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Revolutionizing Healthcare with Wearable Devices and IoT Integration for Personalized Health Management in Next-Generation Health Informatics

Revolutionizing Healthcare with Wearable Devices and IoT Int...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Parthipan, V. Ramesh, P.S. Gururama Senthilvel, P. Dineshkumar, R. Vennila, G. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India

ISBN: (纸本)9798350388602

This work outlines a paradigm shift that is created by the integration of wearable devices with the Internet of Things for health consciousness. With the help of the proposed system, the user is given the constant readings of the key heath indicators including heart rate, blood pressure, blood glucose level, physical activity, and the quality of sleep, all the data are transferred in real-time to an IoT platform, and the data analysis supported by machine learning algorithms provide the user with individually tailored heath reports. Wearable Medical today carried out a 90 persons trial over a six-month long program and noted changes in health parameters, user interactions and results. Several client outcomes highlighted that clients' heart rate, blood pressure and glucose levels, physical activity, and sleep quality improved. The system also provided strong control in data protection and the compliance with different regulations which is needed for any company. Although there are issues with device and network integration and the supporting architecture, the study demonstrates the system's capability of decreasing hospital attendance, increasing medication compliance and quality of life, thereby opening up possibilities for exploring wearable health technologies in clinical environments in the future. © 2024 IEEE.

关键词： Electronic health record

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Energy-aware Coverage Path Planning of Multi-UAV Based on Relative Distance Scaling Cluster Method 8

Energy-aware Coverage Path Planning of Multi-UAV Based on Re...

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8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Liu, Shen Li, Xiuxian Meng, Min Gong, Xin College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Department of Control Science and Engineering Shanghai201800 China School of Cyber Science and Engineering Southeast University Nanjing210096 China The University of Hong Kong Department of Mechanical Engineering 999077 Hong Kong

ISBN: (纸本)9798350300178

In recent years, unmanned aerial vehicle (UAV) aerial photography becomes a new method of disaster area exploration. UAVs have been widely used in the fields for aerial photography, monitoring, transportation, communication and so on. They can expand the dimension and scale of search and rescue (SAR) work, reduce the cost of human and material resources, and improve the efficiency of SAR work. Considering the limited of a single UAV, multi-UAV and energy-aware path planning algorithms are proposed here for SAR missions, which improve the cluster algorithm. In the algorithm design, we take the energy consumption of each UAV as an important factor for optimizing the division of search area, and propose the spiral search path generation algorithm. And numerical examples corroborate the effectiveness of the theoretical results. © 2023 IEEE.

关键词： Aerial photography

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Multi-LiDAR-Inertial Odometry Combined with Vehicle Kinematics for Autonomous Buses

Multi-LiDAR-Inertial Odometry Combined with Vehicle Kinemati...

引用

2024 lEEE international conference on Advanced Information, Mechanical engineering, robotics and Automation, AIMERA 2024

作者： Song, Tao Zheng, Ying Wang, Pengcheng Wu, Xinkai Beihang University School of Transportation Science and Engineering Beijing China Robint Technologies Company Ltd. Beijing China Beihang University School of Cyber Science and Technology Beijing China

ISBN: (纸本)9798350343335

The application of multi-LiDAR setup in autonomous buses is becoming increasingly widespread. However, challenges in real-world applications of multi-LiDAR SLAM remain largely unexplored. This paper introduces a multi-LiDAR-inertial odometry algorithm that utilizes vehicle kinematics to improve positioning accuracy and reliability. First, we introduce a sensor fusion method that synchronizes and integrates data from point cloud, IMU, and wheel encoder in the shortest possible time intervals, using IMU data to correct point cloud distortions. Subsequently, we developed a multi-sensor fusion odometry system combining multi-LiDAR, IMU, and vehicle wheel encoder data. Finally, leveraging the odometry's results, we generated a comprehensive global point cloud map. The evaluation of the proposed method in real-world scenarios demonstrates its capability to achieve high-precision odometry data and state-of-the-art performance. © 2024 IEEE.

关键词： Magnetic levitation vehicles

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Enhancing Electromechanical Coupling: A Modified Model for Studying Uterine Myometrial Contraction 2

Enhancing Electromechanical Coupling: A Modified Model for S...

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2nd international conference on Automation, robotics and Computer engineering, ICARCE 2023

作者： Wang, Mengying Ruan, Luyi Lin, Yiwei Chen, Zhenqin Xu, Jinshan Zhejiang University of Technology College of Computer Science Hangzhou China

ISBN: (纸本)9798350308341

The mechanism of uterine electromyographic signals and mechanical contractions is a significant research topic. In this study, we propose a novel electromechanical model that utilizes the FitzHugh-Nagumo (FHN) model to simulate the enhanced excitability of uterine cells influenced by hormones, leading to the successful generation of pacemaker behavior upon the introduction of small perturbations. Furthermore, we observe that as pregnancy progresses, enhanced gap junction, anchoring junction, and electromechanical coupling activity contributes to global mechanical contraction synchronization. Our research results also demonstrate a considerable level of synchronization between electrical and mechanical signals, providing robust theoretical support for the practical application of electrical signals. These findings deepen our understanding of electromechanical coupling effects and offer new perspectives for research and applications in related fields. © 2023 IEEE.

关键词： Electromechanical coupling

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EG53: Automation of seismic scouting with robotics and machine learning 7

EG53: Automation of seismic scouting with robotics and machi...

