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Feasibility of a Digital Health Puzzle Game for Detecting computer Mouse Behavioral Patterns in ADHD

Feasibility of a Digital Health Puzzle Game for Detecting Co...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Shehjar Sadhu Elijah Castillo Lisa Weyandt Dhaval Solanki Kunal Mankodiya Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA Department of Computer Science & Statistics University of Rhode Island Kingston RI USA Department of Psychology University of Rhode Island Kingston RI USA

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Attention Deficit Hyperactive Disorder (ADHD) is a neurodevelopmental condition. Globally, more than 366 million individuals are diagnosed with ADHD. ADHD is a chronic condition and has no known cure. However, treatments do exist in the form of behavior therapy and medication. Currently, management of ADHD symptoms during treatment is done through standardized questionnaire assessments. While valuable, these assessments are subjective. Researchers have proposed using computer-based games for objective monitoring of ADHD symptoms. In this work, we present MindGame, a puzzle game designed to monitor ADHD symptoms using computer mouse movement. To evaluate the feasibility of this application, we conducted an in-home study (N=4; 2 Neurotypical and 2 ADHD) participants who were asked to play the game 8–10 times in one- week at-home. computer mouse data from 128 individual puzzle levels were analyzed. The game has three levels built-in with increasing level of difficulty. A significant difference (p-value=0.01786) between ADHD and Neurotypical participants in the Euclidean distance of the mouse cursor movement. All participants completed the study at-home 8–10 times except for P2 who was found dropped out of the study due to personal reasons.

关键词： Body sensor networks Medical treatment Games Euclidean distance Electronic healthcare biomedical monitoring Monitoring Medical diagnostic imaging

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Learning-based Camera Relocalization with Domain Adaptation via Image-to-Image Translation

Learning-based Camera Relocalization with Domain Adaptation ...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Yang, Chenhao Liu, Yuyi Zell, Andreas University of Tübingen Chair of Cognitive Systems Department of Computer Science Tübingen Germany Hri Laboratory Graduate School of Informatics Kyoto University Dept. of Social Informatics Kyoto Japan

ISBN: (纸本)9780738131153

Camera relocalization is promising for robot navigation;however, the lack of sufficient reference images and challenging shifts of conditions limit its further application. This paper proposes a learning-based three-step pipeline that applies spatial information features for camera relocalization problems. We first introduce a frustum intersection over union (IoU) to represent the image pair's spatial similarity. This representation is used to train an image retrieval model to find nearest neighbor candidates for query images. A spatial sample consensus (SPASAC) operation is then deployed to filter the outliers in the candidates. Afterward, a relative camera pose regressor is used with the valid candidates to predict every query image's absolute pose. Besides, we introduce two implementations of image-to-image translation networks for camera relocalization to increase the number of synthetic reference images and challenging night-to-day localization performance. Experiments show that our method can estimate camera poses across different domains and outperforms related methods in four benchmarks. Specifically, the experiments on the Tuebingen Buildings dataset demonstrate the robustness of our approach when localizing UAV-captured images with highspeed movement and large viewpoint variation. © 2021 IEEE.

关键词： Cameras

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Deep-learning for thyroid microstructure segmentation in 2D OCT images 25

Deep-learning for thyroid microstructure segmentation in 2D ...

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Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV 2021

作者： Tampu, Iulian Emil Eklund, Anders Haj-Hosseini, Neda Dept. of Biomedical Engineering Linkoping University 581 85 Sweden Dept. of Computer and Information Science Linkoping University 581 83 Sweden Center for Medical Image Science and Visualization Linkoping University 581 85 Sweden

ISBN: (数字)9781510640962

ISBN: (纸本)9781510640955

Optical coherence tomography (OCT) can provide exquisite details of tissue microstructure without traditional tissue sectioning, with potential diagnostic and intraoperative applications in a variety of clinical areas. In thyroid surgery, OCT could provide information to reduce the risk of damaging normal tissue. Thyroid tissue's follicular structure alters in case of various pathologies including the non-malignant ones which can be imaged using OCT. The success of deep learning for medical image analysis encourages its application on OCT thyroid images for quantitative analysis of tissue microstructure. To investigate the potential of a deep learning approach to segment the follicular structure in OCT images, a 2D U-Net was trained on b-scan OCT images acquired from ex vivo adult human thyroid samples a effected by a range of pathologies. Results on a pool of 104 annotated images showed a mean Dice score of 0.74±0.19 and 0.92±0.09 when segmenting the follicular structure and the surrounding tissue on the test dataset (n=10 images). This study shows that a deep learning approach for tissue microstructure segmentation in OCT images is possible. The achieved performance without requiring manual intervention encourages the application of a deep-learning method for real-time analysis of OCT data. © 2021 SPIE.

