This document presents a new methodology to detect the effects of emotions on different biometrics in real time. Two designs were implemented based on a microcontroller and National Instruments myRIO to measure four vital parameters (temperature, heartbeat, blood pressure, body resistance) in real-time while recording the effects of different emotions on those parameters. Over 400 people were tested while exposed to videos and music representing different emotions. The results showed that the design using NI myRIO achieved more accurate results and faster response time compared to the microcontroller-based design, qualifying it for use in intensive care units. The methodology contributes to early diagnosis of diseases by analyzing the impact of emotions on vital readings.
Ecis final paper-june2017_two way architecture between iot sensors and cloud ...Oliver Neuland
Improving health care with IoT - Research into a weight monitoring bed - ECIS 2017 paper.
Resulting from smart furniture applications research project in Germany, Oliver Neuland and partners from AUT developed a smart bed concept which utilizes weight monitoring for AAL and elderly care. Initially strategies were applied to find meaningful use cases, later a prototype was developed. Here a paper presented during ECIS in Portugal which describes the architecture of the prototype.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
Activity and health monitoring systems
This paper presents an Open Platform Activity and health monitoring systems which are also called e-Health systems. These systems measure and store parameters that reflect changes in the human body. Due to continuous monitoring (e.g. in rest state and in physical effort state), a specialist can learn about the individual's physiological parameters. Because the human body is a complex system, the examiner can notice some changes within the body by looking at the physiological parameters. Six different sensors ensure us that the patient's individual parameters are monitored. The main components of the device are: A Raspberry Pi 3 small single-board computer, an e-Health Sensor Platform by Cooking-Hacks, a Raspberry Pi to Arduino Shields Connection Bridge and a 7-inch Raspberry Pi 3 touch screen. The processing unit is the Raspberry Pi 3 board. The Raspbian operating system runs on the Raspberry Pi 3, which provides a solid base for the software. Every examination can be controlled by the touch screen. The measurements can be started with the graphical interface by pressing a button and every measured result can be represented on the GUI’s label or on the graph. The results of every examination can be stored in a database. From that database the specialist can retrieve every personalized data
An internet of things-based automatic brain tumor detection systemIJEECSIAES
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection systemnooriasukmaningtyas
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.
Intelligent Healthcare Monitoring in IoTIJAEMSJORNAL
The developing of IoT-based health care systems must ensure and increase the safety of the patients, their quality of life and other health care activities. We may not be aware of the health condition of the patient during the sleeping hours. To overcome this problem. This paper proposes an intelligent healthcare monitoring system which monitors and maintains the patient health condition at regular intervals. The heart rate sensor and temperature sensor would help us analyze the patients’ current health condition. In case of major fluctuations in consecutive intervals a buzzer is run in order to notify the hospital staff and doctors. The monitored details are stored in the cloud "ThingSpeak". The doctor can view the patient health condition using Virtuino simulator. This system would help in reducing the random risks of tracing a patient medical highly. Arduino UNO is used to implement this intelligent healthcare monitoring system.
Ecis final paper-june2017_two way architecture between iot sensors and cloud ...Oliver Neuland
Improving health care with IoT - Research into a weight monitoring bed - ECIS 2017 paper.
Resulting from smart furniture applications research project in Germany, Oliver Neuland and partners from AUT developed a smart bed concept which utilizes weight monitoring for AAL and elderly care. Initially strategies were applied to find meaningful use cases, later a prototype was developed. Here a paper presented during ECIS in Portugal which describes the architecture of the prototype.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
Activity and health monitoring systems
This paper presents an Open Platform Activity and health monitoring systems which are also called e-Health systems. These systems measure and store parameters that reflect changes in the human body. Due to continuous monitoring (e.g. in rest state and in physical effort state), a specialist can learn about the individual's physiological parameters. Because the human body is a complex system, the examiner can notice some changes within the body by looking at the physiological parameters. Six different sensors ensure us that the patient's individual parameters are monitored. The main components of the device are: A Raspberry Pi 3 small single-board computer, an e-Health Sensor Platform by Cooking-Hacks, a Raspberry Pi to Arduino Shields Connection Bridge and a 7-inch Raspberry Pi 3 touch screen. The processing unit is the Raspberry Pi 3 board. The Raspbian operating system runs on the Raspberry Pi 3, which provides a solid base for the software. Every examination can be controlled by the touch screen. The measurements can be started with the graphical interface by pressing a button and every measured result can be represented on the GUI’s label or on the graph. The results of every examination can be stored in a database. From that database the specialist can retrieve every personalized data
An internet of things-based automatic brain tumor detection systemIJEECSIAES
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection systemnooriasukmaningtyas
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.
