Trust anchors financial markets, which directly contribute to the foundation of global economies, and simultaneously fuel FinTech innovations. AI-driven tools such as Robo-Advisors, Equity crowdfunding, and Peer-To-pe...
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This study introduces a VR-based breathing and relaxation exergame tailored for individuals with Duchenne muscular dystrophy (DMD). DMD is a rare neuromuscular disease that leads to respiratory muscle dysfunction with...
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ISBN:
(纸本)9798350348392
This study introduces a VR-based breathing and relaxation exergame tailored for individuals with Duchenne muscular dystrophy (DMD). DMD is a rare neuromuscular disease that leads to respiratory muscle dysfunction with anxiety being a common comorbidity. Clinical management requires frequent visits to rare disease specialists to manage symptom progression. Limited availability and/or proximity of rare disease experts present challenges to care and can lead to missed care opportunities and reduced quality of life. We propose a breathing and relaxation exergame with remote telehealth applicability that incorporates shared patient -clinician VR interaction, and physiological sensors that provide both real-time feedback to the patient and health analytics for the clinician. The game focuses on two key aspects of DMD clinical care that can be mediated through control of breathing- relaxation/mindfulness training and respiratory muscle exercise. The system was evaluated among 13 individuals, including 4 participants with DMD. Feedback surveys, interviews, and focus group discussions with participants, accompanying family members, and clinicians demonstrated the feasibility of this VR tool for telehealth or as part of a home exercise program.
The human genome is incredibly information-rich, consisting of approximately 25,000 protein-coding genes spread out over 3.2 billion nucleotide base pairs contained within 24 unique chromosomes. The genome is importan...
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ISBN:
(纸本)9798350328387
The human genome is incredibly information-rich, consisting of approximately 25,000 protein-coding genes spread out over 3.2 billion nucleotide base pairs contained within 24 unique chromosomes. The genome is important in maintaining spatial context, which assists in understanding gene interactions and relationships. However, existing methods of genome visualization that utilize spatial awareness are inefficient and prone to limitations in presenting gene information and spatial context. This study proposed an innovative approach to genome visualization and exploration utilizing virtual reality. To determine the optimal placement of gene information and evaluate its essentiality in a VR environment, we implemented and conducted a user study with three different interaction methods. Two interaction methods were developed in virtual reality to determine if gene information is better suited to be embedded within the chromosome ideogram or separate from the ideogram. The final ideogram interaction method was performed on a desktop and served as a benchmark to evaluate the potential benefits associated with the use of VR. Our study findings reveal a preference for VR, despite longer task completion times. In addition, the placement of gene information within the visualization had a notable impact on the ability of a user to complete tasks. Specifically, gene information embedded within the chromosome ideogram was better suited for single target identification and summarization tasks, while separating gene information from the ideogram better supported region comparison tasks.
Users are often interested in exploring ranks over time data to compare the performance or ranking of multiple observations with respect to each other. However, predominant visualization techniques suffer from a high ...
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We proposed a high-dynamic-range virtual reality (VR) display that can represent a glossy material by combining a conventional frontal projection mapping and a retinal projection light passing through a screen of a mi...
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ISBN:
(纸本)9798350348392
We proposed a high-dynamic-range virtual reality (VR) display that can represent a glossy material by combining a conventional frontal projection mapping and a retinal projection light passing through a screen of a microporous plate. In the prototype system, the retinal projection could be superimposed on the frontal projection and increase the luminance of only the specular highlight in the image. This paper also reports approximately six hundred times brighter presentation of the retinal projection than that of the frontal projection.
In AR applications, the jitter of virtual objects can weaken the sense of integration with the real environment. This jitter is often caused by noise in the pose obtained by 3D tracking or localization methods, especi...
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ISBN:
(纸本)9798350328387
In AR applications, the jitter of virtual objects can weaken the sense of integration with the real environment. This jitter is often caused by noise in the pose obtained by 3D tracking or localization methods, especially in monocular vision systems without IMU support. Filtering the pose is an effective method to eliminate jitter, however, it can also cause significant lag in the filtered pose, seriously degrading the AR experience. Existing filters struggle to simultaneously reduce jitter while maintaining low lag. In this paper, we propose a novel Minilag filter, which achieves excellent pose smoothing while significantly reducing the lag through backtracking update and compensation strategies, and has excellent real-time performance. We represent the rotation in the pose in the Lie algebra and filter it in locally Euclidean space, ensuring that the filtering of rotation is consistent with that of vectors. We also analyze the noise distribution and characteristics in the tracked pose, providing a theoretical basis for setting filter parameters. We evaluated the proposed filter using both objective mathematical metrics and a user study, and the experimental results demonstrate that our method achieves state-of-the-art performance.
