arXiv subjects

Denis Gracanin

Publications and source records attributed to Denis Gracanin.

15 recordsLinked to original sources

arXiv2026

Adaptive Captioning with Emotional Cues: Supporting DHH and Neurodivergent Learners in STEM

Real-time captioning is vital for Deaf and Hard of Hearing (DHH) and neurodivergent learners (e.g., those with ADHD), yet it often omits emotional and non-verbal cues essential for comprehension. This omission is particularly consequential in STEM education, where cognitively demanding material can exacerbate the challenges faced by caption users across diverse ability profiles. In this paper, we present a design-oriented exploration of four captioning prototypes that embed emotional and multimodal cues, including facial expressions, body gestures, keyword highlighting, and emoji. Across a pilot and a main study with 24 participants, we found that certain prototypes reduced self-reported cognitive load and improved comprehension scores compared to traditional captions. Qualitative feedback reveals the importance of customizable caption features to accommodate neurodivergent users' preferences (e.g., ADHD or different levels of comfort with emojis). Our findings contribute to ongoing conversations in accessible technology research about how best to integrate emotional cues into captions in a way that is both usable and beneficial for a wide range of learners.

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arXiv2025

CrossSet: Unveiling the Complex Interplay of Two Set-typed Dimensions in Multivariate Data

The interactive visual analysis of set-typed data, i.e., data with attributes that are of type set, is a rewarding area of research and applications. Valuable prior work has contributed solutions that enable the study of such data with individual set-typed dimensions. In this paper, we present CrossSet, a novel method for the joint study of two set-typed dimensions and their interplay. Based on a task analysis, we describe a new, multi-scale approach to the interactive visual exploration and analysis of such data. Two set-typed data dimensions are jointly visualized using a hierarchical matrix layout, enabling the analysis of the interactions between two set-typed attributes at several levels, in addition to the analysis of individual such dimensions. CrossSet is anchored at a compact, large-scale overview that is complemented by drill-down opportunities to study the relations between and within the set-typed dimensions, enabling an interactive visual multi-scale exploration and analysis of bivariate set-typed data. Such an interactive approach makes it possible to study single set-typed dimensions in detail, to gain an overview of the interaction and association between two such dimensions, to refine one of the dimensions to gain additional details at several levels, and to drill down to the specific interactions of individual set-elements from the set-typed dimensions. To demonstrate the effectiveness and efficiency of CrossSet, we have evaluated the new method in the context of several application scenarios.

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arXiv2025

Transcending Dimensions using Generative AI: Real-Time 3D Model Generation in Augmented Reality

Traditional 3D modeling requires technical expertise, specialized software, and time-intensive processes, making it inaccessible for many users. Our research aims to lower these barriers by combining generative AI and augmented reality (AR) into a cohesive system that allows users to easily generate, manipulate, and interact with 3D models in real time, directly within AR environments. Utilizing cutting-edge AI models like Shap-E, we address the complex challenges of transforming 2D images into 3D representations in AR environments. Key challenges such as object isolation, handling intricate backgrounds, and achieving seamless user interaction are tackled through advanced object detection methods, such as Mask R-CNN. Evaluation results from 35 participants reveal an overall System Usability Scale (SUS) score of 69.64, with participants who engaged with AR/VR technologies more frequently rating the system significantly higher, at 80.71. This research is particularly relevant for applications in gaming, education, and AR-based e-commerce, offering intuitive, model creation for users without specialized skills.

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arXiv2025

From Voices to Worlds: Developing an AI-Powered Framework for 3D Object Generation in Augmented Reality

This paper presents Matrix, an advanced AI-powered framework designed for real-time 3D object generation in Augmented Reality (AR) environments. By integrating a cutting-edge text-to-3D generative AI model, multilingual speech-to-text translation, and large language models (LLMs), the system enables seamless user interactions through spoken commands. The framework processes speech inputs, generates 3D objects, and provides object recommendations based on contextual understanding, enhancing AR experiences. A key feature of this framework is its ability to optimize 3D models by reducing mesh complexity, resulting in significantly smaller file sizes and faster processing on resource-constrained AR devices. Our approach addresses the challenges of high GPU usage, large model output sizes, and real-time system responsiveness, ensuring a smoother user experience. Moreover, the system is equipped with a pre-generated object repository, further reducing GPU load and improving efficiency. We demonstrate the practical applications of this framework in various fields such as education, design, and accessibility, and discuss future enhancements including image-to-3D conversion, environmental object detection, and multimodal support. The open-source nature of the framework promotes ongoing innovation and its utility across diverse industries.