Intelligent Healthcare Monitoring in IoTIJAEMSJORNAL
The developing of IoT-based health care systems must ensure and increase the safety of the patients, their quality of life and other health care activities. We may not be aware of the health condition of the patient during the sleeping hours. To overcome this problem. This paper proposes an intelligent healthcare monitoring system which monitors and maintains the patient health condition at regular intervals. The heart rate sensor and temperature sensor would help us analyze the patients’ current health condition. In case of major fluctuations in consecutive intervals a buzzer is run in order to notify the hospital staff and doctors. The monitored details are stored in the cloud "ThingSpeak". The doctor can view the patient health condition using Virtuino simulator. This system would help in reducing the random risks of tracing a patient medical highly. Arduino UNO is used to implement this intelligent healthcare monitoring system.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASES pijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT)
Real-time Heart Pulse Monitoring Technique Using Wireless Sensor Network and ...IJECEIAES
Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (userfriendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.
A survey on bio-signal analysis for human-robot interactionIJECEIAES
The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems.
Galvanic Skin Response Data Classification for Emotion Detection IJECEIAES
Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
The Analysis of Performace Model Tiered Artificial Neural Network for Assessm...IJECEIAES
The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, that is the tiered approach. This makes the assessment process should conduct a thorough examination. This study aims to analyze the performance of a tiered model assessment. The assessment system is divided into several levels, with reference to the stages of the inspection procedure.The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The effect indicates that the level above gives performance improvement and or strengthens the performance at the previous level.
WBSN based safe lifestyle: a case study of heartrate monitoring system IJECEIAES
A Heart is the vital organ of the body. According to the “world health statistics 2017” by WHO, about 460,000 people die due to fatal heart attacks every year. To reduce the death rate due to fatal heart attacks and malfunctioning of the cardiovascular system, this paper proposed a Wireless Body Sensor Network (WBSN) based, portable, easily affordable, miniatured, accurate “Heartrate Monitoring System (HMS)”. HMS can be used to regularly examine the cardiac condition at home or hospital to avoid or early detection of any serious condition. Heartrate Monitoring Algorithm (HMA) was designed to observe the spread heartbeat spectrum and worked at the backend of HMS. A case study was performed for forty healthy young subjects. Each subject data was computed for 푠푢푏 ̅̅̅̅̅ − 3푆 푑 < 푠푢푏 < 푠푢푏 ̅̅̅̅̅ + 3푆 . All subjects’ 99% data lie in the custom range. The result produced by HMS was the same as the previous medical record of subjects.
Framework for propagating stress control message using heartbeat based iot re...IJECEIAES
Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.
Electrocardiogram signal processing algorithm on microcontroller using wavele...IJECEIAES
The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.
Internet of Things IoT Based Healthcare Monitoring System using NodeMCU and A...ijtsrd
Today Internet has become one of the important parts of daily life. It has changed how people live, work, play and learn. Internet serves for many purposes educations, finance, Business, Industries, Entertainment, Social Networking, etc. The IoT is connected objects to the Internet and used to control of those objects or remote monitoring. A health care monitoring system is necessary to constantly monitor the patient's physiological parameters. The main advantage of this system is that the results can be viewed at any time and place. The doctors can be notified by using mobile phones messages if patient health is abnormal. In this system, heartbeat sensor, temperature sensor and blood pressure sensor are used. The system can analyze the signal to detect normal or abnormal conditions. In the system, the internet of things IoT is becoming a major platform for many services and applications. The IoT is generally considered as connecting objects to the Internet and using that connection for control of those objects or remote monitoring. Khin Thet Wai | Nyan Phyo Aung | Lwin Lwin Htay "Internet of Things (IoT) Based Healthcare Monitoring System using NodeMCU and Arduino UNO" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26482.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26482/internet-of-things-iot-based-healthcare-monitoring-system-using-nodemcu-and-arduino-uno/khin-thet-wai
IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
More Related Content
Similar to New methodology to detect the effects of emotions on different biometrics in real time
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASES pijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT)
Real-time Heart Pulse Monitoring Technique Using Wireless Sensor Network and ...IJECEIAES
Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (userfriendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.
