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检索条件"任意字段=Conference on Smart Biomedical and Physiological Sensor Technology XIX"

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Electrospinning Polyvinyl Alcohol/Polyaniline Nanofiber-Based Humidity sensor

Electrospinning Polyvinyl Alcohol/Polyaniline Nanofiber-Base...

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sensors and Information technology (ICSI), International conference on

作者： Rong Du Han Song Yi Liu Boyi Wang Wuhan University of Technology Wuhan China Wuhan University of Technology Shaoxing Institute for Advanced Research Wuhan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the rapid advancement of smart wearable devices, the monitoring of human physiological signals has emerged as a prominent research focus. In this study, a polyvinyl alcohol/polyaniline (PVA/PANI) resistive humidity sensor with superior humidity sensing performance was fabricated using electrospinning technology. Within the relative humidity ranges of 25% to 65% and 65% to 95%, the sensor exhibits sensitivities of-120.77MΩ/%RH and -4.44MΩ/%RH, respectively. Furthermore, the sensor exhibits a response/recovery time of approximately 45/14 seconds, hysteresis of 2.23%. The PVA/PANI humidity sensor has the ability of respiration monitoring, which provides a new method for the application of polymer humidity sensor in human physiological signal monitoring.

关键词： Sensitivity Humidity Polymers Time factors biomedical monitoring Wearable devices Monitoring Hysteresis Standards Testing

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smart biomedical and physiological sensor technology xix

Smart Biomedical and Physiological Sensor Technology XIX

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smart biomedical and physiological sensor technology xix 2022

ISBN: (纸本)9781510651227

The proceedings contain 12 papers. The topics discussed include: image quality assessment of smartphone-based retinal imaging systems;who watches the watches: on the delivery of the specifications of smart wearable devices and recommendations on the related best practices;impedance-based characterization of healthy and cancer cell derived exosome samples;comparing energy levels in brain regions of interest in ADHD subjects;V-Pods: rapid, sensitive detection of intact SARS-CoV-2 using DNA nets and a smartphone-linked fluorimeter;laser-induced breakdown spectroscopy (LIBS)-based assay for point-of-care (POC) detection of cytokines in COVID-19 infection;predicting heart failure disease using machine learning;three-dimension EEG-based connectivity biomarkers for neurological disorder detections;fiber optic polarization temperature sensor for biomedical and military security systems;efficacy of ferromagnetic cores on the performance of TMS coils;and classification of diabetes mellitus disease using machine learning.

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On Predicting Emotion From Commercial smartwatch - A Big Data Narrative

On Predicting Emotion From Commercial Smartwatch - A Big Dat...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Partha Pratim Sarmah Partha Sarathi Paul Krishnandu Hazra Dept. of CSE IIITG Assam India School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

smart wearable devices such as smartwatches and smart bands are all-pervasive nowadays. They are accompanied by various sensors like heart rate monitors, oximetry sensors, ambient light and temperature sensors, etc. that collect different physiological data about the individuals while wearing. Such data can effectively be used to detect the activities of the user. Here, one relevant intriguing question arises: Is it feasible to predict the emotional status of an individual by utilizing such physiological data from such devices? To address this question, no such work utilizes commercial smartwatches. Therefore, to fill this gap, in this work, we analyze and predict emotions by utilizing the physiological data collected by the commercial Fitbit smartwatch. Here, we have utilized the Lifesnaps Fitbit dataset, a large public dataset of size 9.72 GB that contains both physiological sensor data and psychological survey data collected from 71 volunteers from different demographic areas using commercially available Fitbit smartwatches between May 2021 and January 2022. Here we investigate the correlation between emotional state and physiological parameters using data collected from the Lifesnaps dataset. We perform comprehensive data pre-processing such as data cleaning, data imputation, and scaling to ensure the data integration and usability of the dataset. We apply various non-neural machine learning algorithms (Support Vector Machine, Logistic Regression, Decision Trees, LightGBM, XGBoost, and Random Forest) and achieve an accuracy of up to 78.75% in predicting emotions. We further enhanced the performance of the prediction through efficiently hyper-tuning two neural network approaches (i.e., Feedforward neural network, and BiLSTM) and achieved a maximum accuracy of 97% in the Feedforward neural network. Our experimental evaluation shows a significant potential in commercial smartwatches for mental health and emotion detection.

