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

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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 xv

Smart Biomedical and Physiological Sensor Technology XV

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smart biomedical and physiological sensor technology xv 2019

ISBN: (纸本)9781510627055

The proceedings contain 26 papers. The topics discussed include: determining driver nodes in dynamic signed biological networks;topographic imaging of an absorbing object in a tissue-like scattering medium using a single source-detector pair;development of army relevant integrated photonics MIP platform;biosensor platforms for biomarker detection: plasmonic aptasensors for detection of neuropeptide Y;electric fields assisted fluorescence enhancement for microRNA biomarker detection in serum samples: strategies for combating cancer, obesity and addiction to opioid;guided-mode resonance sensing of neuropeptide-Y with a sandwich assay achieving pg/mL detection;model reduction of structural biological networks by cycle removal;and ruggedized peptide receptors for soldier health and performance monitoring.

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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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Temporal and Strain Dependence of Scattering Coefficients for Fiber Optic Sensing 15

Temporal and Strain Dependence of Scattering Coefficients fo...

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conference on smart biomedical and physiological sensor technology xv

作者： Azizan, Syamimi Hambali, Nor Azura Malini Ahmad Retnasamy, Vithyacharan Abd Wahid, Mohamad Halim Shahimin, Mukhzeer Mohamad Univ Malaysia Perlis Sch Microelect Engn Tun Abdul Razak Laser Lab TAREL SPILS Kampus Pauh Putra Arau Perlis Malaysia Univ Malaysia Perlis Sch Microelect Engn Kampus Alam Arau 02600 Perlis Malaysia Natl Def Univ Malaysia UPNM Fac Engn Dept Elect & Elect Engn Kuala Lumpur 57000 Malaysia

ISBN: (数字)9781510618367

ISBN: (纸本)9781510618367

This paper presents a detailed synthesis of modeling and simulations results of a distributed Brillouin sensor using a newly developed algorithm. The behaviors of the backscattered Brillouin and Rayleigh signals in the optical fibers are studied. Various magnitudes of temporal and strain in the presence of coherent Rayleigh noise are performed and compared with the published experimental results. A model establishing the temporal and strain dependence on scattering coefficients have been developed and discussed in this paper. All developed simulation models illustrate outstanding precision vis-a-vis published measurement results. The work carried out in this paper cemented approach for a more complex distributed Brillouin scattering modeling.

关键词： Fiber optic sensing temperature strain scattering coefficients distributed Brillouin sensor

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Growth of bio sensor materials by physical vapor transport method 15

Growth of bio sensor materials by physical vapor transport m...

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conference on smart biomedical and physiological sensor technology xv

作者： Singh, N. B. Su, Ching Hua Arnold, Bradley Cullum, Brian M. Choa, Fow-Sen Carpenter, Tara Sachs, David Mandal, K. D. Univ Maryland Baltimore Cty Baltimore MD 21250 USA NASA Marshall Space Flight Ctr EM31 Huntsville AL 35812 USA Banaras Hindu Univ Indian Inst Technol Varanasi Uttar Pradesh India

ISBN: (数字)9781510618367

ISBN: (纸本)9781510618367

Recently there is a big thrust on bio-inspired sensors and there has been a large rise in the investment and expectations for nanotechnology to meet these goals. For in situ sensor development materials deposition on substrate is essential part of device development. Physical vapor deposition (PVD), chemical vapor deposition (CVD) and molecular organic vapor deposition methods have developed for growth of semiconductor bulk and thin film growth with some modifications have been used for these materials. Oxides and other elements of VI group such as sulfides and selenides are key components in skins of many species. Growth of ordered structures containing these elements have been achieved by using PVD method. This paper describes effect of growth parameters during PVD growth on the quality of materials. Growth kinetics and mechanism will be discussed for the vertical and horizontal growth reactors. Since most of the efficient materials systems are multinary and in many cases non-congruent, PVD provides pathway to grow materials below melting temperature.

关键词： Physical vapor transport oxides crystal bioinspired sensor growth

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Blast wave sensing from flexible piezoelectric materials 15

Blast wave sensing from flexible piezoelectric materials

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15th conference on smart biomedical and physiological sensor technology held at SPIE Defense + Commercial Sensing conference

作者： Willens, Kyle Mannschreck, Richard Muzinich, Blake Rosa, Christopher Kavlicoglu, Barkan Brennecka, Geoff Gordaninejad, Faramarz Adv Mat & Devices Inc Reno NV 89502 USA Colorado Sch Mines Dept Met & Mat Engn Golden CO 80401 USA

ISBN: (数字)9781510627062

ISBN: (纸本)9781510627055;9781510627062

This study investigates the response of flexible piezoelectric materials exposed to blast wave pressure impulses from in-air and underwater explosions. A shock tube was used to produce reproducible shock waves from explosions with average peak pressures in excess of 1,000 kPa for underwater experiments and 100 kPa for in-air experiments. Flexible piezoelectric polyvinylidene fluoride (PVDF) and lead zirconate titanate Pb(Zr,Ti)O3 (PZT) materials were tested for sensing the pressure impulse generated from an explosion. The rise time, peak amplitude, and duration of the blast wave pressure impulse were measured for each piezoelectric material and compared to an OEM blast wave sensor. This study uniquely identifies flexible piezoelectric materials that can accurately measure the blast wave pressure impulse from both in-air and underwater explosions. The accurate response and flexibility of the selected piezoelectric materials demonstrate the potential to be integrated into several forms of sensors, including wearable. Military and industrial applications can potentially benefit from a wearable blast wave sensor to improve medical diagnosis and treatment of blast exposure.

关键词： Blast wave shock wave smart materials sensing piezoelectric fibers piezoelectric films wearable devices sensors

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smart Wristband with Integrated Chemical sensors for Detecting Glucose Levels using Breath Volatile Organic Compounds 15

Smart Wristband with Integrated Chemical Sensors for Detecti...

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15th conference on smart biomedical and physiological sensor technology held at SPIE Defense + Commercial Sensing conference

作者： Shrestha, S. Harold, C. Boubin, M. Lawrence, L. Sonoma State Univ Dept Engn Sci Intelligent Syst Lab 1801 E Cotati Ave Rohnert Pk CA 94928 USA Miami Univ Dept Elect & Comp Engn 260 E High St Oxford OH 45056 USA

ISBN: (数字)9781510627062

ISBN: (纸本)9781510627055;9781510627062

This paper presents a microcontroller-based solution to classify blood glucose levels using acetone and ethanol breath volatile organic compounds. Two metal oxide semiconductor-based chemical sensors able to detect acetone and ethanol at parts per million concentrations were used. The sensors were tested in a controlled setup with humidified air spiked with acetone and ethanol, mimicking human breath corresponding to low and high blood glucose groups. A support vector machine algorithm was trained and implemented in a microcontroller. In a real time-time test, the trained algorithm classified low and high blood glucose groups with 97% accuracy. Subsequently, a smart wristband prototype that integrates the two sensors was developed. An Arduino-based wearable microcontroller platform was used for its small formfactor and a low-power operation. The wristband is enclosed in a 3D printed housing and powered by an onboard 3.7 V 500 mAh rechargeable Li-ion battery. A smartphone app communicates with the wristband through Bluetooth, allows data visualization, and saves data in the cloud. The presented work makes a significant contribution towards the development of a wearable device for detecting blood glucose levels from a patient's breath.

关键词： smart wristband wearable sensors smart sensing systems sensor data analysis blood glucose level diabetes volatile organic compounds

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