Typing and editing precise numerical input, in particular floating-point values, are essential tasks in Spatial Computing for applications such as 3D precision modeling, object measurement, object dimensioning, mathematical visualization, and immersive media creation. Yet the adopted interfaces and interaction techniques in VR/AR/MR are often replicas of flat, palm-sized numpads such as those found in physical calculators or their WIMP counterparts. To move beyond such conventional confines, this paper explores redesigning the numpad by leveraging the spatial freedom of VR with a specific focus on introducing redundancy in the input of floating point values. To do so, we took inspiration from mechanisms such as combination dials, movable numbers, and a mechanical calculator that offer a larger and multi-column number layout. We assess how redundant interfaces can enhance user experience and efficiency when it comes to precise number editing of floating point values. Through a user study (N=30), we compared four numpads where participants engage in inputting a list of target numbers within a virtual environment. Our findings reveal that the redesigned numpads, which utilize redundant design elements, were preferred by users over the conventional numpad design as they provided clearer and more efficient number input methods in VR.

This work examines the estimation of heart rate and respiratory rate using only the kinematic data captured by a consumer-grade standalone VR devices. The high-resolution motion tracking offered by these devices creates an opportunity for indirect vital sign detection through spectral analysis of subtle VR device movement data. In our study, kinematic data were collected from a Meta Quest 3 head-mounted display, controllers and MX Ink pen across multiple posture configurations (e.g., seated, standing, lying down), both at rest and after moderate exercise. These postures emulate real-world XR scenarios for rest, fitness, and meditation. The collected data was processed using what we refer to as the Ghost approach, a simple yet effective method that applies a Fast Fourier Transform to capture the spectral components associated with respiratory and cardiac rhythms. Ground-truth biosignals were simultaneously recorded using wearable physiological sensors for validation. Results clearly reveal that both heart rate and respiratory rate can be reliably estimated from subtle micro-movements in the head-mounted display, VR controllers, or VR pen, revealing the potential for non-contact physiological monitoring within immersive environments. Finally, we demonstrate a use case of a VR stethoscope, where a standard VR controller is repurposed to estimate heart and respiratory rates.

Multidisciplinary teams are crucial in tailoring cancer care through collaborative decision-making involving several clinical specialties. The inherent complexity of clinical cases, the increasing abundance of unstructured textual data, and the time restrictions of professionals pose significant challenges to team coordination and patient care. This creates an opportunity for generative AI technologies, such as LLMs, to enhance collaborative work. Despite the growing interest in HCI research to explore LLMs in healthcare, we have yet to understand clinicians' perspectives on this emerging technology in multidisciplinary teams. Our work investigates the challenges, expectations and opportunities for LLMs in this context through a speculative approach. We leveraged the Futures Cone framework and conducted a qualitative study with 11 physicians from different cancer multidisciplinary teams. We contribute with an analysis of themes that emerged from individual interviews and a focus group, highlighting LLMs' potential to enhance and reshape multidisciplinary teams' practices. In addition, we uncover concerns and coping strategies related to LLMs' adoption and provide a set of design opportunities to inform the development of technologies for LLM-enhanced multidisciplinary teams.

Traditional anatomy flashcards, with their recognizable static illustrations on the front side and comprehensive lists of concepts on the back, are a long-standing tool for memorizing and refreshing anatomical concepts. This study repurposes such established tool by introducing two key elements: (i) Augmented Reality (AR) lenses acting as magic mirrors enabling users to view anatomical illustrations mapped onto their own faces, and (ii) a knowledge map layer acting as the card’s backside to visually and explicitly illustrate conceptual connections between anatomical reference points. Using Snapchat’s Lens Studio, we crafted a deck of interactive facial anatomy flashcards to assess the potential of AR and knowledge maps for retaining and refreshing anatomical concepts. We conducted a user study involving 44 university-level students. Divided into two groups, participants utilized either flashcard lenses with knowledge maps or traditional flashcards to quickly grasp and refresh anatomical concepts. By employing an approach that integrates anatomical quizzes for objective assessment with surveys and interviews for subjective feedback, our results indicate that anatomy flashcard lenses with knowledge maps offer a more engaging educational experience, yielding higher user preferences and satisfaction levels compared to traditional flashcards. While both approaches showed similar effectiveness in quiz scores, anatomy flashcard lenses with knowledge maps were favored for their usability, significantly reducing temporal demand. These findings underscore the engaging and effective nature of anatomy flashcard lenses with knowledge maps, highlighting them as an alternative tool for the quick retention and review of anatomical concepts.

