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

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Design of Wireless Based Telemonitoring System with biomedical sensor

Design of Wireless Based Telemonitoring System with Biomedic...

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IEEE International conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： A. O. Ogunsanya D. O. Daramola O. O. Ogunbiyi J. W. Sojobi Y. T. Oladijo S. T. Ajayi Department of Biomedical Engineering Bells University of Technology Ota Nigeria Department of Mechanical Engineering Bells University of Technology Ota Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Patient surveillance is critical in-order to decrease and/or avoid the spread of the chronic conditions. Although effective, the traditional healthcare systems of patient monitoring are limited, making it difficult for ambulatory patient seek medical care and have their physiological parameters evaluated. Advent of wireless technology and advancements in on-body sensor design have enabled a shift in the traditional health-care system. Thereby, allowing smart healthcare systems to take its place. This paper aims to describe the design and development of wireless based telemonitoring system with biomedical sensors for vulnerable individuals. Non-invasive biomedical sensors, signal condition system, Internet of Things (IoT), radio frequency module (RF) was used to implement the design. The sensors were used to collect physiological parameters before transferring over the hybrid transmission medium. Thing-Speak and graphic user interface (GUI) were used to display the results. The system design was successfully deployed, with efficient transmission of patient data with an average accuracy of 99.06%. The hybrid wireless monitoring system can give continuous physiological data as well as more detailed information about an individual's overall health. Hybrid wireless physiological parameter monitoring system has the potential of enhancing the quality of life of disease management individuals and lower healthcare expenditures.

关键词： Wireless communication Wireless sensor networks Surveillance Medical services Internet of Things Biosensors Reliability biomedical monitoring System analysis and design Graphical user interfaces

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Design of a Multi-sensor Wearable physiological Parameters Monitoring System Using Machine Learning Algorithm

Design of a Multi-Sensor Wearable Physiological Parameters M...

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smart Electronics and Communication Systems (ISENSE), International conference on

作者： Aarthy Prabakaran Elizabeth Rufus School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

An aging population and changing lifestyles have led to an increase in health problems, necessitating a robust and scalable medical system. A healthy society can be fostered by implementing a comprehensive health tracking system that enables early disease detection and continuous remote healthcare monitoring. This can be achieved by employing a wearable healthcare monitoring system, that can assist the medical sector in reducing hospitalizations and facilitating early diagnosis. To address this need, a Wearable physiological Monitoring System (W-PMS) is proposed in this study. A prototype has been developed with the capability to record electrocardiography (ECG), photoplethysmography (PPG), and body temperature, and calculate crucial vital parameters such as heart rate, saturation of peripheral oxygen (SpO2) level, and blood pressure (BP). The W-PMS comprises three sensor modules integrated with a microcontroller to collect the physiological data. The acquired physiological data are processed by the microcontroller to calculate certain parameters such as heart rate and SpO2 level. The acquired data are pushed to the cloud to estimate the BP through an indigenous Android application. The data transmission between the microcontroller and the application occurs via Bluetooth communication. The final results are visualized on a mobile device, enabling users to track their health status. In conclusion, wearable devices incorporating biomedical sensors have the potential to remotely monitor an individual's health condition.

关键词： Temperature measurement Temperature sensors Heart rate Microcontrollers Medical services Electrocardiography biomedical monitoring Wearable devices Monitoring Medical diagnostic imaging

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Design of a smart physiological Monitoring System for the Elderly to Enable Predictive, Preventive, and Personalized Medicine

Design of a Smart Physiological Monitoring System for the El...

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Electronic Information Engineering and Computer Communication (EIECC), International conference on

作者： Ziqing Cai Weitao Wang Han Zhang School of Law and Humanities China University of Mining and Technology (Beijing) Beijing China School of Artificial Intelligence China University of Mining and Technology (Beijing) Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

The global increase in the elderly population presents significant challenges to healthcare systems, requiring the development of innovative solutions for health monitoring. This study presents the design of a smart physiological monitoring system aimed at delivering predictive, preventive, and personalized medicine for elderly individuals. The overall system integrates a microcontroller-based hardware platform with Wi-Fi communication to enable real-time monitoring of key physiological metrics such as heart rate and body temperature. The system’s hardware design utilizes the STC89C52 microcontroller, interfaced with a heart rate detection circuit that employs the ADC0832 analog-to-digital converter to connect a heart rate sensor to the microcontroller, as well as a DS18B20 temperature sensor. The circuit is optimized for low power consumption and incorporates a Wi-Fi module. The software design of the system is based on an embedded platform, utilizing a lightweight real-time operating system to manage sensor data acquisition, processing, and communication. Algorithms for data filtering, anomaly detection, and real-time analysis are implemented to ensure accurate monitoring of physiological conditions. This study eventually concludes by emphasizing the system’s potential to improve elderly healthcare and support for predictive, preventive, and personalized medicine.

