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检索条件"任意字段=Conference on Visualization in Biomedical Computing"

共 897 条记录，以下是11-20 订阅

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A Fiber-optic probe-based Raman imaging approach in combination with AR/MR visualization 3

A Fiber-optic probe-based Raman imaging approach in combinat...

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3rd International computing Imaging conference, CITA 2023

作者： Yang, Wei Liu, Qian Schie, Iwan W. Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical Engineering Hainan University Renmin Avenue 58 Haikou570228 China Leibniz Institute of Photonic Technology Jena Albert-Einstein-Strasse 9 Jena07745 Germany

ISBN: (纸本)9781510671409

In this paper, we summarize our previous relevant works and demonstrate various approaches to overcome the common drawbacks of applying Raman probes for many applications. A handheld fiber-optic Raman probe with an autofocus unit was presented to overcome the problem arising from using fixed-focus lenses, by using a liquid lens as the objective lens, which allows dynamical adjustment of the focal length of the probe. An implementation of a computer vision-based positional tracking to co-register the regular Raman spectroscopic measurements with the spatial location enables fast recording of a Raman image from a large tissue sample by combining positional tracking of the laser spot through brightfield images. The visualization of the Raman image has been extended to augmented and mixed reality and combined with a 3D reconstruction method and projector-based visualization to offer an intuitive and easily understandable way of presenting the Raman image. All these advances are substantial and highly beneficial to further drive the clinical translation of Raman spectroscopy as potential image-guided instrumentation. © 2023 SPIE.

关键词： Augmented reality

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Empowering Non-Expert Users in Fashion Remanufacturing: Enhancing Human-Multi-Robot Interaction through Real-Time visualization

Empowering Non-Expert Users in Fashion Remanufacturing: Enha...

引用

2025 CHI conference on Human Factors in computing Systems, CHI EA 2025

作者： Gollob, Emanuel Bastan, Amir Braumann, Johannes Luible-Baer, Christiane Creative Robotics University of Arts Linz Linz Austria University of Arts Linz Linz Austria

ISBN: (纸本)9798400713958

Automating textile repair and remanufacturing can significantly improve the ecological and societal impact of textiles. However, the complex behavior of textiles poses challenges for integrating robotics into fashion. This research addresses these challenges by developing a platform that empowers non-experts to define complex multi-robot tasks through visualization-enhanced hand guiding. By leveraging collaborative automation, our approach facilitates intuitive human-multi-robot interaction without extensive technical knowledge. Our approach employs flexible, parametric systems and a dual-robot setup to achieve high precision and operational flexibility in remanufacturing processes. Utilizing Grasshopper for path planning and VVVV for real-time interaction, we create a user-friendly interface allowing non-experts to interact intuitively with robots. This work demonstrates how real-time visualization can make advanced robotic capabilities accessible to non-expert users in the fashion industry. By enabling non-experts to leverage these technologies, we aim to transform remanufacturing processes and foster innovation in human-robot collaboration in the fashion and textiles sector. © 2025 Copyright held by the owner/author(s).

关键词： Multipurpose robots

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Personalized Compression Therapy Plans using Cloud computing and ML Analytics

Personalized Compression Therapy Plans using Cloud Computing...

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2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Srinivasan, S. Pandey, Pramod Arulprakash, M. Umapathy, K. Amutha, R. Rajmohan, M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communications Engineering Tamil Nadu Kanchipuram India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

For the treatment of diseases like lymphedema and chronic venous insufficiency, compression therapy is crucial, but conventional methods frequently produce less-than-ideal results because they are impersonal. This paper presents a novel approach that uses cloud computing and decision trees to create personalized compression therapy plans in healthcare settings. The proposed system uses cloud infrastructure's scalability and computational power to handle massive amounts of patient data collected in real-time from wearable Internet of Things (IoT) devices. Through device integration, pertinent physiological parameters can be continuously monitored, allowing prompt therapy plan modifications in response to changing patient requirements. Decision Trees, a machine learning (ML) algorithm selected for its capacity to comprehend complicated datasets and produce useful insights, are essential to the system's operation. Decision trees dynamically customize compression therapy regimens using patient data analysis to maximize patient comfort and treatment effectiveness. Early system trials have produced encouraging outcomes, including notable increases in patient satisfaction and treatment outcomes. This strategy prioritizes the delivery of personalized healthcare, which increases therapeutic efficacy and fosters a patient-centered treatment experience. The combination of cloud computing and Decision Trees is a progressive step forward in the healthcare industry, providing scalable solutions that can be tailored to the specific needs of each patient and method for future developments in personalized medical care. © 2025 IEEE.

关键词： Personalized medicine

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Comparison of Machine Learning Models for Injury Prediction in Athletes

Comparison of Machine Learning Models for Injury Prediction ...

