Influence of Depth Cues on Eye Tracking Depth Measurement in Augmented Reality Using the Magic Leap Device

The eye-tracking technology is currently implemented in many mixed reality devices. However, eye-tracking measurements must be precise enough to permit correct localization in the environment; to allow, for example, linking such spatial positions to virtual objects. According to the human vision, major measurement issues would more likely occur in the depth axis rather than in the horizontal and vertical axes. In the literature, depth cues are known for improving human depth perception. In this study, our hypothesis is that, in an augmented reality environment, the more realistic virtual objects are displayed, thanks to depth cues, the more precise the eye-tracking device depth measures would be. Thus, using the MagicLeap device, we studied the effects of lighting and textures on eye-tracking depth measurement precision, by comparing the measures obtained under varying conditions of lights and textures, on both real and virtual objects. The results confirmed our general hypothesis, and we noticed a more significant influence of lights rather than textures on the precision of the measures. Moreover, we found that these depth cues reduce the measurement imprecision among observers, making the eye-tracking system more accurate when measuring depth

Perceived Space and Spatial Performance during Path-Integration Tasks in Consumer-Oriented Virtual Reality Environments

Studies using virtual reality environments (VE) have shown that subjects can perform path integration tasks with acceptable performance. However, in these studies, subjects could walk naturally across large tracking areas, or researchers provided them with large- immersive displays. Unfortunately, these configurations are far from current consumer-oriented VEs (COVEs), and little is known about how their limitations influence this task. Using a triangle completion paradigm, we assessed the subjects' spatial performance when developing path integration tasks in two consumer-oriented displays (an HTC Vive and a GearVR) and two consumer-oriented interaction devices (a Virtuix Omni motion platform and a Touchpad Control). Our results show that when locomotion is available (motion platform condition), there exist significant effects regarding the display and the path. In contrast, when locomotion is mediated no effect was found. Some future research directions are therefore proposed

BIM-based Mixed Reality Application for Supervision of Construction

International audienceBuilding Information Modelling (BIM) is an up-and-coming methodology and technology used in the Architecture, Engineering and Construction (AEC) industry, that allows data centralization and stakeholders' collaboration. But to check the accuracy of the work done on the worksite, it is necessary first to go on site and then to modify the BIM model. This paper presents a mixed reality (MR) application based on BIM data and drone videos, allowing off-site construction supervision. It permits to make annotations about differences between what has been planned in BIM and what has been built, using superimposition of the two sources. Then these ones can be transferred to the BIM model for corrections. Finally, we evaluate our work with building construction experts, providing to them a questionnaire to grade the application and to get feedback. Our major result is that as for them the application does really help to do construction supervisions; however, they suggest that the application should provide more interactions with the 3D model and with the videos

A Task-Centred Methodology to Evaluate the Design of Virtual Reality User Interactions: A Case Study on Hazard Identification

Virtual reality (VR) is a computer-based technology that can be used by professionals of many different fields to simulate an environment with a high feeling of presence and immersion. Nonetheless, one main issue when designing such environments is to provide user interactions that are adapted to the tasks performed by the users. Thus, we propose here a task-centred methodology to design and evaluate these user interactions. Our methodology allows for the determination of user interaction designs based on previous VR studies, and for user evaluations based on a task-related computation of usability. Here, we applied it on the hazard identification case study, since VR can be used in a preventive approach to improve worksite safety. Once this task and its related user interactions were analysed with our methodology, we obtained two possible designs of interaction techniques for the worksite exploration subtask. About their usability evaluation, we proposed in this study to compare our task-centred evaluation approach to a non-task-centred one. Our hypothesis was that our approach could lead to different interpretations of user study results than a non-task-centred one. Our results confirmed our hypothesis by comparing weighted usability scores from our task-centred approach to unweighted ones for our two interaction techniques.Conseil régional de Bourgogne-Franche-Comté (reference: CDBOBI

Twenty-year follow-up of kangaroo mother care versus traditional care

Q1Q1e20162063BACKGROUND AND OBJECTIVES: Kangaroo mother care (KMC) is a multifaceted intervention for preterm and low birth weight infants and their parents. Short- and mid-term benefits of KMC on survival, neurodevelopment, breastfeeding, and the quality of mother–infant bonding were documented in a randomized controlled trial (RCT) conducted in Colombia from 1993 to 1996. The aim of the present study was to evaluate the persistence of these results in young adulthood. METHODS: From 2012 to 2014, a total of 494 (69%) of the 716 participants of the original RCT known to be alive were identified; 441 (62% of the participants in the original RCT) were re-enrolled, and results for the 264 participants weighing ≤1800 g at birth were analyzed. The KMC and control groups were compared for health status and neurologic, cognitive, and social functioning with the use of neuroimaging, neurophysiological, and behavioral tests. RESULTS: The effects of KMC at 1 year on IQ and home environment were still present 20 years later in the most fragile individuals, and KMC parents were more protective and nurturing, reflected by reduced school absenteeism and reduced hyperactivity, aggressiveness, externalization, and socio-deviant conduct of young adults. Neuroimaging showed larger volume of the left caudate nucleus in the KMC group. CONCLUSIONS: This study indicates that KMC had significant, long-lasting social and behavioral protective effects 20 years after the intervention. Coverage with this efficient and scientifically based health care intervention should be extended to the 18 million infants born each year who are candidates for the method

An Uncertainty-Aware Visual System for Image Pre-Processing

Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image’s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets

VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies

Visual data analysis helps to understand different types of phenomena by allowing experts to explore for relationships, patterns, outliers, unexpected changes, and more. Experts need tools that help them find useful and actionable information in the data so that they can test their hypotheses and develop new ones. This need becomes more evident in longitudinal studies, where there are usually a large number of variables and the process being analyzed can be complex as well. We present VALS (Visual Analytics in Longitudinal Studies), a framework for visually exploring longitudinal clinical data. VALS includes a data model, a task categorization model, and an approach to guidance through feature engineering techniques and interactive visualizations, all of which help analysts perform their analysis tasks. VALS was designed in collaboration with healthcare experts with experience in longitudinal studies. We have also developed a tool prototype for a case study using real-world datasets. The evidence collected in the case study shows the usefulness of a VALS-based visual analytics tool

VafusQ: A methodology to build visual analysis applications with data quality features

The use of interactive applications to support the decision-making process is more common every day. However, a huge amount of data is required in order to make more informed decisions. Fortunately, with the arrival of new technologies there are many data sources available. This requirement of data causes heterogeneity and data quality problems. A set of data quality problems are reduced in the preprocessing stage. However, many data quality issues persist after the preprocessing stage. For this reason, we proposed a methodology to take the data quality problems, to represent them and simultaneously support the analysis process. In addition, an application is developed as a use case of the methodology by analyzing the public transport system in Bogotá. Furthermore, a case study is performed to test the usefulness of the developed application. As a result, the methodology made possible the development of interactive visualizations that constitute an application that is useful to achieve the analysis tasks by including data quality features

An Uncertainty-Aware Visual System for Image Pre-Processing

Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image’s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets

An Uncertainty-Aware Visual Systemfor Image Pre-Processing

Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image’s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets