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A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and real-time data processing

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SENSORS 2018年第7期18卷 2183-2183页

作者： Alfian, Ganjar Syafrudin, Muhammad Ijaz, Muhammad Fazal Syaekhoni, M. Alex Fitriyani, Norma Latif Rhee, Jongtae Dongguk Univ Nano Informat Technol Acad U SCM Res Ctr Seoul 100715 South Korea Dongguk Univ Dept Ind & Syst Engn Seoul 100715 South Korea

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users' vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user's sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.

关键词： diabetes BLE real-time data processing classification forecasting

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Advanced web analytics tool for mouse tracking and real-time data processing 14

Advanced web analytics tool for mouse tracking and real-time...

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14th IEEE International Scientific Conference on Informatics

作者： Cegan, Lukas Filip, Petr Univ Pardubice Fac Elect Engn & Informat Pardubice Czech Republic

ISBN: (纸本)9781538608890

Web analytic tools offer important support for better recognition of the web user's behavior, identification of bottlenecks and errors in user interface design, performance measurement of web environment, monitoring of website availability or recommendation of appropriate website content. These tools are based on tracking techniques and sophisticated algorithms that process and evaluate large volumes of captured data. In this paper, we propose a new solution to capture mouse movements of web users, to identify their area of interest. This solution is based on real-time data transformation, which converts discrete position data with high sample period to predefined functions. This transformation has a high degree of accuracy, which is exemplified by case scenarios. The result of this solution is a significant saving in data, transmitted from the client to the server, which leads to significant savings in system resources on the server side.

关键词： mouse tracking real-time data processing user behavior

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Research on real time data processing Algorithm for Optical Measurement 3

Research on Real time Data Processing Algorithm for Optical ...

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3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Xin Jian Unit 91550 Dalian Peoples R China

ISBN: (纸本)9781538684818

The algorithm of optical measurement for real-time data processing based on Kalman filter is studied, by analyzing the present situation and exiting problem in the real-time data processing of optical measurement. In this paper, the feasible model and data processing method are given. The simulation results show that the algorithm model is accurate and effective. The research result plays an important role in raising the precision of real-time data processing. Meantime, it also has a guiding significance in further improving of measurement & control software system.

关键词： flight test optical measurement real-time data processing Kalman filter

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real-time Fuzzy data processing Based on a Computational Library of Analytic Models

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data 2018年第4期3卷 59页

作者： Kondratenko, Yuriy Kondratenko, Nina Petro Mohyla Black Sea Natl Univ Intelligent Informat Syst Dept UA-54003 Mykolaiv Ukraine Univ South Carolina Darla Moore Sch Business Columbia SC 29208 USA

This work focuses on fuzzy data processing in control and decision-making systems based on the transformation of real-timeseries and high-frequency data to fuzzy sets with further implementation of diverse fuzzy arithmetic operations. Special attention was paid to the synthesis of the computational library of horizontal and vertical analytic models for fuzzy sets as the results of fuzzy arithmetic operations. The usage of the developed computational library allows increasing the operating speed and accuracy of fuzzy data processing in real time. A computational library was formed for computing of such fuzzy arithmetic operations as fuzzy-maximum. Fuzzy sets as components of fuzzy data processing were chosen as triangular fuzzy numbers. The analytic models were developed based on the analysis of the intersection points between left and right branches of considered triangular fuzzy numbers with different relations between their parameters. Our study introduces the mask for the evaluation of the relations between corresponding parameters of fuzzy numbers that allows to determine the appropriate model from the computational library in automatic mode. The simulation results confirm the efficiency of the proposed computational library for different applications.

关键词： big data fuzzy set vertical and horizontal models maximum computational library real-time data processing

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A real-time processing method for GB-SAR monitoring data by using the dynamic Kalman filter based on the PS network

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LANDSLIDES 2023年第8期20卷 1639-1655页

作者： Xiang, Xia Chen, Chen Wang, Hui Lu, Heng Zhang, Han Chen, Jiankang Sichua n Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Sichuan Peoples R China Sichuan Univ Coll Hydraul & Hydroelect Engn Chengdu 610065 Sichuan Peoples R China Sichuan Prov Stn Surveying & Mapping Prod Qual Supervis & Inspect Chengdu 610041 Sichuan Peoples R China

Ground-based synthetic aperture radar (GB-SAR) has been widely used in the safety monitoring of slopes, dams, and buildings due to its high precision, large coverage area, and fast image acquisition. The real-time processing of high frequency and continuous deformation monitoring data is particularly important for early warning of landslides and high-risk buildings. Yet very limited studies have been conducted on the real-time processing method of GB-SAR monitoring data. In this study, a novel real-time processing method of GB-SAR monitoring data is proposed by using the Kalman filter based on the permanent scatterer (PS) network. The proposed method starts from the radiation characteristic and the phase composition of the GB-SAR monitoring data and instantaneously processes the acquired radar image by using the dynamic Kalman filter based on PSs and PS network. Then, a real-time processing Kalman mathematical model can be established, the model parameters are initialized, and the recursive Kalman filter to solve the timely deformation monitoring. By continuously updating the image data, the real-time and high-efficient calculation of PS deformation parameters can be achieved, which realizes the high accuracy and continuous deformation monitoring. The proposed novel method fills the gap in the real-time processing techniques of GB-SAR monitoring data and solves key problems of PS network updating, phase unwrapping, atmospheric phase correction, deformation calculation, etc.