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arXiv2025

Wearable Meets LLM for Stress Management: A Duoethnographic Study Integrating Wearable-Triggered Stressors and LLM Chatbots for Personalized Interventions

We use a duoethnographic approach to study how wearable-integrated LLM chatbots can assist with personalized stress management, addressing the growing need for immediacy and tailored interventions. Two researchers interacted with custom chatbots over 22 days, responding to wearable-detected physiological prompts, recording stressor phrases, and using them to seek tailored interventions from their LLM-powered chatbots. They recorded their experiences in autoethnographic diaries and analyzed them during weekly discussions, focusing on the relevance, clarity, and impact of chatbot-generated interventions. Results showed that even though most events triggered by the wearable were meaningful, only one in five warranted an intervention. It also showed that interventions tailored with brief event descriptions were more effective than generic ones. By examining the intersection of wearables and LLM, this research contributes to developing more effective, user-centric mental health tools for real-time stress relief and behavior change.

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arXiv2024

Multilingual Standalone Trustworthy Voice-Based Social Network for Disaster Situations

In disaster scenarios, effective communication is crucial, yet language barriers often hinder timely and accurate information dissemination, exacerbating vulnerabilities and complicating response efforts. This paper presents a novel, multilingual, voice-based social network specifically designed to address these challenges. The proposed system integrates advanced artificial intelligence (AI) with blockchain technology to enable secure, asynchronous voice communication across multiple languages. The application operates independently of external servers, ensuring reliability even in compromised environments by functioning offline through local networks. Key features include AI-driven real-time translation of voice messages, ensuring seamless cross-linguistic communication, and blockchain-enabled storage for secure, immutable records of all interactions, safeguarding message integrity. Designed for cross-platform use, the system offers consistent performance across devices, from mobile phones to desktops, making it highly adaptable in diverse disaster situations. Evaluation metrics demonstrate high accuracy in speech recognition and translation, low latency, and user satisfaction, validating the system's effectiveness in enhancing communication during crises. This solution represents a significant advancement in disaster communication, bridging language gaps to support more inclusive and efficient emergency response.

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arXiv2024

Interactive Design-of-Experiments: Optimizing a Cooling System

The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space. The extent of the parameter space, the complexity of the non-linear model of the system, as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other. The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation. When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.

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arXiv2024

Narrative Review of Emotional Expression Support in XR: Psychophysiology of Speech-to-Text Interfaces

This narrative review examines recent advancements, limitations, and research gaps in integrating emotional expression into speech-to-text (STT) interfaces within extended reality (XR) environments. Drawing from 37 peer-reviewed studies published between 2020 and 2024, we synthesized literature across multiple domains, including affective computing, psychophysiology, captioning innovation, and immersive human-computer interaction. Thematic categories include communication enhancement technologies for Deaf and Hard of Hearing (DHH) users, emotive captioning strategies, visual and affective augmentation in AR/VR, speech emotion recognition, and the development of empathic systems. Despite the growing accessibility of real-time STT tools, such systems largely fail to convey affective nuance, limiting the richness of communication for DHH users and other caption consumers. This review highlights emerging approaches such as animated captions, emojilization, color-coded overlays, and avatar-based emotion visualization, but finds a persistent gap in real-time emotion-aware captioning within immersive XR contexts. We identify key research opportunities at the intersection of accessibility, XR, and emotional expression, and propose future directions for the development of affect-responsive, user-centered captioning interfaces.

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arXiv2024

Extended Reality for Smart Built Environments Design: Smart Lighting Design Testbed

Smart Built Environment is an eco-system of `connected' and `smart' Internet of Things (IoT) devices that are embedded in a built environment. Smart lighting is an important category of smart IoT devices that has recently attracted research interest, particularly for residential areas. In this paper, we present an extended reality based smart lighting design testbed that can generate design prototypes based on the functionality of the physical environment. The emphasis is on designing a smart lighting system in a controlled residential environment, with some evaluation of well-being and comfort.

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arXiv2024

Systematic Review of Extended Reality for Smart Built Environments Lighting Design Simulations

This systematic literature review paper explores the use of extended reality {(XR)} technology for smart built environments and particularly for smart lighting systems design. Smart lighting is a novel concept that has emerged over a decade now and is being used and tested in commercial and industrial built environments. We used PRISMA methodology to review 270 research papers published from 1968 to 2023. Following a discussion of historical advances and key modeling techniques, a description of lighting simulation in the context of extended reality and smart built environment is given, followed by a discussion of the current trends and challenges.