A survey on bio-signal analysis for human-robot interactionIJECEIAES
The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems.
Galvanic Skin Response Data Classification for Emotion Detection IJECEIAES
Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
The Analysis of Performace Model Tiered Artificial Neural Network for Assessm...IJECEIAES
The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, that is the tiered approach. This makes the assessment process should conduct a thorough examination. This study aims to analyze the performance of a tiered model assessment. The assessment system is divided into several levels, with reference to the stages of the inspection procedure.The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The effect indicates that the level above gives performance improvement and or strengthens the performance at the previous level.
WBSN based safe lifestyle: a case study of heartrate monitoring system IJECEIAES
A Heart is the vital organ of the body. According to the “world health statistics 2017” by WHO, about 460,000 people die due to fatal heart attacks every year. To reduce the death rate due to fatal heart attacks and malfunctioning of the cardiovascular system, this paper proposed a Wireless Body Sensor Network (WBSN) based, portable, easily affordable, miniatured, accurate “Heartrate Monitoring System (HMS)”. HMS can be used to regularly examine the cardiac condition at home or hospital to avoid or early detection of any serious condition. Heartrate Monitoring Algorithm (HMA) was designed to observe the spread heartbeat spectrum and worked at the backend of HMS. A case study was performed for forty healthy young subjects. Each subject data was computed for 푠푢푏 ̅̅̅̅̅ − 3푆 푑 < 푠푢푏 < 푠푢푏 ̅̅̅̅̅ + 3푆 . All subjects’ 99% data lie in the custom range. The result produced by HMS was the same as the previous medical record of subjects.
Framework for propagating stress control message using heartbeat based iot re...IJECEIAES
Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.
Electrocardiogram signal processing algorithm on microcontroller using wavele...IJECEIAES
The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.
Internet of Things IoT Based Healthcare Monitoring System using NodeMCU and A...ijtsrd
Today Internet has become one of the important parts of daily life. It has changed how people live, work, play and learn. Internet serves for many purposes educations, finance, Business, Industries, Entertainment, Social Networking, etc. The IoT is connected objects to the Internet and used to control of those objects or remote monitoring. A health care monitoring system is necessary to constantly monitor the patient's physiological parameters. The main advantage of this system is that the results can be viewed at any time and place. The doctors can be notified by using mobile phones messages if patient health is abnormal. In this system, heartbeat sensor, temperature sensor and blood pressure sensor are used. The system can analyze the signal to detect normal or abnormal conditions. In the system, the internet of things IoT is becoming a major platform for many services and applications. The IoT is generally considered as connecting objects to the Internet and using that connection for control of those objects or remote monitoring. Khin Thet Wai | Nyan Phyo Aung | Lwin Lwin Htay "Internet of Things (IoT) Based Healthcare Monitoring System using NodeMCU and Arduino UNO" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26482.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26482/internet-of-things-iot-based-healthcare-monitoring-system-using-nodemcu-and-arduino-uno/khin-thet-wai
IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Student information management system project report ii.pdf
New methodology to detect the effects of emotions on different biometrics in real time
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 2, April 2023, pp. 1358~1366
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp1358-1366 1358
Journal homepage: http://ijece.iaescore.com
New methodology to detect the effects of emotions on different
biometrics in real time
Yahia Zakria Abd Elgawad, Mohamed I. Youssef, Tarek Mahmoud Nasser
Faculty of Engineering, Al-Azhar University, Cairo, Egypt
Article Info ABSTRACT
Article history:
Received Mar 10, 2022
Revised Sep 26, 2022
Accepted Oct 26, 2022
Recently, some problems have appeared among medical workers during the
diagnosis of some diseases due to human errors or the lack of sufficient
information for the diagnosis. In medical diagnosis, doctors always resort to
separating human emotions and their impact on vital parameters. In this
paper, a methodology is presented to measure vital parameters more
accurately while studying the effect of different human emotions on vital
signs. Two designs were implemented based on the microcontroller and
National Instruments (NI) myRIO. Measurements of four different vital
parameters are measured and recorded in real time. At the same time, the
effects of different emotions on those vital parameters are recorded and
stored for use in analysis and early diagnosis. The results proved that the
proposed methodology can contribute to the prediction and diagnosis of the
initial symptoms of some diseases such as the seventh nerve and Parkinson’s
disease. The two proposed designs are compared with the reference device
(beurer) results. The design using NI myRIO achieved more accurate results
and a response time of 1.4 seconds for real-time measurements compared to
its counterpart based on microcontrollers, which qualifies it to work in
intensive care units.
Keywords:
Bio measurements
Galvanic skin response
Microcontroller
MyRIO
Sensors
This is an open access article under the CC BY-SA license.
Corresponding Author:
Yahia Zakria Abd Elgawad
Faculty of Engineering, Al-Azhar University
Cairo, Egypt
Email: yahia_elrefaay@hotmail.com
1. INTRODUCTION
As information technology and medical discoveries advanced, it became vital to combine the two in
order to improve the quality of medical services supplied to patients. So, Brucal et al. [1] developed a
portable electrocardiogram (ECG) device based on National Instruments (NI) myRIO 1900. The NI LabView
program was used to develop a graphical interface to facilitate reading and analysis of the results of the
proposed device. The results proved that the proposed system achieved an accuracy rate of 90.68% compared
to other devices available in the local market. The researchers also confirmed that the system can be used in
remote places that are difficult for doctors to reach due to its portable design. Eesee [2] conducted a galvanic
skin response (GSR) study to monitor the agitation and tension of a group of volunteers and analyze the GSR
signal while performing a walking exercise on the treadmill. The results and statistical analyzes showed that
using pictures only as an emotional stimulus did not give the desired results [2]. Karanchery and
Palaniswamy [3] have worked to provide a solution using machine learning technology to help healthcare
providers deal with individuals with autism spectrum disorders. A machine learning model is designed that
uses emotional data captured for individuals to identify and display emotions. The accuracy of the model
reached (99.8%) with a total of 125 training images. Azgomi et al. [4] create a simulation environment to
study the state of kinetic stress in a closed motion method using GSR. An easy filter was used for diagnosis
2. Int J Elec & Comp Eng ISSN: 2088-8708
New methodology to detect the effects of emotions on different biometrics in … (Yahia Zakria Abd Elgawad)
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and analysis. The results confirmed that the proposed structure is an initial step for diagnosing cognitive
disorders related to the state of the brain. Satti et al. [5] used GSR to collect data for a group of volunteers to
study and analyze stress patterns based on a learning model. The results proved that the accuracy of the
analysis reached (63.39%) compared to similar models. The proposed model also achieved an increase in the
rate of class recall to determine stress, which reached (27.08%). Hanoon and Aal-Nouman presented an
internet of things (IoT) application connected to a low-cost health monitoring device. The proposed device
for health monitoring depends on the Arduino to measure the heart rate and the level of oxygen saturation in
the blood [6].
Some patient health monitors relied on Arduino UNO, Arduino NANO, Raspberry pi, and
PIC16f877 as a processor for these systems. Advanced technologies were used to transfer data from patients
to doctors in hospitals and patient’s relatives. Where Xbee used a short message service (SMS) to send data
to doctors. The data was processed in real-time using LabVIEW. Some researchers also tried to use and
repurpose these systems to measure vital parameters in other similar research [7]–[13]. Previous research
relied on designing systems using Arduino and some sensors to measure vital parameters. Data is transmitted
over the IoT and machine learning and fuzzy logic techniques are used to analyze different data. Most of
these systems help to notify physicians as quickly as possible of a patient’s medical condition and past
medical history. Various technologies are used to transmit information such as Wi-Fi and GSM. The
researchers did not address the impact of positive and negative emotions on these measurements [14]–[25].
Lubis et al. [26] presented a model that relies entirely on industrial intelligence to assess the
implementation of laws and study caliphs and the principles based on the assessment of crimes. The study
proved that the model is effective as a first model in the same matter, and the researchers recommend an
evolution. Ibrahim et al. [27] Create a graphical interface to characterize the respiratory conditions of the
respiratory system. LabView program was used to create the graphic interface with the addition of a software
algorithm that receives data from a set of sensors. The proposed model has proven effective in measuring
lung parameters with high accuracy and is connected to 81.1%. Hutagalung et al. [28] provided a cumulative
analysis of the performance of family planning trainers and analysis of the factors supporting improving their
work performance. Use in the analysis of more than one ready statistical model dependent on the machine
learning algorithms. The results of the analysis using ready-made algorithms were almost satisfactory and
showed effectiveness in assessing cumulative performance. Despite the efforts of the aforementioned
researchers, they did not address the development and design of low-cost devices that work in the actual time
to monitor the health condition or performance analysis. None of the researchers presented how to remove
the chosen receiving signals and did not provide an automatic learning model that enables prediction or
diagnosis.