关键词： GB-SAR real-time data processing Kalman filter PS network Deformation monitoring

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real-time data processing for In-line Monitoring of a Pharmaceutical Coating Process by Optical Coherence Tomography

Real-time Data Processing for In-line Monitoring of a Pharma...

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Conference on Biophotonics - Photonic Solutions for Better Health Care IV

作者： Markl, Daniel Ziegler, Jakob Hannesschlaeger, Guenther Sacher, Stephan Buchsbaum, Andreas Leitner, Michael Khinast, Johannes G. Res Ctr Pharmaceut Engn GmbH Inffeldgasse 13-2 A-8010 Graz Austria Res Ctr Non Destruct Testing GmbH A-4040 Linz Austria Graz Univ Technol Inst Proc & Particle Engn A-8010 Graz Austria

ISBN: (纸本)9781628410778

Coating of tablets is a widely applied unit operation in the pharmaceutical industry. Thickness and uniformity of the coating layer are crucial for efficacy as well as for compliance. Not only due to different initiatives it is thus essential to monitor and control the coating process in-line. Optical coherence tomography (OCT) was already shown in previous works to be a suitable candidate for in-line monitoring of coating processes. However, to utilize the full potential of the OCT technology an automatic evaluation of the OCT measurements is essential. The automatic evaluation is currently implemented in MATLAB and includes several steps: (1) extraction of features of each A-scan, (2) classification of A-scan measurements based on their features, (3) detection of interfaces (air/coating and coating/tablet core), (4) correction of distortions due to the curvature of the bi-convex tablets and the oblique orientation of the tablets, and (5) determining the coating thickness. The algorithm is tested on OCT data acquired by moving the sensor head of the OCT system across a static tablet bed. The coating thickness variations of single tablets (i.e., intra-tablet coating variability) can additionally be analysed as OCT allows the measurement of the coating thickness on multiple displaced positions on one single tablet. Specifically, the information about those parameters emphasises the high capability of the OCT technology to improve process understanding and to assure a high product quality.

关键词： Optical coherence tomography pharmaceutical coating process in-line monitoring real-time data processing

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Architecture for Intensive Care data processing and Visualization in real-time

Architecture for Intensive Care Data Processing and Visualiz...

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12th International Conference on Ambient Systems, Networks and Technologies (ANT) / 4th International Conference on Emerging data and Industry 4.0 (EDI40)

作者： Cruz, Ricardo Guimaraes, Tiago Peixoto, Hugo Santos, Manuel Filipe Univ Minho Ctr Algoritmi P-4710 Braga Portugal

Clinical data is growing every day. Ergo, to treat, store and publish such data is an emergent task. Furthermore, analysing data in real-time using streaming and processing technologies and methods, in order to obtain quality data, prepared to support decision making is of extreme value. Big data emerged with the introduction of real-time processing, thus revolutionizing traditional technologies and techniques through the ability to deal with the volume, speed and variety of data. Countless studies have been proposed in the healthcare domain in search of solutions that allow the flow of data in real-time. However, the work presented hereby is distinguished by allowing the collection, processing, storage and analysis of Intensive Care Units (ICU) data, both collected in real-time from bedside monitors but also stored in a historical repository. The architecture proposed makes use of current technologies, like Nextgen Connector as message supplier and integrator, Elasticsearch as a search index, Kibana for viewing stored data and Grafana for real-time streaming. This article is part of the ICDS4IM project - Intelligent Clinical Decision Support in Intensive Care Medicine to support the experimentation of data processing techniques and technologies, based in HL7 format and collected in real-time so that it can be made available through Health Information Systems across the healthcare institutions. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： real-time data processing real-time data Visualization Decision Support Systems Big data

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Ensuring real-time data integrity in smart building applications: A systematic end-to-end comprehensive pipeline evaluated in numerous real-life cases

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ENERGY AND BUILDINGS 2025年 336卷

作者： Stefanopoulou, Aliki Michailidis, Iakovos Karatzinis, Georgios Lepidas, Georgios Boutalis, Yiannis S. Kosmatopoulos, Elias B. Ctr Res & Technol Hellas Informat Technol Inst 6th Km Harilaou Thermis Thessaloniki 57001 Greece Democritus Univ Thrace Sch Engn Dept Elect & Comp Engn Vasilissis Sofias 12 Xanthi 67100 Greece