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Murugan, Suganiya Kumar, S. Pradeep Kalaiarasi, G. Saritha, B. Rubini, B. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Vels Institute of Science Technology and Advanced Studies Department of Eee Tamil Nadu Chennai India Vignan Foundation for Science Technology and Research Department of Acse Andhrapradesh India Erode Sengunthar Engineering College Thudupathi Erode Department of Biomedical Engineering Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Eee TamilNadu Chennai India

ISBN: (纸本)9798331521394

Running is a favoured exercise for millions, but it carries a risk of musculoskeletal injuries, particularly to the lower extremities. Identifying and predicting potential injuries is crucial for prevention. This study explores the use of machine learning techniques to predict injuries among runners, leveraging data on demographics, running habits, health conditions, and previous injuries. Using models like Random Forest, K-Nearest Neighbours, Decision Trees, and Logistic Regression, the study examines the relevance of various attributes and assesses the predictive power of these models. Remarkably, our model achieved an impressive accuracy of over 99%, setting a new benchmark for predictive precision in injury prevention. visualizations provide insight into attribute interactions and their influence on injury risk, facilitating the development of targeted prevention strategies. © 2025 IEEE.

关键词： Logistic regression

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Promoting Physical Exercise Adherence and Safety in Virtual Reality Using IoT Architecture 15

Promoting Physical Exercise Adherence and Safety in Virtual ...

引用

15th IEEE Annual computing and Communication Workshop and conference, CCWC 2025

作者： Tsai, Te-Yi Smiley, Aref Finkelstein, Joseph University of Utah School of Medicine Dept. Biomedical Informatics Salt Lake CityUT United States

ISBN: (纸本)9798331507695

Immersive virtual reality (VR) can enhance the cycling exercise experience and promote adherence to daily physical activity. The goal of this project was to facilitate exercise adherence and safety by implementing real-time visualization of cycling trajectory and oxygen saturation within a three-dimensional space generated by an interactive VR app. Our implementation included an Oculus headset and a Metaverse application for a racetrack game, leveraging IoT infrastructure to collect and process real-time data from a portable exercise bike and wireless pulse oximeter. The IoT interface facilitated transmission, storage, and visualization of exercise bike speed within the VR environment. The system wirelessly interfaced with a portable bike and pulse oximeter, enabling real-time monitoring of exercise intensity, heart rate, and oxygen saturation. These parameters were continuously displayed on the VR dashboard during the exercise. A dedicated web portal supported individualized exercise program prescriptions and adjustments based on patient performance. Real-time alerts based on personalized thresholds ensured adherence to exercise prescriptions and patient safety. Our prototype demonstrated the potential of Metaverse applications using IoT infrastructure for cost-effective home-based telerehabilitation. Future enhancements include wireless physiological sensors, vital sign monitors, and remote communication with clinical rehabilitation teams. A randomized clinical trial will evaluate system efficacy. © 2025 IEEE.

关键词： Oximeters

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Cloud-Enabled Regression Model for Enhanced Patient Authentication Systems

Cloud-Enabled Regression Model for Enhanced Patient Authenti...

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2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Sindhuja, K. Thanalakshmi, S. Jai Ganesh, B. Muthumarilakshmi, S. Ganeshbabu, T.R. Sujatha, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Gojan School of Business and Technology Department of Management Studies Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Protecting private health information and facilitating easy access to healthcare services depends on reliable patient authentication. Improving patient authentication systems via cloud computing and Artificial Intelligence (AI) approaches is the main topic of this paper. It uses linear regression (LR) models to provide a scalable solution that uses big datasets to enhance authentication efficiency and accuracy by using cloud-based platforms. LR examines several authentication elements to predict and validate patient identities, such as user behavior patterns and biometric data. The proposed solution aims to reduce authentication failures and mitigate possible security concerns while preserving a user-friendly experience. With better performance in terms of authentication accuracy and system responsiveness, experimental findings reveal that the cloud enabled strategy is beneficial. This system provides valuable information for future research on strong patient authentication systems and shows how cloud computing and AI may work together to improve healthcare security. © 2025 IEEE.

关键词： Sensitive data

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Analysis of Data visualization Techniques Useful for Machine Learning and Visual Reality 2

Analysis of Data Visualization Techniques Useful for Machine...

