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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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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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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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Modelling and implementation of a Fixed-Length-Extension to measure fluorescent intensity in bioprocesses using an optical sensor 13

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conference on smart biomedical and physiological sensor technology xiii

作者： Sardesai, Neha Al-Adhami, Mustafa Rao, Govind Kostov, Yordan Univ Maryland Baltimore Cty Comp Sci & Elect Engn Dept Baltimore MD 21250 USA Univ Maryland Baltimore Cty Chem Biochem & Environm Engn Dept Baltimore MD 21250 USA

ISBN: (纸本)9781510601048

Fluorescent proteins are often used as reporters of protein concentration in biology and biomedicine applications. They can be detected using a fluorimeter equipped with fiber optics for ease of access. However, small changes in the path length due to change in the position, or immersion depth of the optical fiber results in large changes in readings. To alleviate the situation, the fiber is equipped with a fixed-length-extension that provides constant path length. The operation of the fiber equipped fluorimeter is theoretically modelled and practically verified in this paper.

关键词： Fluorescence GFP fixed-length extension optical sensor

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A highly sensitive pressure sensor using conductive composite elastomers with wavy structures 13

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conference on smart biomedical and physiological sensor technology xiii

作者： Sun, Rujie Zhang, Xiao-Chong Rossiter, Jonathan Scarpa, Fabrizio Univ Bristol Adv Composites Ctr Innovat & Sci Queens Bldg Bristol BS8 1TR Avon England Univ Bristol Dept Engn Math Merchant Venturers Bldg Bristol BS8 1UB Avon England

ISBN: (纸本)9781510601048

Flexible pressure sensors are crucial components for the next generation wearable devices to monitor human physiological conditions. In this paper, we present a novel resistive pressure sensor based on hybrid composites made from carbon nanotube (CNT) for the conductive coating layer and polydimethylsiloxane (PDMS) elastomers as the substrate. The high sensitivity of these sensors is attributed to the change of contact resistance caused by the variation of the contact areas between the wavy film and the electrodes. Porous electrodes were designed to increase the roughness of the interfaces, thus further enhancing the pressure sensitivity. The developed device was verified through a series of tests, and the sensor exhibited a high sensitivity of 2.05 kPa(-1) under a low pressure of 35.6 Pa,

关键词： pressure sensor contact resistance wavy structure porous electrodes nanocomposites

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Integration of biochemical sensors into wearable biomaterial platforms 13

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conference on smart biomedical and physiological sensor technology xiii

作者： Jandhyala, Sidhartha Walper, Scott A. Cargill, Allison A. Ozual, Abigail Daniele, Michael A. North Carolina State Univ Joint Dept Biomed Engn Raleigh NC 27695 USA Univ N Carolina Raleigh NC 27695 USA US Naval Res Lab Ctr Bio Mol Sci & Engn Washington DC 20375 USA US Naval Res Lab Naval Res Enterprise Internship Program Washington DC 20375 USA US Naval Res Lab Naval Res Lab HBCU MI Internship Program Washington DC 20375 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781510601048

With rapidly inflating healthcare costs, a limited supply of physicians and an alarming surge in lifestyle diseases, radical changes must be made to improve preventative medicine and ensure a sustainable healthcare system. A compelling solution is to equip the population with wearable health monitors to continuously record representative and actionable physiological data. Herein, we present a preliminary design and evaluation of a biochemical sensor node enabled by a substrate comprised of a nanocellulose thin-film that conforms to the skin and carries a printed sensor element. The nanocellulose layer ensures conformal and biocompatible contact with the skin, while a printed layer provides a high surface-area electrode. While the recognition/transduction element can be exchanged for many different sensing motifs, we utilize the general structure of an electrochemical glucose sensor.

关键词： Biosensors Wearable Electronics Biomaterials Nanocellulose Glucose sensor Glucose Oxidase Amperometric Electrochemical

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Effect of platinum nanoparticle deposition parameters on hydrogen peroxide transduction for applications in wearable electrochemical glucose biosensors 13

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conference on smart biomedical and physiological sensor technology xiii

作者： Cargill, Allison A. Neil, Kathrine M. Hondred, John A. McLamore, Eric. S. Claussen, Jonathan C. Iowa State Univ Dept Mech Engn Ames IA 50011 USA Univ Florida Dept Agr & Biol Engn Gainesville FL 32611 USA

ISBN: (纸本)9781510601048

Enhanced interest in wearable biosensor technology over the past decade is directly related to the increasing prevalence of diabetes and the associated requirement of daily blood glucose monitoring. In this work we investigate the platinum-carbon transduction element used in traditional first-generation glucose biosensors which rely on the concentration of hydrogen peroxide produced by the glucose-glucose oxidase binding scheme. We electrodeposit platinum nanoparticles on a commercially-available screen printed carbon electrode by stepping an applied current between 0 and 7.12 mA/cm(2) for a varying number of cycles. Next, we examine the trends in deposition and the effect that the number of deposition cycles has on the sensitivity of electrochemical glucose sensing. Results from this work indicate that applying platinum nanoparticles to screen printed carbon via electrodeposition from a metal salt solution improves overall biosensor sensitivity. This work also pinpoints the amount of platinum (i.e., number of deposition cycles) that maximizes biosensor sensitivity in an effort to minimize the use of the precious metals, viz., platinum, in electrode fabrication. In summary, this work quantifies the relationship between platinum electrodeposition and sensor performance, which is crucial in designing and producing cost-effective sensors.

关键词： biosensor glucose sensor electrodeposition platinum nanoparticles hydrogen peroxide screen printed electrodes

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