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arXiv2024

A digital twin based approach to smart lighting design

Lighting has a critical impact on user mood and behavior, especially in architectural settings. Consequently, smart lighting design is a rapidly growing research area. We describe a digital twin-based approach to smart lighting design that uses an immersive virtual reality digital twin equivalent (virtual environment) of the real world, physical architectural space to explore the visual impact of light configurations. The CLIP neural network is used to obtain a similarity measure between a photo of the physical space with the corresponding rendering in the virtual environment. A case study was used to evaluate the proposed design process. The obtained similarity value of over 87% demonstrates the utility of the proposed approach.

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arXiv2024

Attention and Sensory Processing in Augmented Reality: Empowering ADHD population

The brain's attention system is a complex and adaptive network of brain regions that enables individuals to interact effectively with their surroundings and perform complex tasks. This system involves the coordination of various brain regions, including the prefrontal cortex and the parietal lobes, to process and prioritize sensory information, manage tasks, and maintain focus. In this study, we investigate the intricate mechanisms underpinning the brain's attention system, followed by an exploration within the context of augmented reality (AR) settings. AR emerges as a viable technological intervention to address the multifaceted challenges faced by individuals with Attention Deficit Hyperactivity Disorder (ADHD). Given that the primary characteristics of ADHD include difficulties related to inattention, hyperactivity, and impulsivity, AR offers tailor-made solutions specifically designed to mitigate these challenges and enhance cognitive functioning. On the other hand, if these ADHD-related issues are not adequately addressed, it could lead to a worsening of their condition in AR. This underscores the importance of employing effective interventions such as AR to support individuals with ADHD in managing their symptoms. We examine the attentional mechanisms within AR environments and the sensory processing dynamics prevalent among the ADHD population. Our objective is to comprehensively address the attentional needs of this population in AR settings and offer a framework for designing cognitively accessible AR applications.

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arXiv2024

Empowering Mobility: Brain-Computer Interface for Enhancing Wheelchair Control for Individuals with Physical Disabilities

The integration of brain-computer interfaces (BCIs) into the realm of smart wheelchair (SW) technology signifies a notable leap forward in enhancing the mobility and autonomy of individuals with physical disabilities. BCIs are a technology that enables direct communication between the brain and external devices. While BCIs systems offer remarkable opportunities for enhancing human-computer interaction and providing mobility solutions for individuals with disabilities, they also raise significant concerns regarding security, safety, and privacy that have not been thoroughly addressed by researchers on a large scale. Our research aims to enhance wheelchair control for individuals with physical disabilities by leveraging electroencephalography (EEG) signals for BCIs. We introduce a non-invasive BCI system that utilizes a neuro-signal acquisition headset to capture EEG signals. These signals are obtained from specific brain activities that individuals have been trained to produce, allowing for precise control of the wheelchair. EEG-based BCIs are instrumental in capturing the brain's electrical activity and translating these signals into actionable commands. The primary objective of our study is to demonstrate the system's capability to interpret EEG signals and decode specific thought patterns or mental commands issued by the user. By doing so, it aims to convert these into accurate control commands for the wheelchair. This process includes the recognition of navigational intentions, such as moving forward, backward, or executing turns, specifically tailored for wheelchair operation. Through this innovative approach, we aim to create a seamless interface between the user's cognitive intentions and the wheelchair's movements, enhancing autonomy and mobility for individuals with physical disabilities.

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arXiv2022

Interactive Visual Analysis of Structure-borne Noise Data

Numerical simulation has become omnipresent in the automotive domain, posing new challenges such as high-dimensional parameter spaces and large as well as incomplete and multi-faceted data. In this design study, we show how interactive visual exploration and analysis of high-dimensional, spectral data from noise simulation can facilitate design improvements in the context of conflicting criteria. Here, we focus on structure-borne noise, i.e., noise from vibrating mechanical parts. Detecting problematic noise sources early in the design and production process is essential for reducing a product's development costs and its time to market. In a close collaboration of visualization and automotive engineering, we designed a new, interactive approach to quickly identify and analyze critical noise sources, also contributing to an improved understanding of the analyzed system. Several carefully designed, interactive linked views enable the exploration of noises, vibrations, and harshness at multiple levels of detail, both in the frequency and spatial domain. This enables swift and smooth changes of perspective; selections in the frequency domain are immediately reflected in the spatial domain, and vice versa. Noise sources are quickly identified and shown in the context of their neighborhood, both in the frequency and spatial domain. We propose a novel drill-down view, especially tailored to noise data analysis. Split boxplots and synchronized 3D geometry views support comparison tasks. With this solution, engineers iterate over design optimizations much faster, while maintaining a good overview at each iteration. We evaluated the new approach in the automotive industry, studying noise simulation data for an internal combustion engine.

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arXiv2019

A Comparison of Radial and Linear Charts for Visualizing Daily Pattern

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts - even for visualizing periodical daily patterns.

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