This paper describes two designs implemented using microcontrollers and myRIO from National
Instruments Corporation. By these two designs, four parameters (temperature-heartbeat-blood pressure-body
resistance) are measured in real-time. Relying on the heart rate sensor, blood pressure sensor, temperature
sensor, as well as GSR sensor, the results are recorded and saved to analyze people’s feelings. A database is
established for more than four hundred people who are tested, ranging in age from 3 to 72. The people are
exposed to a set of short videos and some music representing different emotional states (anger, sadness,
happiness, fear, neutrality). At the same time, the change in their vital readings is recorded. This paper
contributes to diagnosing the initial symptoms of some diseases such as (Seventh nerve-Parkinson’s) by
analyzing people’s feelings and their impact on vital readings. The indicated contributions are presented in
several sections where electronic circuits designed for the proposed devices using microcontrollers and
myRIO are explained in section 2. The results and decision-making are presented in section 3. Conclusions
and future work are described in section 4.
2. METHOD
2.1. Research significance
The main contribution of this paper is to design an integrated mobile system for actual time. This
paper addresses previous problems, as it provides a lower financial cost system, efficiency, and high
measurement accuracy. The research methodology presented contributes to providing a sufficient study on
the effect of human feelings on vital parameters while providing a learning model capable of diagnosis and
classification based on these data.
2.2. Functional scheme
Two designs are built based on a microcontroller (ATMEGA 16) and NI myRIO using different
sensors to measure vital parameters of patients. Figure 1 illustrates the components used in these designs:
i) pulse sensor to measure heart rate, ii) a galvanic skin response sensor that detects changes in body posture
via the skin, iii) a thermometer to measure body temperature, and iv) blood pressure sensor BMP085 to
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measure blood pressure (systolic-diastolic). After measuring the vital signs of the patients, the measured data
is sent to the computer for analysis using the MATLAB program. To provoke certain emotions, test
participants are given brief videos and photographs. Anger, sorrow, happiness, fear, and neutrality are among
the five emotions represented by the five videos. Each of the videos utilized in this study reflects a distinct
emotional state. It also shows five groups of pictures to everyone, each representing a distinct feeling. Each
group of photos has 50 photographs depicting a certain feeling. Vital data (temperature, heart rate, blood
pressure, and body resistance) are monitored at the same time as the photos and videos are shown. The
MATLAB application saves and analyses the data for use in forecasting and early detection.
2.3. System circuits
Figure 2 shows a circuit of the proposed system. The circuit diagram is designed with the aid of
easy. This algorithm is important to handle the drawing of big circuits like in our case. Let’s go to describe
the details of each one of the designs in Figures 2(a) and 2(b). The first design using NI myRIO connected to
the aforementioned sensors to measure heart rate, blood pressure, body temperature, and skin resistance as
shown in Figure 2(a).
Figure 1. The design structure’s block diagram
(a)
(b)
Figure 2. A circuit of the proposed system (a) design one using NI myRIO and (b) design two using
microcontroller
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NI myRIO is a tool that allows the use of real-time (RT) and field-programmable gate array (FPGA)
capabilities. The main reason for choosing this board is that two of its most important features are the two
analog outputs connected to the ground, which allow communication with countless sensors and devices and
programmable control of the systems. A data acquisition board (DAQ) is linked to convert analogue
waveforms to digital values and to eliminate noise from measurements. An array of light-emitting diodes
(LEDs) is used in the circuit to indicate the reading on the liquid-crystal display (LCD) screen. Green lights
indicate normal, yellow indicates medium, and red indicates dangerous. At the same time, the readings
appear on the LCD screen, then the readings are sent to the computer for analysis. All sensors are connected
to amplifiers to amplify the measured signals. The pulse sensor (S1) is a medical sensor used for non-
invasive heart rate monitoring. The pulse sensor responds to relative changes in light intensity by reflecting
light from the sensor’s built-in green LED during each pulse. The galvanic skin response (GSR) sensor (S2)
is used to determine how much change there is in electrical resistance values. The sweat glands are controlled
by the human body’s nervous system, so when a person experiences strong emotional moments, the electrical
resistance of the skin changes. The temperature sensor (S3) is used to measure the temperature of the human
body. Blood pressure sensor BMP085 (S4) to measure (diastolic-systolic blood pressure). Measurements are
sent in real-time to a computer to analyze the results and use them in early diagnosis and forecasting
programs.