关键词： Temperature sensors Temperature measurement Wearable Health Monitoring Systems Correlation Accuracy Big Data Feedforward neural networks biomedical monitoring Usability Intelligent sensors

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Optimized Stacked Sparse Autoencoder for IoT-Cloud smart Healthcare Networks

Optimized Stacked Sparse Autoencoder for IoT-Cloud Smart Hea...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Baba Fakruddin Ali B H L. Guganathan Prasanta Kumar Parida D. Samundeeswari Sridhar S Department of CSE Nagarjuna College of engineering and technology Bengaluru Karnataka India Department of Physics Saveetha School of Engineering Saveetha Insitute of Medical and Technical science (SIMATS) Thandalam Chennai Tamil Nadu India School of Rural Management KIIT University Bhubaneswar Odisha India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India Department of Research Rajalakshmi Institute of Technology (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

IoT technology growth in healthcare has been a revolution, and that is exciting, with the monitoring of real-time critical physiological and clinical parameters from real-time data: data-driven, personalized healthcare solutions. Even with such potential, health IoT faces challenges such as heterogeneity in data, noise, and inconsistent feature extraction that leads to suboptimal decision-making. Methods have to be developed efficiently for preprocessing and feature extraction to analyze data so that this disease can be predicted or monitored correctly. The motivation is to design a solid framework that bridges such gaps by making sure reliable, accurate predictions fit the smart healthcare application. Testing the proposed system with IoT-enabled wearable devices and clinical electronic health records (EHR), physiological metrics were collected from IoT sensors, supplemented by medical history and diagnostic data retrieved from cloud databases for blood pressure, heart rate, glucose levels, and oxygen saturation. The pre-processing step utilized Global-Local Depth Normalization (GLDN), which normalizes global structures and local variances to remove noise and scale variations. These features were analyzed using a Stacked Sparse Autoencoder (SSAE), optimized by Cat Hunting Optimization (CHO) to enhance prediction accuracy and computational efficiency. Experimental results validate the system with a remarkable 99.7% accuracy, 98.1% precision, and 97.5% recall and reduces computation time by 22% compared to the existing techniques. This shows the robustness and feasibility of the proposed approach in applications of smart healthcare. In conclusion, this framework represents an effective solution for smart healthcare systems that deals with data heterogeneity and the challenge of feature optimization for better patient care and resource management in IoT-driven environments.

关键词： Cloud computing Accuracy Autoencoders Medical services sensor systems Real-time systems biomedical monitoring Optimization Monitoring Diseases

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smart Savior: A Life Jacket Integrated IoT Emergency Response System Utilizing TTGO T-Call ESP32 and Advanced sensor Technologies

Smart Savior: A Life Jacket Integrated IoT Emergency Respons...

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International conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Md. Jakarea Debopom Sutradhar Ariful Islam Talukder S.M. Azizul Hoque Sourov Ripon Kumar Debnath Md Shifat Karim Md. Tamzidul Islam Nion Tania Akter Helena Bulbul Department of EEE United International University Dhaka Bangladesh Department of CSE United International University Dhaka Bangladesh Department of BBA United International University Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Life jackets are crucial for aquatic safety, preventing drowning and aiding in rescue. However, locating survivors can be challenging. The smart Savior life jacket, enhanced with IoT technology, integrates advanced sensors into a wristband and jacket to monitor environmental and physiological data. Key components include the NEO-6M GPS module, heart rate and body temperature sensors, and the ESP32 microprocessor. Data is transmitted via ESP-NOW for real-time monitoring and analysis. Rescue teams access a dynamic dashboard to prioritize rescue efforts based on geolocation and critical health indicators. The system's performance, evaluated against industry standards, shows faster GPS connectivity, real-time data synchronization, and improved accuracy in heart rate monitoring (98%) and water temperature sensing (±0.5 ∘ C). This integration offers a robust and effective approach to enhancing safety and responsiveness during aquatic disasters, making it a vital tool in saving lives.

关键词： Temperature sensors Heart rate Geology Robot sensing systems Real-time systems sensor systems biomedical monitoring Intelligent sensors Monitoring Global Positioning System

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MediBot: IoT-Driven Healthcare for Remote Communities

MediBot: IoT-Driven Healthcare for Remote Communities

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Ramprabu J Nandhini D Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

It is a new IoT-included healthcare application for remote supervision of patients’ health overseeing signs and notifying in case of detecting possible health issues with the help of advanced sensor technology. Plan C: MediBot to use sensors to take measurements of remote patients' body temperature, pulse, SpO2, movements, and environment; MediBot acquires real-time health data, with data displayed at the local level as well as in the cloud through an ESP32 microcontroller. This allows users to remotely choose, recommend their vitals, and schedule appointments with healthcare providers using MediBot based on specialists, the latter can monitor in real-time patient-shared vitals and data and may prescribe drugs online. Using live data classification, MediBot accurately identifies potential health deteriorations such as fever, hypoxia, stress, sleep apnea, and dehydration and opportunities for medical interference. The subset connects the patient and doctor by closing the loop and continued screening so that the availability and effectiveness of services are improved. MediBot is a smart healthcare system applied to IoT with the help of sensors and machine learning to predict the health state of patients. Real-time physiological indices such as body temperature, pulse rate, blood oxygen saturation, movement, and environmental conditions are captured through sensors mounted on MediBot and presented on a local interface as well as the ESP32 microcontroller uploading data to the cloud. Using al; machine learning algorithms, MediBot in real-time and historical data to assess health risks such as fever, hypoxia, stress, sleep apnea, and dehydration, allowing for timely alerts for the patient’s immediate medical attention. Thus it creates a channel of constant supervision, and preliminary and effective interaction as well as improves the organization and effectiveness of managing the patients’ and doctors’ encounters.