关键词： Temperature sensors Microcontrollers Precision medicine Medical services Real-time systems Hardware Older adults biomedical monitoring Monitoring Wireless fidelity

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Experimental Study on Designing Advanced Driver Assistance System for Two-Wheelers Using Mobility and physiological sensors

Experimental Study on Designing Advanced Driver Assistance S...

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IEEE International conference on Vehicular Electronics and Safety (ICVES)

作者： Daiwat Amit Vyas Manish Chaturvedi Naveen Kumar Karan Kamath Piyusha Gupta Aryan Dwivedi Department of Computer Science and Engineering Institute of Infrastructure Technology Research and Management (IITRAM) Institute of Technology Nirma University Department of Computer Science and Engineering IITRAM Indian Institute of Information Technology (IIIT) Vadodara SRM Institute of Science and Technology

ISBN: (数字)9798350387278

ISBN: (纸本)9798350387285

Advanced Driver Assistance Systems (ADAS) enhance driver safety through various technologies. In high-end cars, ADAS typically uses computer vision-based solutions, such as eye-tracking, to monitor driver attention, mood, and drowsiness. However, these ADAS solutions are challenging to implement for two-wheeler (2W) drivers, who constitute about 80% of drivers population in India, mainly due to helmet usage and the unique dynamics of 2W vehicles. This paper investigates improving 2W driver safety through integrating mobility and physiological sensors for ADAS. Experimental studies evaluated a novel ADAS framework combining mobility sensor data (e.g., speed, acceleration, position) with physiological parameters (e.g., heart rate, etc.). This fusion of sensors data approach enables monitoring vehicle dynamics and driver conditions for providing adaptive safety interventions by identifying aggressive/non-aggressive and stress/non-stress patterns. To facilitate the collection of mobility and physiological sensors data a mobile application was developed using Flutter. The physiological parameters were captured from the smart-watch paired with the smart-phone and the mobility parameters were captured from the mobility sensors that were in-built in the smart-phone. The data was collected in Navi-Mumbai area of India. Preliminary experiments conducted using these integrated sensors data show promising results. To incorporate supervised approach for Driver Behaviour Analysis (DBA) requires a tedious task of manual tagging and each driver reacts differently to the different situations that arise while driving. So, an un-supervised learning approach has been incorporated and clustering techniques like k-Means, Gaussian Mixture Model (GMM), Hierarchical Clustering, and k-Means with Dynamic Time Wrapping (DTW) analysed the sensor data to identify patterns indicating potential hazards or driver impairment. The findings based on the preliminary experimental studies done indicate t

关键词： Tagging sensor systems biomedical monitoring Vehicle dynamics Advanced driver assistance systems Driver behavior Wrapping Intelligent sensors Monitoring Vehicles

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smart Device Fall Detection System for Real-Time Monitoring and Emergency Response for Specially Abled Individuals

Smart Device Fall Detection System for Real-Time Monitoring ...

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Software, Systems and Information technology (SSITCON), International conference on

作者： N Rajesh A C Ramachandra V Viswanatha V Chathurima V N Pallavi Swati Pujari Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Fall detection systems are essential for protecting specially-abled individuals and the elderly, who face an increased risk of injury due to falls. This paper presents the design and implementation of a smart Device Fall Detection System aimed at real-time monitoring and emergency response, supported by a dedicated Data Analysis Module for post-fall analysis. The system integrates multiple sensors, including an accelerometer, gyroscope, pressure sensor, cardiotachometer, and GPS module, all connected through an ESP32 microcontroller. It continuously monitors users’ movements and physiological data, which are transmitted to the Blynk cloud service for analysis. The algorithm processes the sensor data to detect fall events and trigger emergency alerts. The Data Analysis Module further analyzes fall data to extract patterns and trends, enhancing the understanding of fall incidents and informing preventive measures. The proposed system was evaluated through various fall and non-fall scenarios, achieving an impressive accuracy of $97.9 \%$ and a response time of just 1.45 seconds. Compared to existing solutions, this system demonstrates enhanced performance in fall detection accuracy, sensitivity, and emergency response efficiency. Ultimately, the smart Device Fall Detection System, combined with the Data Analysis Module, offers a practical and scalable solution to improve the safety and well-being of individuals at risk of falls, paving the way for further advancements in fall prevention technology.

关键词： Data analysis Accuracy Emergency services sensor systems Real-time systems Time factors Fall detection smart devices biomedical monitoring Monitoring

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Does glucose affect our vision? A preliminary study using smart glasses

Does glucose affect our vision? A preliminary study using sm...

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Data Science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic conference on

作者： Natalia Piaseczna Rafał Doniec Szymon Sieciński Marcin Grzegorzek Ewaryst Tkacz Department of Biosensors and Processing of Biomedical Signals Faculty of Biomedical Engineering Silesian University of Technology 41-800 Zabrze Poland Institute of Medical Informatics University of Lübeck 23562 Lübeck Germany Fraunhofer IMTE Mönkhofer Weg 239a 23562 Lübeck Germany

In this study, we investigate the relationship between blood glucose levels and visual acuity during simulated driving using smart glasses and machine learning. We found compelling correlations between blood glucose levels and visual performance, with a classification accuracy of 98.70%. These results highlight the importance of blood glucose regulation for road safety and human well-being, and the potential of wearable sensor technology to advance our understanding of physiological influences on human performance.