Figure 2(b) shows the second design using the ATmega328P microcontroller. This microcontroller
is selected based on its availability in the local market and its ease of programming. Sensors used to measure
patients’ vital parameters are connected to an amplifier to further amplify the received signal. The amplifier
output of each sensor is connected to the analog-to-digital converter (ADC1) output control unit on the
microcontroller. Sensors on the circuit are indicated by (S1-S2-S3-S4). Where S1 is an indicator of the heart
rate sensor. S2 stands for the galvanic skin response sensor. The human body temperature sensor can be
found under the symbol S3. The pressure sensor for measuring blood pressure is denoted by S4. The circuit is
designed so that more than one sensor can be connected to it in the future. A set of LED lights are used to
indicate the reading of the sensors. The lighting is green if the measurements are in the normal position. The
mean average of the measurements is indicated by yellow illumination. The circuit gives a red-light warning
when the measurements are at a critical level. All measurements are shown on an LCD screen connected
directly to the microcontroller.
3. RESULTS AND DISCUSSION
In our research, participants are expected to watch brief movies and images to provoke specific
emotions. Patients’ reactions are captured while they watch live films (5 videos), each representing one of the
five main emotions (anger, sadness, happiness, fear, and neutrality). Each video is limited to 4 minutes in
length, and all vital signs of the persons being evaluated are measured before, during, and after the exam.
Each person being evaluated is shown five sets of images, each representing a different emotion. Each group
of photos has 50 visuals depicting a certain feeling. Vital parameters (temperature, heart rate, blood pressure,
and body resistance) are monitored at the same time. The MATLAB program keeps track of the results and
analyses them for use in prediction and early detection systems.
3.1. Effect of emotions on blood pressure measurements
Table 1 presents the systolic and diastolic blood pressure data acquired using the three devices. The
Table 1 shows the average results of blood pressure measurements for five moods. The resulting findings are
compared to certified reference devices utilizing the created devices.
Table 1. Average blood pressure readings
AGE
Systolic BP Diastolic BP Time(sec) Emotion
type
NI Micro-
controller
Reference
value
NI Micro-
controller
Reference
values
NI Micro-
controller
Reference
values
20 122 126 120 80 80 80 20 21 20 Neutral
(calm)
60 121 121 121 80 80 80 20 25 20
20 130 133 131 85 82 85 21 23 22 Angry
60 153 153 153 99 105 100 23 26 20
20 113 113 113 75 75 75 23 26 20 Sad
60 99 98 98 70 70 70 20 25 23
20 120 126 120 81 81 81 20 26 21 happy
60 120 120 120 80 81 80 19 23 21
20 113 115 115 59 62 60 20 24 20 Fear
60 146 139 144 94 90 94 19 24 21
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Table 1 denotes a calm state, in which blood pressure measurements taken with the NI myRIO are
close to those taken with the reference device. In the anger stage, the NI myRIO-based design delivers
outcomes that are as near to the reference device. In the sad condition, the outcomes of the microcontroller-
based design are comparable to those of the reference device. NI myRIO gives values that are remarkably
close to the reference device readings in both the joyful and terror phases. Figure 3 shows the relationship
between blood pressure measurements versus age using the proposed devices. The results of the reference
device are shown by a dotted line, while the Dash-style font displays the design results based on the
microcontroller, and the solid font style is used to separate the design results based on myRIO.
Figure 3(a) indicates that the myRIO design’s normal-state diastolic blood pressure values are
comparable to those of the reference device. Figure 3(b) illustrates that the systolic blood pressure
measurements varied in rising and fall, despite the fact that the myRIO device generated essentially identical
findings to the reference device.