关键词： Optical tomography

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A Reproducible ETL Approach for Window-based Prediction of Acute Kidney Injury in Critical Care Unit and Some Preliminary Results with Support Vector Machines

A Reproducible ETL Approach for Window-based Prediction of A...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Chiorean, Isabela A. Amico, Beatrice Combi, Carlo Holmes, John H. University of Verona Dept. of Diagnostics and Public Health Verona Italy University of Verona Dept. of Computer Science Verona Italy University of Pennsylvania Dept. of Biostatistics Epidemiology and Informatics Perelman School of Medicine PhiladelphiaPA United States

ISBN: (纸本)9781665401265

Acute kidney injury (AKI) is a frequent complication in hospitalized patients, and is associated with worse short and long-term outcomes. An early prediction of AKI to detect the patients at risk could be a first step in the discovery and assessment of new therapies, and in improvements of patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed the development of predictive models of AKI diagnosis. In this research work we provide a consistent reproducible ETL pipeline for Intensive Care Unit (ICU) data, in particular regarding the MIMIC III database, to support the early prediction of AKI. Then, we build different predictive models aimed at early identifying subjects who could experience AKI syndrome in their next 7 days after the ICU admission. The entire procedure is based on a recently proposed rolling observational window approach. We consider two predictive models, Gradient Boosting Decision Trees and Support Vector Machines, via different platforms. © 2021 IEEE.

关键词： Forecasting

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Classification of diseases in citrus fruits using SqueezeNet

Classification of diseases in citrus fruits using SqueezeNet

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2021 International Conference on Applied and Engineering Mathematics, ICAEM 2021

作者： Khan, Ejaz Zia Ur Rehman, Muhammad Ahmed, Fawad Khan, Muhammad Attique School of Engineering RMIT University Melbourne Australia Dept. of Electrical Engineering HITEC University Taxila Pakistan Dept. of Biomedical Engineering HITEC University Taxila Pakistan Dept. of Computer Science HITEC University Taxila Pakistan

ISBN: (纸本)9781665413039

Recent advances in AI technology have paved the way to precisely detect and classify fruit and vegetable diseases. Vision based accurate detection and classification techniques can help farmers to quickly identify different types of plant and fruit diseases. Citrus fruits are globally famous due to their taste and nutrients. In this paper, a deep learning-based technique is proposed for classification of six different citrus diseases that severely affect the yield and quality of citrus fruits. Data augmentation is performed to enlarge the privately acquired dataset. Later the augmented dataset is used to retrain two lightweight pre-trained deep learning models;the SqueezeNet and MobileNetV2. Features are extracted from the retrained models which are subsequently optimized by using the Whale Optimization Algorithm. Classification results obtained from both the models show that the SqueezeNet model outperforms MobileNetV2 and attains an accuracy of 96%. The proposed citrus fruit disease classification technique also outperforms when compared with a current technique proposed in the literature. © 2021 IEEE.

关键词： Citrus fruits

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Respiratory Rate Estimation via Sensor Pressure Mattress: a single subject evaluation 2

Respiratory Rate Estimation via Sensor Pressure Mattress: a ...

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2nd Workshop on Artificial Intelligence for Human-Machine Interaction, AIxHMI 2023

作者： Sarteschi, Artemisia Sorrenti, Domenico G. work performed as thesis for the MSc in Computer Science Univ. Milano - Bicocca the Sensory Motor Systems Lab ETH Zürich currently with ARTORG Center for Biomedical Eng. Research University of Bern Bern Switzerland Dept. Informatica Sistemistica e Comunicazione Università di Milano - Bicocca Milano Italy

Respiratory rate monitoring is crucial for many diseases, correlated or not with the lungs, like chronic bronchitis and obstructive apnea, and it is central for the study of sleep stages. Notably for sleep-related diseases, it is important to develop a non-intrusive method to monitor the respiratory rate. This single-subject study investigates the feasibility of using a pressure-sensor mattress to avoid cables and discomfort, for both the patient and the staff, typical of other devices. A pressure-sensor mattress generates a 2D matrix of pressure signals: in this work, those signals are analysed by a processing pipeline to detect the best signal in the matrix. The aim is to find the best signal to exploit for measuring the respiratory rate. Criteria have been identified, resulting in a metric to order the signals. The respiratory rate is then determined by another processing pipeline acting on the stream from this specific sensor. Many complications made the data gathered from all but one subject unusable: nevertheless, the results show that the approach is effective and the respiratory rate can reliably be measured with a commercial pressure-sensor mattress. © 2023 CEUR-WS. All rights reserved.

关键词： Pressure sensors

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Special Session: Amplifying Voices Through Animaker Animation: Results from a Longitudinal Study with Latino/a/x Engineering Students

Special Session: Amplifying Voices Through Animaker Animatio...

