Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182128
Debasish Das, M. Singh, Sarthak Mohanty, S. Chakravarty
Agriculture is the most important sector in Indian economy. India occupies the second highest rank in farm outputs in the world. Its contribution to the development of Indian economy has immense potential. So agriculture products may play vital role for economic growth. But the different kind of diseases in plant decreases the production of crops and growth rate of farmers. To identify and monitor the leaf diseases manually by farmers is very difficult. This is one of the reasons to develop an automatic leaf diseases detection model. The proposed model helps in automatic detection of different plant diseases at early stages. Thus, the production will increase in many folds. The main aim of this study is to identify different types of leaf diseases. Different feature extraction techniques have been used to enhance the classification accuracy. Support Vector Machine (SVM), Random Forest and Logistic Regression have been applied to classify different types of leaf diseases. When obtained results are compared SVM outperforms other two classifiers. Results show that, the model can be used in real life applications.
{"title":"Leaf Disease Detection using Support Vector Machine","authors":"Debasish Das, M. Singh, Sarthak Mohanty, S. Chakravarty","doi":"10.1109/ICCSP48568.2020.9182128","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182128","url":null,"abstract":"Agriculture is the most important sector in Indian economy. India occupies the second highest rank in farm outputs in the world. Its contribution to the development of Indian economy has immense potential. So agriculture products may play vital role for economic growth. But the different kind of diseases in plant decreases the production of crops and growth rate of farmers. To identify and monitor the leaf diseases manually by farmers is very difficult. This is one of the reasons to develop an automatic leaf diseases detection model. The proposed model helps in automatic detection of different plant diseases at early stages. Thus, the production will increase in many folds. The main aim of this study is to identify different types of leaf diseases. Different feature extraction techniques have been used to enhance the classification accuracy. Support Vector Machine (SVM), Random Forest and Logistic Regression have been applied to classify different types of leaf diseases. When obtained results are compared SVM outperforms other two classifiers. Results show that, the model can be used in real life applications.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129413332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper focuses on the idea of providing surveillance using a robot with the techniques of IOT. Surveillance is a major issue in public restricted areas. The robot is hired here to monitor throughout the day. This robotic vehicle has ability to substitute the human in hazardous area to provide surveillance. The robot is operated manually by connecting it to Wi-Fi and consists of sensors for identifying any obstacles and identifying humans and give live streaming to respective admin. This is operated over Wi-fi using blynk app software. Arduino IDE is used in programming the robot. ICs like L293D (motor driver) and sensors like PIR, ultrasonic helps in movement of the mechanical body and detection of obstacles respectively. A camera is equipped for capturing the image of the person identified. A face recognition algorithm can help in spotting the intruder. The gas sensor provided can sense the presence of toxic chemicals in its surroundings. Thus the robot continuously provides data in remote location in addition to the advantages of reduced human loss and detection of threats.
{"title":"Development of Surveillance Robot to Monitor the Work Performance in Hazardous Area","authors":"Sushma Sirasanagandla, Mounisha Pachipulusu, Ramesh Jayaraman","doi":"10.1109/ICCSP48568.2020.9182126","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182126","url":null,"abstract":"The paper focuses on the idea of providing surveillance using a robot with the techniques of IOT. Surveillance is a major issue in public restricted areas. The robot is hired here to monitor throughout the day. This robotic vehicle has ability to substitute the human in hazardous area to provide surveillance. The robot is operated manually by connecting it to Wi-Fi and consists of sensors for identifying any obstacles and identifying humans and give live streaming to respective admin. This is operated over Wi-fi using blynk app software. Arduino IDE is used in programming the robot. ICs like L293D (motor driver) and sensors like PIR, ultrasonic helps in movement of the mechanical body and detection of obstacles respectively. A camera is equipped for capturing the image of the person identified. A face recognition algorithm can help in spotting the intruder. The gas sensor provided can sense the presence of toxic chemicals in its surroundings. Thus the robot continuously provides data in remote location in addition to the advantages of reduced human loss and detection of threats.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129932488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182452
Sunil Singh, Neha Yadav, Pawan Kumar Chuarasia
Recent advancement of cyber physical systems open doors to various safety measures, threats, attacks and vulnerabilities are such major key challenges now days. The globally adoption of cyber physical systems basically forms a basis for cyber social attack in order to breakdown secure channel and control actions. Hence loopholes and vulnerabilities in trending cyber physical systems are targeted to make systems unstable and unsafe state. The subjection of CPSs causes new critical issues for research and academics. However expeditious growth of CPS devices a question marks on security, integrity and confidentiality. The paradigm which forms basis for CPS are Smart phones, Defense System, Meteorology, Big data, Smart Technologies and Smart Vehicles. The purpose and analysis behind this paper to find out security issues and challenges of CPSs. Comparison of various cyber physical attacks and analysis on several parameters has been done. Key noted issues are results of cyber attacks, CPS attack traceability and the review on communication security architecture.