(a) (b)
Figure 3. The relationship between BP and age using three devices (a) indicates that normal-state diastolic
blood pressure and (b) indicates that normal-state systolic blood pressure
3.2. Effect of emotions on heart rate measurements
Table 2 presents the pulse rate data obtained from three devices. The data in the Table 2 are the
averages of pulse rate measurements for five human emotions. It is noticed in Table 2 that the time taken to
measure using myRIO is closer to the time taken to measure using reference devices. It is also noted that the
device designed using the microcontroller gives a relatively long time to measure compared to the time taken
to measure using the reference device. Table 2 also indicates the difference in heart rate readings across the
three devices utilized for the measurement; nonetheless, the values obtained with the myRIO device are the
most similar to the data obtained with the reference device.
Figure 4 depicts the graphical association between pulse rate and age in the normal and angry states
of the three devices. The dot-style line represents the results obtained with the reference device, the dash
style illustrates the results obtained with the microcontroller, and the solid-style results are obtained with the
myRIO device. Figures 4(a) and 4(b) depict the shift in heart rate during normal and angry states,
respectively.
Table 2. Estimates of average desired heart rate
Age Heart Rate Time (Sec) Emotion type
NI Microcontroller Reference Value NI Microcontroller Reference Value
20 80 75 80 20 21 20
Neutral (calm)
60 80 90 79 19 25 21
20 95 96 95 19 20 20 Angry
60 101 95 99 20 26 20
20 70 74 70 21 28 22 Sad
60 64 61 64 20 18 20
20 83 80 80 19 19 20 happy
60 85 79 79 20 21 20
20 95 92 95 20 21 20 Fear
60 105 100 100 20 21 20
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(a) (b)
Figure 4. The relationship between pulse rate and age using three devices (a) heart rate during normal state
and (b) heart rate during angry state
3.3. Effect of emotions on temperature measurements
Table 3 illustrates that there is a modest variation in temperature data as people’s emotional states
vary. The temperature change utilizing the three suggested devices is shown in this table. When the three
devices were utilized, Figure 5 depicts the association between body temperature and age. The results of
using the reference device are presented in the dotted line, while the results of using the microcontroller are
shown in the dashed line, and the results of using the myRIO device are shown in the solid line. Figures 5(a)
and 5(b) depict the temperature change in the normal and angry states, respectively.
Table 3. Body temperature on average readings
Age Temperature measurements (degrees Celsius) Time (Sec) Emotion type
NI Microcontroller Reference Value NI Microcontroller Reference Value
20 36.6 36.9 36.5 60 60 60
Neutral (calm)
60 37.1 36.6 37 60 60 60
20 37 37 37.5 60 60 60 Angry
60 37 37.1 37 60 60 60
20 36.4 36.1 36.5 60 60 60 Sad
60 36.6 36.6 36.6 60 60 60
20 37.1 37.2 37 60 60 60 happy
60 37.3 37 37.2 60 60 60
20 36.3 36.5 36.3 60 60 60 Fear
60 36 36.3 36.1 60 60 60
(a) (b)
Figure 5. Relationship between body temperature and age using three devices (a) temperature in normal state
and (b) temperature in angry state
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3.4. Effect of emotions on GSR measurements
Because the GSR signal has a low frequency (2 Hz), we employed a low-pass filter to eliminate any
noise. An Op-amp is a buffered signal that is used to convert high impedance to low impedance. The voltage
across the resistance of the body is:
Va = 50MΩ/(50MΩ + R6) Vi (1)
which the output voltage is:
Vo = R4/R3 (Vref − Va) + Vref (2)
The (1) depicts the connection between the voltage flowing through the resistance of the human body (Va)
and the input voltage (Vi). The connection between the output voltage (Vo) and the reference voltage (Vref) is
depicted in (2). Table 4 demonstrates how the resistance value (R6) and voltage values (Va) and (Vo) alter
depending on the person’s emotional state. Table 4 shows that the value of R6 grows as the value of Va
drops. As indicated in Table 5, the statistical analysis approach is utilized to determine the correctness of the
various findings obtained from the reference device and devices suggested in this work.