Even though the analysis of unsteady 2D flow fields is challenging, fluid mechanics experts generally have an intuition on where in the simulation domain specific features are expected. Using this intuition, showing s...
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ISBN:
(纸本)9798350325577
Even though the analysis of unsteady 2D flow fields is challenging, fluid mechanics experts generally have an intuition on where in the simulation domain specific features are expected. Using this intuition, showing similar regions enables the user to discover flow patterns within the simulation data. When focusing on similarity, a solid mathematical framework for a specific flow pattern is not required. We propose a technique that visualizes similar and dissimilar regions with respect to a region selected by the user. Using infinitesimal strain theory, we capture the strain and rotation progression and therefore the dynamics of fluid parcels along pathlines, which we encode as distributions. We then apply the Jensen-Shannon divergence to compute the (dis)similarity between pathline dynamics originating in a user-defined flow region and the pathline dynamics of the flow field. We validate our method by applying it to two simulation datasets of two-dimensional unsteady flows. Our results show that our approach is suitable for analyzing the similarity of time-dependent flow fields.
Single-cell RNA sequencing (scRNA-seq) is becoming popular in studying the gene expression of cells at the single-cell level. ScRNA-seq enables analysts to characterize cell types, thereby providing a better understan...
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ISBN:
(纸本)9798350321241
Single-cell RNA sequencing (scRNA-seq) is becoming popular in studying the gene expression of cells at the single-cell level. ScRNA-seq enables analysts to characterize cell types, thereby providing a better understanding of dynamic biological processes. In scRNA-seq data analysis, principal component analysis (PCA) is commonly used to reduce at least thousands of dimensions in the raw data to a manageable size so that analysts can visualize and cluster cells to identify different cell types. The conventional process to determine the optimal dimensionality includes a laborious manual review of hundreds of different projection plots. To address this problem, we introduce a dimensionality explorer for single-cell analysis, which is a visualization system that helps analysts to effectively determine the optimal dimensionality of scRNA-seq data. It employs a hull heatmap, which provides a holistic view of overlaps among multiple cell types across various dimensionalities using a convex hull-embedded color map. The hull heatmap effectively reduces the burden of manually reviewing hundreds of projection plots to determine the optimal dimensionality. Our system also provides interactive gene expression level visualization and intuitive lasso selection, thereby allowing analysts to progressively refine the convex hulls of the hull heatmap. We demonstrate the usefulness of the proposed system through a user study and three case studies conducted by domain experts.
To enable eXtended Reality (XR) remote collaboration with copresence, it is necessary to overcome the spatial difference between remote spaces and create a single mutual space where users can utilize their physical ob...
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ISBN:
(纸本)9798350348392
To enable eXtended Reality (XR) remote collaboration with copresence, it is necessary to overcome the spatial difference between remote spaces and create a single mutual space where users can utilize their physical objects. Previous studies mainly consider semantic information and walkable area matching rate to create mutual space in a 1:1 scale, making it challenging to precisely match edge information between physical and virtual objects. In this work, we. propose an edge-aware mutual space creation method by adjusting the VR client's space with Redirected Walking (RDW) technology. In our previous works, we introduced Relative Translation Gains (RTGs), a length -based RDW, and estimated the cognitive thresholds range of RTGs depending on the configuration of virtual space (size, object existence, spatial layout) to make an effect of resealing VR clients' spaces. In addition, we introduced an RTG-based mutual space generation method that allows VR and AR users to interact with objects in physical space while they collaborate remotely. We will develop an enhanced mutual space generation method and conduct a user study, and our findings can be used to generate a mutual space for asymmetric remote collaboration.
Electronic health records (EHRs), serving as patient-centered repositories for medical data, offer the opportunity for researchers to uncover concealed patterns using machine learning (ML). However, in real-world medi...
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ISBN:
(纸本)9798350330243
Electronic health records (EHRs), serving as patient-centered repositories for medical data, offer the opportunity for researchers to uncover concealed patterns using machine learning (ML). However, in real-world medical settings, clinicians often face the task of selecting pertinent feature dimensions from a range of potential medical metrics and then deducing potential labels from vague diagnostic descriptions, prior to the modeling phase. This complexity presents challenges in obtaining reliable training/testing data and conducting thorough analysis. Consequently, these hurdles hinder the practical application of ML for automated modeling and comprehensible interpretation of influencing factors. To tackle these challenges, we introduce a visual analytics approach designed to navigate the feature and label space within EHRs, while also streamlining the modeling process through automated ML algorithms and techniques for improved interpretability.
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