引用

7th international conference on engineering Geophysics, ICEG 2023

作者： Golikov, Pavel Ramdani, Ahmad Alsoqair, Khaled Bakulin, Andrey EXPEC Advanced Research Center Saudi Aramco Dhahran Saudi Arabia King Abdullah University of Science and Technology Saudi Arabia

The accuracy of the survey design for the land seismic data acquisition became crucial for the best data quality and highest productivity with the emerging trend of high-channel high trace density acquisition. Satellite maps remain being a valuable source of information on the ground conditions in the area of interest despite typically lacking the necessary resolution and possibly being out of date for seismic survey planning. We propose using a small, dedicated field crew equipped with UAVs to quickly acquire high-resolution imagery in the most critical areas for the planned survey. Within days, this data can be processed, automatically interpreted using machine learning techniques, and provided to the survey planning specialists. © 2024 7th international conference on engineering Geophysics, ICEG 2023. All rights reserved.

关键词： Machine learning

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Feedback-Based Fusion Filtering for Rectangular Descriptor Systems via Amplify-and-Forward Relay Strategy

Feedback-Based Fusion Filtering for Rectangular Descriptor S...

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2024 IEEE international conference on Control science and Systems engineering, ICCSSE 2024

作者： Zhou, Zhaozhao Hu, Jun Luo, Ruonan Du, Junhua Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China School of Automation Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9798331517199

This paper is concerned with the feedback-based fusion filtering problem for linear rectangular descriptor systems. By utilizing full-order transformation, the original descriptor system is transformed into a full-order non-descriptor system. Based on the measurement output forwarded by the relay and the feedback information from the fusion center, the local filters are constructed, and the upper bound of each filtering error covariance matrix is obtained by using the basic inequality and mathematical induction. Moreover, the local filter gain is derived by calculating the minimum value of the obtained upper bound. The local information is comprehensively processed by using the covariance intersection fusion criterion and the corresponding feedback-based fusion filtering algorithm is proposed. Finally, a numerical simulation is given to verify the effectiveness of the designed filtering algorithm. © 2024 IEEE.

关键词： Wiener filtering

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Machine Learning Based Age Prediction Using Extensive Health and Nutritional Factors 2

Machine Learning Based Age Prediction Using Extensive Health...

引用

2nd international conference on advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Thota, Durga Sravanthi Varma, Pothuri Surendra Varla, Anusha Tipirneni, Pranavi Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798331541217

With the increasing demand for machine learningin the medical field, this work addresses the growing need for accurate age prediction. Leveraging the NHANES dataset, which includes seven key features such as Body Mass Index and Glucose levels, the proposal utilizes advanced machine learning techniques to train a model that effectively predicts individuals' ages. After exploring various predictive approaches, a model was selected that demonstrated superior performance, highlighting its potential to enhance healthcare planning, risk assessment, and personalized health interventions. This data-driven approach promises to improve healthcare outcomes and support strategicdecision-making in the medical domain. © 2024 IEEE.

关键词： Age prediction Health Lifestyle factors Machine Learning Nutritional Data

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Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT Data Analytics

Random Forest Predictive Model for Ventilator-Associated Pne...

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2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Ramadevi, R. Kishoresonti, V.J.K. Jain Jacob, M. Vaidehi, V. Mohankumar, N. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Sathyabama Institute Of Science And Technology Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Tamil Nadu Chennai India Department Of Computer Science Tamil Nadu Salem India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

To identify and forecast ventilator-associated pneumonia (VAP) early on, this study investigates the use of random forest predictive models in combination with Internet of Things (IoT) data analytics. Patients on mechanical ventilation are particularly vulnerable to VAP, a serious nosocomial infection. It uses Random Forest algorithms to examine a wide range of factors, such as vital signs, patient demographics, and ventilator settings, by using the abundance of healthcare data supplied by the IoT. The goal is to create reliable prediction models that can identify people who are at risk of getting VAP before any symptoms appear. Using data gathered from ventilator-integrated real-time monitoring devices, it creates a complete dataset. The findings reveal that random forest models are reliable and very accurate in predicting VAP. Integrating analytics from the IoT improves prediction accuracy, which in turn helps healthcare providers make better, timelier preventative decisions. Improving patient outcomes and optimizing resource allocation in critical care units may be possible as a result of this study's contributions to the expanding body of work on using sophisticated data-driven methodologies for infectious illness prediction in clinical settings. © 2024 IEEE.

关键词： Internet of things

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An Online Strategy for Nonlinear Vector Autoregression 7

An Online Strategy for Nonlinear Vector Autoregression

引用

7th international conference on robotics, Control and Automation engineering, RCAE 2024

作者： Fang, Gang Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Nonlinear vector autoregression (NVAR), as an effective alternative to traditional reservoir computing (RC), has garnered significant attention in the field of time series prediction in recent years. Unlike conventional RC, NVAR does not require the use of random sampling matrices to define the underlying recurrent neural network, and it features fewer hyperparameters. However, NVAR is an offline model, making it challenging to meet the real-time monitoring requirements of industrial processes. Inspired by the Woodbury matrix identity, this paper proposes an online NVAR that can update the model using data arriving in blocks. Subsequently, this study constructs a soft sensing model based on the online NVAR to perform real-time monitoring of industrial processes. Finally, the effectiveness of the proposed method is validated using a real-world dataset from a wastewater treatment plant. © 2024 IEEE.

关键词： Wastewater treatment

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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