The proposed machine learning model depends on data for training, learning, and improving
accuracy over time. A set of vital data available internet medical web [29] is used as an input to train the
model. When using the form to decide the actual time, the model compares the new inputs with the inputs
stored during training, and the decision is extracted with the highest accuracy possible. And to get the best
performance of the proposed form and the highest accuracy of the diagnosis, a set of algorithms was tried and
know what is best. Figure 6 shows the method of designing the proposed learning program.
Table 4. Emotional changes in Va and Vo
Age R Vo Va Emotion
20 1.00E+05 0.291 4.993 neutral
60 3.80E+05 0.812 3.992
20 6.10E+05 0.664 3.942 angry
60 1.30E+06 0.881 3.767
20 2.60E+06 0.983 3.197 sad
60 5.80E+06 1.667 2.743
20 2.00E+07 2.141 2.083 happy
60 3.80E+07 2.551 1.677
20 6.60E+05 0.464 3.642 fear
60 1.00E+06 0.581 3.467
Table 5. Statistical analysis of average measured data
Parameters Heart rate Blood pressure (diastole) Blood pressure (systolic) Temperature
Ref device NI Micro Ref device NI Micro Ref device NI Micro Ref device NI Micro
Min 75.1 75.3 76 81.5 80 82 75.5 76 76.6 77.3 77 78
Max 97.5 98 98.5 94 95 96 98.1 98 99 99 99 98.5
Mean 86 85.3 85.9 87 86.9 86.9 86 87 86.5 87 87.5 86.2
Median 86 85.9 86.5 87.5 88 89.6 87 86.5 85.2 88 88.7 88
Figure 6. block diagram of proposed machine learning training steps
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The machine learning model is designed to diagnose all possible medical conditions that may arise
depending on the type of data that is entered. The proposed form was also tested using several algorithms.
The model passes two stages when created, the first stage is the use of a well-known diagnostic data set and
predictions. The second stage is using real data, and the outputs are a diagnosis or expectation of these data
produced by the proposed form automatically. Table 6 shows the algorithms used with the accuracy and
efficiency of each of them. Five algorithms were tested to build the proposed system (naive Bayes (NB),
k-nearest neighbors (KNN), supportive vectors (SVM), random forest (RF), and simple logistical decade
(SL)).
Table 6. Performance of five algorithms
Criterion NB SVM RF SL KNN
Accuracy 91.54% 94.62% 92.88% 92.72% 89.89%
Sensitivity 88.50 95.10 95.00 94.22 86.32
F-score 87.30 96.22 94.20 95.01 87.75
4. CONCLUSION
It is clear from the results of the tests; Some important conclusions, as the proposed design using NI
provides a lower error rate than the microcontroller design. The statistical analysis of the results also showed
that the results of the proposed device using NI are close to reference readings, although there are 1.4 seconds
delay in showing the results. The design based on myRIO can be considered a reliable device by recording
readings in actual time and is very suitable for use in intensive care and can be used as a first aid tool in
accidents or distant places. The proposed machine learning model gives accuracy and effectiveness to use the
diagnosis of some diseases such as COVID-19 and some heart diseases and muscle neuropathy.
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BIOGRAPHIES OF AUTHORS
Yehia Zakaria Abd El Gawad is currently working toward a Ph.D. degree in the
Implementation and Analysis of Medical Systems. He received a B.Sc. degree from Alexandria
University, Egypt in 2009, and an M.Sc. degree in medical systems design from Arab Academy
for Science, Technology, and Maritime Transport, Alexandria, Egypt in 2012. He can be
contacted at email: yahia_elrefaay@hotmail.com.
Mohamed I. Youssef received a Ph.D. degree from Ruhr University, Bochum,
Germany, in 1988. He is currently a professor of Communications Systems with the Electrical
Engineering Department, Al-Azhar University, Cairo, Egypt. He has published several papers in
national and international conferences and journals. His current research areas are in digital
communication systems, wireless networks, digital signal processing, image processing, and
mathematical algorithms. He can be contacted at email: mohiyosof@gmail.com.
Tarek Mahmoud Nasser is a lecturer in the Faculty of Engineering, Al-Azhar
University. He received his Ph.D. degree from the University of Victoria, Canada in 2003. His
research interests include; multi-hop networking; routing, AODV protocol, scheduling
algorithms in networking, radio frequency identification RFID, image processing; enhancement,
restoration, and compression, and Pattern recognition using hidden Markov models. He can be
contacted at email: tarekmahmoudn1967@hotmail.com.