{"title":"A Review on Cyber Physical System Attacks: Issues and Challenges","authors":"Sunil Singh, Neha Yadav, Pawan Kumar Chuarasia","doi":"10.1109/ICCSP48568.2020.9182452","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182452","url":null,"abstract":"Recent advancement of cyber physical systems open doors to various safety measures, threats, attacks and vulnerabilities are such major key challenges now days. The globally adoption of cyber physical systems basically forms a basis for cyber social attack in order to breakdown secure channel and control actions. Hence loopholes and vulnerabilities in trending cyber physical systems are targeted to make systems unstable and unsafe state. The subjection of CPSs causes new critical issues for research and academics. However expeditious growth of CPS devices a question marks on security, integrity and confidentiality. The paradigm which forms basis for CPS are Smart phones, Defense System, Meteorology, Big data, Smart Technologies and Smart Vehicles. The purpose and analysis behind this paper to find out security issues and challenges of CPSs. Comparison of various cyber physical attacks and analysis on several parameters has been done. Key noted issues are results of cyber attacks, CPS attack traceability and the review on communication security architecture.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129924957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182149
Binish Fatimah, A. Javali, Haaris Ansar, B. Harshitha, Hemant Kumar
Solving an arithmetic problem is a complex task which involves fact retrieval, memory, sequencing and decision making. Automatic detection of such an activity from EEG signals will help in understanding of brain response to these cognitive tasks. In this work, we propose a mental arithmetic task detection algorithm from a single lead EEG signal. Fourier Decomposition method is used to decompose the signal into M uniform sub-bands and features, like energy, entropy, and variance, are computed from each of these sub-bands. Kruskal-Wallis method has been used to select only the statistically relevant features. These selected features are, then, used to classify the given EEG dataset into two classes using support vector machine with cubic kernel. To validate the efficacy of the proposed algorithm, simulation results are presented using dataset available on MIT PhysioNet, titled EEG during mental arithmetic task.
{"title":"Mental Arithmetic Task Classification using Fourier Decomposition Method","authors":"Binish Fatimah, A. Javali, Haaris Ansar, B. Harshitha, Hemant Kumar","doi":"10.1109/ICCSP48568.2020.9182149","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182149","url":null,"abstract":"Solving an arithmetic problem is a complex task which involves fact retrieval, memory, sequencing and decision making. Automatic detection of such an activity from EEG signals will help in understanding of brain response to these cognitive tasks. In this work, we propose a mental arithmetic task detection algorithm from a single lead EEG signal. Fourier Decomposition method is used to decompose the signal into M uniform sub-bands and features, like energy, entropy, and variance, are computed from each of these sub-bands. Kruskal-Wallis method has been used to select only the statistically relevant features. These selected features are, then, used to classify the given EEG dataset into two classes using support vector machine with cubic kernel. To validate the efficacy of the proposed algorithm, simulation results are presented using dataset available on MIT PhysioNet, titled EEG during mental arithmetic task.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124814196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182045
Athira Devaraj, Aswathy K. Nair
This research work focuses on identifying a specific hand gesture from the given EMG signal, acquired by sensor-based band. Surface EMG and machine learning techniques are used for the identification and classification purpose. The raw EMG signal captured using the sensor is initially passed through suitable preprocessing steps to avoid the noise artifacts. Followed by this, 8 different time-domain features are collected from these raw EMG signals, using which a feature matrix is created. SVM and KNN are the machine learning classifiers used here. The entire system is implemented in MATLAB 2019a. Using these methods, a promising accuracy of 93% is obtained through KNN and an accuracy of 83% using SVM.
{"title":"Hand Gesture Signal Classification using Machine Learning","authors":"Athira Devaraj, Aswathy K. Nair","doi":"10.1109/ICCSP48568.2020.9182045","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182045","url":null,"abstract":"This research work focuses on identifying a specific hand gesture from the given EMG signal, acquired by sensor-based band. Surface EMG and machine learning techniques are used for the identification and classification purpose. The raw EMG signal captured using the sensor is initially passed through suitable preprocessing steps to avoid the noise artifacts. Followed by this, 8 different time-domain features are collected from these raw EMG signals, using which a feature matrix is created. SVM and KNN are the machine learning classifiers used here. The entire system is implemented in MATLAB 2019a. Using these methods, a promising accuracy of 93% is obtained through KNN and an accuracy of 83% using SVM.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124968572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182116
K. Rajini, K. Vasanth
A Novel Area efficient and low power multiplexer based Data comparator for median filter in De-noising application is proposed. The proposed method uses multiplexer based implementation of borrow equation in a full subtractor which acts as a basic processing element of a Data Comparator. The proposed work was implemented in Microwind for three different Models of Mosfet and different technologies. The modifications in the existing borrow equation of a full subtractor using multiplexer only resulted in reduced number of transistors with reduced power. For an 8 bit image de-noising approach using median filter, which consists of a 8 bit data comparator will require only 116 transistors and dissipates 52.25uw of power for 90nm technology.