关键词： Temperature sensors Temperature measurement Temperature distribution Microcontrollers Medical services Real-time systems Sleep apnea biomedical monitoring Stress Monitoring

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Fiber optic polarization temperature sensor for biomedical and military security systems 19

Fiber optic polarization temperature sensor for biomedical a...

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conference on smart biomedical and physiological sensor technology xix

作者： Kyselak, Martin Slavicek, Karel Grenar, David Bohrn, Marek Vavra, Jiri Univ Def Kounicova 65 Brno Czech Republic Masaryk Univ Zerotinovo Nam 617-9 Brno Czech Republic Brno Univ Technol Brno Czech Republic

ISBN: (纸本)9781510651234;9781510651227

The paper deals with the influence of birefringence on the change of polarization and the change of frequency in sensory optical fibers. The aim of the described research was to design and implement a set of measurements that analyze these influences. The article builds on research that has dealt with this issue in the past and offers some simplifications in the design of the measuring station, describes control measurements and analyzes in detail the response of light parameters at the sensor output to changes in temperature. The article presents, compares and discusses the results of intensity changes at individual wavelengths, which demonstrate changes not only in the instantaneous polarization state of light but also changes in wavelengths with maximum light intensity depending on temperature changes. These principles can then be used to advantage with Military, biomedical and physiological sensors Systems.

关键词： optical fibre sensor biomedical sensor military security systems remote sensing defence frequency changes temperature field disturbance optical nonlinearity

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Who watches the watches: on the delivery of the specifications of smart wearable devices and recommendations on the related best practices 19

Who watches the watches: on the delivery of the specificatio...

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conference on smart biomedical and physiological sensor technology xix

作者： Simon, Aniko Szy, Laszlo Kara, Peter A. Guindy, Mary Sigma Technol Budapest Hungary Radicon Budapest Hungary Budapest Univ Technol & Econ Budapest Hungary Kingston Univ London England Holografika Budapest Hungary Pazmany Peter Catholic Univ Budapest Hungary

ISBN: (纸本)9781510651234;9781510651227

smart wearable devices have taken the market by storm. They are now their own device category on the consumer market, their popularity is unquestioned due to their ever-increasing set of functionalities and the vigorous competition between some of the biggest companies, and they seamlessly integrated into our everyday lives, as well as into professional contexts. New models appear on the market regularly, particularly since the sensory system of such devices is continuously developing, adding more ways of data acquisition and processing, along with projections and analyses. However, while the devices of certain subcategories are nearly identical with regard to their core functionalities, there may be significant differences in their specifications. Furthermore, the delivery of specifications towards the users is highly manufacturer-dependent and lacks coherent standardization. This is particularly relevant to professional contexts, such as defense, where individuals competently familiarizing themselves with their personal devices is essential. In this paper, we investigate the delivery of the specifications of the state-of-the-art smart wearable devices. We separately study the commonalities and best practices by device subcategories and usage contexts. We also highlight certain deviations on the current market and provide recommendations for the further evolution of such practices. The paper introduces the results of a study on documentation-related user behavior as well, in order to support future research.

关键词： smart wearables wearable devices wearable sensor technologies assistive wearable technologies device specification

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Special Challenges and Opportunities for Application of Bio-Medical sensors 13

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conference on smart biomedical and physiological sensor technology XIII

作者： Weinrich, Michael NIH Eunice Kennedy Shriver Inst Child Hlth & Human De Bethesda MD 20892 USA

ISBN: (纸本)9781510601048

Real-time data from sensors can potentially transform the practice of medicine. This article compares progress in genetics with sensors to draw some lessons for investigators developing sensor technologies. Special opportunities for research funding related to sensors at the NIH and challenges for sensor applications are highlighted.

关键词： sensors Medicine Genetics Clinical trials Analytics Biology Blood oxygen saturation Cancer Data centers sensor technology

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Towards a modular, robust, and portable sensing platform for biological and point of care diagnostics

Towards a modular, robust, and portable sensing platform for...

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conference on smart biomedical and physiological sensor technology X

作者： Finch, Amethist S. Bickford, Justin R. Conn, Marvin A. Coppock, Matthew B. Sarkes, Deborah A. Stratis-Cullum, Dimitra N. US Army Res Lab Sensors & Elect Devices Directorate Adelphi MD 20783 USA

ISBN: (纸本)9780819495105

The ability to conveniently and immediately test and diagnose in a diverse and rapidly changing environment is critical for field diagnostics. smart biomedical sensors employ many different diagnostic/therapeutic methodologies;however, an ideal system would include the ability for results to be shared instantaneously with all members of the team through a software interface. We discuss our efforts towards the development and use of a robust, mobile platform (Android-based smart phone) that incorporates stable molecular recognition elements in sensor development. The inexpensive, compact, robust, archival, and portable design is ideal for rapid field diagnostics.

关键词： Point of Care Molecular Recognition Hand-Held Device smart Phone Ubiquitous Sensing

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