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Detection of Mishap and Myocardial Infraction

Detection of Mishap and Myocardial Infraction

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Recent Innovation in smart and Sustainable technology (ICRISST), International conference on

作者： Pusphalatha N Sri Dhananjayan K Senthooriya O S Tharunprasath K J.Arulvadivu Gowtham M Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Coimbatore Department of Electronics and Communication Engineering Coimbatore Institute of Engineering and Technology Coimbatore Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Mishap is the most common cause of death if not detected in time. Myocardial infractions lead to a heavy loss to an individual’s family. The proposed system is a kind of health monitoring system with a location tracking facility that is used to identify if there are any accidents prone to the user or if the driver suddenly gets any high or low BPM. The system immediately senses the situation and sends information to the registered mobile number. The designed system uses a microcontroller chip, a GSM module, a gyroscopic sensor, and a pulse ox meter sensor to detect the user’s health condition. In the present scenario, lots of people are suffering from heart-related diseases. In order to reduce this, a technology using the Node MCU and GSM Module Track has been introduced. This will be processed when the driver gets a sudden cardio attack, a higher heart rate, or a lower heart rate. It will detect the oxygen level and give an alert to the user, admin, police, and the nearby ambulance. The goal of this research is to automatically assess a person's heart rate. This method can categorize, process, and evaluate key physiological information for long-term health monitoring applications.

关键词： Heart rate GSM Meters Technological innovation Microcontrollers Myocardium Real-time systems biomedical monitoring Monitoring Accidents

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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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Development of smart Mask System Integrated with Alert Detection and Vital-sign Measurement

Development of Smart Mask System Integrated with Alert Detec...

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IEEE International conference on Systems, Man and Cybernetics

作者： Wei-Lun Ting Chun-Chieh Hsiao Ren-Guey Lee Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan R.O.C. Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan R.O.C.

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This research focuses on enhancing worker safety in environments with high levels of TVOC gases, such as toluene, a neurotoxin. Traditional cartridge replacement methods, based on fixed intervals, lack precision and pose risks. We propose a novel approach using the SGP30 sensor to monitor cartridge effectiveness, aligned with PEL-TWA regulations. Our system triggers alerts when sensor readings exceed 10 ppm for toluene, 35 ppm for carbon monoxide, and 5000 ppm for carbon dioxide. Additionally, we incorporate physiological monitoring via Photoplethysmography (PPG) using the AFE4404 sensor to assess Heart Rate (HR) and Heart Rate Variability (HRV), alongside respiration monitoring with the D6F-P0010AM2 airflow sensor. Data from these sensors is transmitted via low-power Bluetooth to a mobile APP, enabling real-time monitoring of the wearer's condition. In case of cartridge failure or abnormal physiological readings, the APP triggers vibrations on the mask and automatically sends an SOS to the employer's server via UDP protocol, ensuring immediate intervention. This system aims to enhance occupational safety by reducing the risk of accidents and safeguarding worker well-being.

关键词： Vibrations Protocols Occupational safety Real-time systems Safety Mobile applications Servers biomedical monitoring Heart rate variability Monitoring

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Transformative Potential IoT sensor Frameworks for Real-Time Alzheimer's Detection and Monitoring in Elderly Populations

Transformative Potential IoT Sensor Frameworks for Real-Time...

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Advances in Computing Research on Science Engineering and technology (ACROSET), International conference on

作者： Kamlesh Mani Kamlesh Kumar Singh Ratnesh Litoriya Computer science and engineering Amity University Lucknow India Electronics and Communication Amity University Lucknow India Computer science and engineering Medi-Caps University Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This review paper presents a comprehensive overview of IoT sensor-based frameworks for real-time Alzheimer's disease detection and monitoring in elderly populations. The paper examines the current state of Alzheimer's disease, including its definition, stages, symptoms, and challenges in diagnosis. It explores the importance of early detection and monitoring in improving patient outcomes and highlights the role of IoT sensors in enabling real-time monitoring of physiological parameters, activity levels, and behavior patterns associated with Alzheimer's disease. The paper discusses various applications of IoT sensors in healthcare and disease monitoring, including smart home monitoring systems, wearable sensor-based interventions, and community-based screening programs. It analyzes methodologies, technologies, and outcomes in IoT sensor-based research, emphasizing the integration of machine learning algorithms for real-time analysis of sensor data. Additionally, the paper addresses challenges and future directions in the field, including regulatory and ethical considerations, technical challenges, and potential advancements. Overall, this review paper underscores the transformative potential of IoT sensor-based frameworks in improving early detection, personalized care, and disease management for individuals living with Alzheimer's disease.

关键词： Machine learning algorithms Reviews Medical services Real-time systems sensors Alzheimer's disease biomedical monitoring Older adults Monitoring Intelligent sensors

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