of test development in Psychological and Educational Measurement: (1) Test Blueprint Construction, (2) Test Item Generation, (3) Content Validity Evaluation, (4) Test Tryout and Item Analysis, (5) Test Item Selection, and (6) Reliability Evaluation. The VLAT consists of 12 data visualizations and 53 multiple-choice test items that cover eight data visualization tasks. The test items in the VLAT were evaluated with respect to their essentialness by five domain experts in Information Visualization and Visual Analytics (average content validity ratio = 0.66). The VLAT was also tried out on a sample of 191 test takers and showed high reliability (reliability coefficient omega = 0.76). In addition, we demonstrated the relationship between users' visualization literacy and aptitude for learning an unfamiliar visualization and showed that they had a fairly high positive relationship (correlation coefficient = 0.64). Finally, we discuss evidence for the validity of the VLAT and potential research areas that are related to the instrument.

We present an avatar animation technique for a telepresence system that allows for the hand contact, especially handshaking, between remote users. The key idea is that, while the avatar follows the remote user's motion normally, it modifies the motion to create and maintain hand contact with the local user when the two users try to engage hand contact. To this end, we develop the support vector machine (SVM)-based classifiers to recognize the users' intention for contact interaction, and online motion generation method to create realistic image sequence of an avatar to realize the continuous contact with the user. A user study has been conducted to verify the effect of our method on the social telepresence.

Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes-a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables.

In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information Vlsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.

Social touch such as a handshake increases the sense of coexistence and closeness between remote users in a social telepresence environment, but creating such coordinated contact movements with a distant person is extremely difficult if given only visual feedback, without haptic feedback. This paper presents a method to enable hand-contact interaction between remote users in an avatar-mediated telepresence environment. The key approach is, while the avatar directly follows its owner's motion in normal conditions, it adjusts the pose to maintain contact with the other user when the two users attempt to make contact interaction. To this end, we develop classifiers to recognize the users' intention for the contact interaction. The contact classifier identifies whether the users try to initiate contact when they are not in contact, and the separation classifier identifies whether the two in contact attempt to break contact. The classifiers are trained based on a set of geometric distance features. During the contact phase, inverse kinematics is solved to determine the pose of the avatar's arm so as to initiate and maintain natural contact with the other user's hand. Our system is unique in that two remote users can perform real time hand contact interaction in a social telepresence environment.

Healthcare information systems (e.g., Bar Code Medication Administration [BCMA] system) have been adopted to deliver efficient healthcare services recently. However, though it is seemingly simple to use (scanning barcodes before medication), users of the BCMA system (e.g., nurses and pharmacists) often show noncompliance behaviors. Therefore, the goal of this study is to comprehensively understand why such noncompliance behaviors occur with BCMA system. Through comprehensive literature review, 128 instances of causes were identified, which were categorized into five categories: Poor Visual and Audio Interface, Poor Physical Ergonomic Design, Poor Information Integrity, Abnormal Situation for System Use, and User Reluctance and Negligence. The results show that successful use of a BCMA system requires supportive systems and environments, so it is more like an issue of the system rather than that of an individual user or a device. It is believed that the proposed categories could be applicable in investigating noncompliance behaviors in other healthcare information systems as well.