In this study, we propose a comprehensive, end-to-end data healing pipeline, developed and tested in real buildings facing diverse severity problems. This pipeline is designed to ensure the accuracy and reliability of smart building data in a fast and responsive manner, using computationally lightweight statistical approaches for outlier detection and LightGBM for data imputation. Both methods are optimized for low computational cost, making them ideal for real-world scenarios requiring immediate feedback and capable of handling very large datasets efficiently. Our system is designed to operate automatically, capable of applying real-time data processing and periodic model updates without manual intervention. It was evaluated using Key Performance Indicators over nine weeks across five smart buildings in the EU, revealing discrepancies in performance across different time periods and buildings. These findings highlight the need for tailored data healing strategies for varying dataset sizes, ultimately enhancing data quality for more reliable analyses and informed decision-making. The implementation of this pipeline contributes to more accurate energy usage, improved occupant comfort, and more efficient building operations, supporting the broader goals of sustainability and energy efficiency in smart buildings.

关键词： Smart buildings data filtering data imputation real-time data processing

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Application and optimization of intelligent image identification technology in highway inspection data

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年 1-13页

作者： Wang, Wenjuan Li, Chengwu Sun, Shouwei Shen, Chao Wang, Yunbin Beijing Yunxuehui Informat Technol Co LTD Beijing 100080 Peoples R China Yunnan Transportat Res Inst Co LTD Kunming 650011 Yunnan Peoples R China

Traditional inspection methods for highways include fixed cameras and automatic detection systems. Although they automate the inspection process to a certain extent, they still face problems such as limited coverage, delayed data processing, and insufficient identification accuracy. These systems may face compatibility challenges when integrated with new technologies. To address these issues, this article explores the application of intelligent image identification technology in highway inspections. High-resolution cameras are used for data acquisition, and convolutional neural networks (CNN) are applied to extract image features and generate feature maps. The faster region-based convolutional neural network (Faster R-CNN) is used to classify and perform the bounding box regression on the features extracted by CNN in these regions, determining the category and precise location of the object. At the same time, the deep learning algorithm Autoencoder is used for anomaly detection, and multi-source data is integrated through Kalman filtering. The model parameters and computational graphs are also optimized to improve system efficiency. A 12-km-long highway was selected for testing. The average processing time of the system was within 400 ms, and the processing time for each frame of the image was about 150 ms. It can be seamlessly integrated with the existing system, meeting the real-time requirements. The results show that intelligent image recognition technology can achieve higher highway inspection efficiency and accuracy while maintaining good compatibility with existing equipment.

关键词： Intelligent image identification technology Convolutional neural network System integration real-time data processing Performance optimization

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Comparative Analysis of Hadoop and Spark Performance for real-time Big data Smart Platforms Utilizing IoT Technology in Electrical Facilities

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第7期19卷 4595-4606页

作者： Gabdullin, Maratbek T. Suinullayev, Yerulan Kabi, Yelikbay Kang, Jeong Won Mukasheva, Assel Kazakh British Tech Univ Sch Mat Sci & Green Technol Alma Ata Kazakhstan Almaty Univ Power Engn & Telecommun Alma Ata Kazakhstan Korea Natl Univ Transportat Transportat Syst Engn Uiwang Si 16106 Gyeonggi Do South Korea Kazakh British Tech Univ Sch Informat Technol & Engn Alma Ata Kazakhstan

As the adoption of IoT technology in power systems accelerates and the need for improved methods to handle large volumes of data emerges, real-time big data smart platforms must address the growing data processing demands in IoT integrated power systems. Therefore, in this study, we assess the performance of Hadoop and Spark for iterative computing and real-time data processing applications. Our evaluation is based on metrics such as execution time, resource utilization, and scalability, particularly with increasing data volume. The comparison aims to provide guidance to researchers, practitioners and entrepreneurs on platform selection depending on their specific requirements. The study identified the strengths and weaknesses of both platforms and provided valuable insights into optimizing the performance of big data applications. Text documents and charts for Word Count and PageRank tasks were used for comparison, and performance testing was performed on datasets of different sizes. The results showed that Spark outperforms Hadoop in most applications, especially in iterative computation and real-time data processing, due to its use of in-memory computation. However, Hadoop is best suited for batch processing operations that require multiple steps. It can perform these operations in parallel across multiple cluster nodes, enabling fast processing of large amounts of data. This comprehensive performance comparison of Hadoop and Spark in iterative computing and real-time data processing applications provides valuable information for researchers, practitioners, and enterprises on the trade-offs and benefits of using these big data platforms.

关键词： IoT real-time data processing Hadoop Spark Performance evaluation

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