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2nd International conference on Edge computing and Applications, ICECAA 2023

作者： Singh, Gurpreet Kumar Singh, Subham Chandigarh University Department of Cse Mohali India Chandigarh University Department of Mathematics Mohali India

ISBN: (纸本)9798350347579

Throughout the past couple of decades, machine learning (ML) has made its way into scientific research and engineering. Machine learning (ML) strategies are widely employed in processing information, data mining, especially scientific computation. Data visualization is essential. Despite the fact that numerous types of visualization tools are commonly used, the majority of them need sufficient coding knowledge, are developed for specific purposes, or are not free. Virtual reality (VR) provides intuitive interactivity and comprehensive visualization. Researchers use virtual reality to make it possible for any biomedical specialist to use a machine learning (DL) framework for picture analysis. Although ML models can be effective instruments for assessing information, they can additionally be difficult to comprehend and create. We have developed a ML development system based on virtual reality in order to render the technology more user-friendly and approachable. The intuitive interactivity and vivid visualisation are offered by virtual reality (VR). Any technical discipline can create a machine learning (ML) approach to recognising pictures using VR. This paper offers a thorough analysis of ML visualisation techniques, resources, and procedures. By looking at the visual analytical pipeline customers, and researchers place data visualisation into the visual analytics methodology. It present an analysis of the many chart types that are available for data visualisation and discuss guidelines for using each one while taking into account the unique circumstances of the given utilise case. There look more closely at a few of the latest and greatest exciting visualisation tools. We research visualisation challenges in each domain because each ML model is unique in terms to VR strategies. Finally, we present a summary of the main difficulties with ML visualisations. © 2023 IEEE.

关键词： visualization

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Classification of optical defects in spherical mirrors from digital images of Ronchi interference patterns using convolutional neural networks

Classification of optical defects in spherical mirrors from ...

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2023 IEEE Colombian conference on Communications and computing, COLCOM 2023

作者： Rey, Andres Jimenez, Andres-F Cruz-Roa, Angel Universidad de Los Llanos Estudiante de Ingeniería de Sistemas Gitecx & AdaLab Facultad de Ciencias Básicas e Ingeniería Villavicencio Colombia Universidad de Los Llanos Sistemas Dinámicos & FarmTechnology Facultad de Ciencias Básicas e Ingeniería Villavicencio Colombia Universidad de Los Llanos Gitecx & AdaLab Facultad de Ciencias Básicas e Ingeniería Villavicencio Colombia

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Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics 3rd

Quantifying Knee Cartilage Shape and Lesion: From Image to...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Yao, Yongcheng Chen, Weitian UKRI CDT in Biomedical AI School of Informatics The University of Edinburgh Edinburgh United Kingdom Department of Imaging and Interventional Radiology The Chinese University of Hong Kong China

ISBN: (纸本)9783031820069

Imaging features of knee articular cartilage have been shown to be potential imaging biomarkers for knee osteoarthritis. Despite recent methodological advancements in image analysis techniques like image segmentation, registration, and domain-specific image computing algorithms, only a few works focus on building fully automated pipelines for imaging feature extraction. In this study, we developed a deep-learning-based medical image analysis application for knee cartilage morphometrics, CartiMorph Toolbox (CMT). We proposed a 2-stage joint template learning and registration network, CMT-reg. We trained the model using the OAI-ZIB dataset and assessed its performance in template-to-image registration. The CMT-reg demonstrated competitive results compared to other state-of-the-art models. We integrated the proposed model into an automated pipeline for the quantification of cartilage shape and lesion (full-thickness cartilage loss, specifically). The toolbox provides a comprehensive, user-friendly solution for medical image analysis and data visualization. The software and models are available at https://***/YongchengYAO/CMT-AMAI24paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cartilage

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Integrating Cloud computing with IoT for Battery Monitoring in Electric Vehicles 2

Integrating Cloud Computing with IoT for Battery Monitoring ...

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2nd International conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Selvi, J. Sathyanathan, P. Priya, S. Balaji Damodhar, T.S. Sujatha, S. Department of Civil Engineering Shree Sathyam College of Engineering and Technology Sankari Tamil Nadu Salem India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Deemed to be University Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Cloud computing and the Internet of Things (IoT) for battery monitoring in electric vehicles (EVs) can improve battery performance and efficiency. EV batteries, IoT devices, cloud infrastructure, data transmission, storage, processing, analysis, visualization, user interface, and integration with EV management systems are used in this integration. These components are integrated to provide a complete system for real-time monitoring, predictive maintenance, and performance improvement of EV batteries. Sensors are essential to this system. The battery's electrical characteristics, temperature, environmental conditions, and potential concerns are collected via voltage, current, and temperature sensors. The technology improves battery performance, lifespan, real-time monitoring and alarms, predictive maintenance insights, and charging and discharging techniques. Stakeholders may make informed decisions and take proactive steps to enhance EV battery efficiency and lifetime using cloud infrastructure, data analysis and visualization tools, and IoT. Advancing electric mobility and sustainability, the system monitors, manages, and improves battery performance. © 2023 IEEE.

关键词： Battery monitoring Cloud computing Electric vehicle IoT Sensors

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