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Frontiers in Education (FIE) Conference

作者： Joel Alejandro Mejia Sofia Hernandez Neethu Paul Dept. of Biomedical & Chemical Eng. University of Texas at San Antonio San Antonio TX USA Dept. of Computer Science University of Texas at San Antonio San Antonio TX USA Dept. of Int. Learning & Teaching University of Texas at San Antonio San Antonio United States

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This special session explores the application of Chicana Feminist Epistemology methodologies in engineering education research with a focus on Latino/a/x students at Hispanic Serving Institutions. By utilizing Platicas and Testimonios as forms of data and data collection approaches in engineering education research, this session aims to foster a more inclusive and empowering research approach while challenging traditional Westernized paradigms. Through the analysis of Animaker videos created from vignettes emerging from these Platicas and Testimonios, participants will have a better understanding of the experiences of Latino/a/x engineering students, emphasizing asset-based methodologies and the creation of multimodal artifacts for research purposes. The research approaches presented in this session seek to amplify marginalized voices, promote critical consciousness, and challenge traditional ways of knowing and doing in engineering education research, encouraging attendees to integrate these methodologies into their research and teaching practices effectively to support diverse student populations.

关键词： Data collection Animation Engineering students Videos Gender issues

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Optimizing Brain Tumor Segmentation with Advanced Superpixel Techniques in Conjunction with Deep Learning Methods

Optimizing Brain Tumor Segmentation with Advanced Superpixel...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Chythanya, Kanegonda Ravi Aruna, S. Karuppaiah, Jayakumar Arul, S. Benjamin Kumar Pareek, Piyush Sr University School of Computer Science & Artificial Intelligence Warangal India Srmist Dept. of Computational Intelligence Chennai India KIT- Kalaignarkarunanidhi Institute of Technology Department of Biomedical Engineering Coimbatore India School of Engineering and Technology Jeppiaar University Chennai India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Brain tumor segmentation in medical imaging is crucial for precise diagnosis and treatment planning. To address these issues, our research leverages deep learning techniques and introduces an innovative approach that combines various key components. Our methodology includes preprocessing, superpixel segmentation, feature extraction, and the use of a Convolutional Neural Network (CNN) to achieve precise brain tumor segmentation. This approach is driven by the necessity for improved accuracy and efficiency, especially with diverse clinical data and tumor types, like the challenging BraTS 2018 dataset, consisting of 1070 T1W-CE MRI images. This approach using metrics such as the Dice score, sensitivity, and specificity, which demonstrate the superior performance of our superpixel model in brain tumor segmentation. This proposed method yields exceptional results, with a Dice score of 97.06%, sensitivity of 99.05%, and specificity of 99.83%, highlighting its accuracy and effectiveness in tumor segmentation when compared to existing methods such as UNet, Kapur entropy, and rough set theory. This research significantly advances the state of the art and offers great promise for enhancing medical image analysis in this critical field. © 2023 IEEE.

关键词： Brain tumor Convolutional Neural Network Deep Learning Superpixel technique Tumor segmentation

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Design of Automatic Radiation Detection System as a Part of Radiation Protection in Linear Accelerator Facilities

Design of Automatic Radiation Detection System as a Part of ...

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Communication and Information Technologies (KIT)

作者： Martin Dugas Norbert Ferenčík Radovan Hudák Zuzana Naďová Petra Kolembusová William Steingartner Dept. of Biomedical Engineering Technical University of Košice Košice Slovakia Faculty of Science Pavol Jozef Šafárik University in Košice Dept. of Computers and Informatics Technical University of Košice Košice Slovakia

The main objective of this study is the development of an automatic radiation detection system that can be installed at linear accelerator facilities. This system aims to inform the linear accelerator operating staff about the ambient radiation dose rate inside the treatment room. This information might further improve radiation safety. The system is meant to capture residual radiation in the treatment room induced by photonuclear radioactive activation of materials inside the linear accelerator. This study contains current knowledge about ionizing radiation, its dosimetry and detection. It also contains the newest radiation protection laws and radiation protection standards of linear accelerator facilities. The main part of the study is concerned with the design of the detection system - hardware, software, connection and testing of functionality and reliability of the system. The testing was performed by measurement of residual radiation 20 cm away from the source of the linear accelerator. It was performed immediately after applying determined monitor units of photons with energies of 10 MeV and 15 MeV. Measurements were carried out simultaneously using our system and a certified dosimeter RAYSAFE 452. The measured values were compared afterward. Measurement error of 6,08 % and 6,25 %, respectively, was observed. Therefore, the system is arguably reliable and adequate for its purposed use.

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Designing and Simulating a Smart Water Harvesting System Using PLC Techniques

Designing and Simulating a Smart Water Harvesting System Usi...

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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Haitham B. Qasim Thabit H. Thabit Firas M. Mahmood Dept. of Medical Instruments Techniques Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Computer Science Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Iraq

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques.

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