Monitoring and analyzing physical activity is becoming an important task in both clinical and non-clinical settings. To accomplish this desideratum, stick figures are often used as abstractions of human poses and movements by representing body segments as straight lines (sticks). Despite their straightforwardness, this minimalist representation is incomplete as it lacks the segments’ longitudinal rotations, and therefore, is insufficient for applications requiring full 3D kinematic data. We introduce STONES, an advanced machine learning approach for estimating longitudinal body segment rotations of based on stick figures defined from a minimal set of body points. Our approach relies on a recurrent deep neural network, which takes 3D joint positions from a minimalist stick figure representation, such as those acquired by conventional depth camera sensors, and completes it with accurate longitudinal segment rotations. We validated our approach via a test scenario based on exergaming activities (e.g., lunges, squats, and kicks), which are becoming an emerging trend in several healthcare sectors, and our estimations show a fit above 98% and mean errors of approximately 1 ∘. Our deep learning approach effectively surpasses other machine learning-based strategies and closely matches the accuracy of state-of-the-art motion capture systems while running at real-time speeds.

Supershapes are used in Parametric Design to model, literally, thou-sands of natural and man-made shapes with a single 6 parameter formula. However, users are left to probe such a rich yet dense collection of supershapes using a set of independent 1-D sliders. Some of the formula’s parameters are non-linear in nature, making them particularly difficult to grasp with conventional 1-D sliders alone. VR appears as a promising setting for Parametric Design with supershapes since it empowers users with more natural visual inspection and shape browsing techniques, with multiple solutions being displayed at once and the possibility to design more interesting forms of slider interaction. In this work, we propose VR shape widgets that allow users to probe and select supershapes from a multitude of solutions. Our designs take leverage on thumbnails, mini-maps, haptic feedback and spatial interaction, while supporting 1-D, 2-D and 3-D supershape parameter spaces. We conducted a user study (N = 18) and found that VR shape widgets are effective, more efficient, and natural than conventional VR 1-D sliders while also usable for users without prior knowledge on supershapes. We also found that the proposed VR widgets provide a quick overview of the main supershapes, and users can easily reach the desired solution without having to perform fine-grain handle manipulations.

The Empathy-Effective Communication hypothesis states the better a speaker can understand their listener’s emotions, the better can they transmit information; and the better a listener can understand the speaker’s emotions, the better can they apprehend the information. Previous emotional sharing systems have managed to create a space of emotional understanding between collaborators on remote locations using bio-sensing, but how a context of face-to-face communication can benefit from biofeedback is still to be studied. This study introduces a new Augmented Reality communication cue from an emotion recognition neural network model, trained using electrocardiogram physiological data (AuRea). The proposed de-sign is meant to facilitate emotional state understanding, increasing cognitive empathy without compromising the existing verbal, non-verbal, and paraverbal communication cues. We conducted a study where pairs of participants (N=12) engaged in three tasks where AuRea was found to positively affect performance and emotional understanding, but negatively affect memorization.

course Share on Approaches and challenges to virtual and augmented reality in health care and rehabilitation: a short course Authors: Joaquim Jorge U Lisboa U LisboaView Profile , Pedro Campos U Lisboa U LisboaView Profile , Daniel Simões Lopes U Lisboa U LisboaView Profile Authors Info & Claims SA '19: SIGGRAPH Asia 2019 CoursesNovember 2019 Article No.: 124Pages 1–85https://doi.org/10.1145/3355047.3359418Published:17 November 2019Publication History 0citation206DownloadsMetricsTotal Citations0Total Downloads206Last 12 Months20Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Current state-of-the-art point cloud visualization techniques have shortcomings when dealing with sparse and less accurate data or close-up interactions. In this paper, we present a visualization technique called stroke-based splatting, which applies concepts of stroke-based rendering to surface-aligned splatting, allowing for better shape perception at lower resolutions and close-ups. We create a painterly depiction of the data with an impressionistic aesthetic, which is a metaphor the user is culturally trained to recognize, thus attributing higher quality to the visualization. This is achieved by shaping each object-aligned splat as a brush stroke, and orienting it according to globally coherent tangent vectors from the Householder formula, creating a painterly depiction of the scanned cloud. Each splat is sized according to a color-based clustering analysis of the data, ensuring the consistency of brush strokes within neighborhood areas. By controlling brush shape generation parameters and blending factors between neighboring splats, the user is able to simulate different painting styles in real time. We have tested our method with data sets captured by commodity laser scanners as well as publicly available high-resolution point clouds, both having highly interactive frame rates in all cases. In addition, a user study was conducted comparing our approach to state-of-the-art point cloud visualization techniques. Users considered stroke-based splatting a valuable technique as it provides a higher or similar visual quality to current approaches.

Reading room conditions such as illumination, ambient light, human factors and display luminance, play an important role on how radiologists analyze and interpret images. Indeed, serious diagnostic errors can appear when observing images through everyday monitors. Typically, these occur whenever professionals are ill-positioned with respect to the display or visualize images under improper light and luminance conditions. In this work, we show that virtual reality can assist radiodiagnostics by considerably diminishing or cancel out the effects of unsuitable ambient conditions. Our approach combines immersive head-mounted displays with interactive surfaces to support professional radiologists in analyzing medical images and formulating diagnostics. We evaluated our prototype with two senior medical doctors and four seasoned radiology fellows. Results indicate that our approach constitutes a viable, flexible, portable and cost-efficient option to traditional radiology reading rooms.

Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.