{"title":"Area Efficient and Low Power Multiplexer based Data Comparator for Median filter in Denoising Application","authors":"K. Rajini, K. Vasanth","doi":"10.1109/ICCSP48568.2020.9182116","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182116","url":null,"abstract":"A Novel Area efficient and low power multiplexer based Data comparator for median filter in De-noising application is proposed. The proposed method uses multiplexer based implementation of borrow equation in a full subtractor which acts as a basic processing element of a Data Comparator. The proposed work was implemented in Microwind for three different Models of Mosfet and different technologies. The modifications in the existing borrow equation of a full subtractor using multiplexer only resulted in reduced number of transistors with reduced power. For an 8 bit image de-noising approach using median filter, which consists of a 8 bit data comparator will require only 116 transistors and dissipates 52.25uw of power for 90nm technology.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128531791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182093
B. Thamaraichelvi
In this proposed method, Magnetic Resonance (MR) Brain image segmentation technique based on Pulse Coupled Neural Network (PCNN) clustering combined with Particle Swarm optimization (PSO) approach has been presented. Since, PCNN is robust to noise, the input image is added with 0.05 Level of impulsive noise and the segmented output was analysed based on the fractions, selectivity and sensitivity. Accuracy of the proposed technique was found to be 93%. Moreover, in this proposed method, instead of selecting the parameters of PCNN in a random manner, they are optimized using PSO technique.
{"title":"PSO optimized Pulse Coupled Neural Network for Segmenting MR Brain Image","authors":"B. Thamaraichelvi","doi":"10.1109/ICCSP48568.2020.9182093","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182093","url":null,"abstract":"In this proposed method, Magnetic Resonance (MR) Brain image segmentation technique based on Pulse Coupled Neural Network (PCNN) clustering combined with Particle Swarm optimization (PSO) approach has been presented. Since, PCNN is robust to noise, the input image is added with 0.05 Level of impulsive noise and the segmented output was analysed based on the fractions, selectivity and sensitivity. Accuracy of the proposed technique was found to be 93%. Moreover, in this proposed method, instead of selecting the parameters of PCNN in a random manner, they are optimized using PSO technique.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"159 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182451
S. Bhimavarapu, P. Vinitha
Infrastructural defects to determine the sicknesses of the crop utilized within the agricultural quarter improvising special standards and solutions. The diagnosis of the different scenario and cause for diseases had been let to indulge in the current mobile technology suitable for the controlling of the disease using wireless scenario or switches. Our paper imparts on the current existing design technique as SVM, providing the mathematical and functional aspects of the design ensuring to improve the locating diseases using test and train scenarios. The setup for the SVM model is also taken in account for considerations of the different data sets of the images related different crops noting to provide the correct information of the problem scenario. These problems might exist due to natural or man-made for each set of the disease observed and identified. Hence recognition of the diseases would suffice the design criteria ensuring different parametric criteria for each level of training and test set provided. To ensure the novel and more accurate scenario different set of data set have been in consideration for different test and train images providing higher and reliable accuracy for the proposed model as part of CNN applying as Transfer learning. Different scenarios of the plant disease image have been considered as data set of 15617 images under restricted cases improvising a train model on CNN with transfer learning. The accuracy observed from the design model is observed 98.56% on the considered test vectors providing required feasibility. These designs also provide a better and convenient solutions for the people utilizing the current technologies.
{"title":"Analysis and Characterization of Plant Diseases using Transfer Learning","authors":"S. Bhimavarapu, P. Vinitha","doi":"10.1109/ICCSP48568.2020.9182451","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182451","url":null,"abstract":"Infrastructural defects to determine the sicknesses of the crop utilized within the agricultural quarter improvising special standards and solutions. The diagnosis of the different scenario and cause for diseases had been let to indulge in the current mobile technology suitable for the controlling of the disease using wireless scenario or switches. Our paper imparts on the current existing design technique as SVM, providing the mathematical and functional aspects of the design ensuring to improve the locating diseases using test and train scenarios. The setup for the SVM model is also taken in account for considerations of the different data sets of the images related different crops noting to provide the correct information of the problem scenario. These problems might exist due to natural or man-made for each set of the disease observed and identified. Hence recognition of the diseases would suffice the design criteria ensuring different parametric criteria for each level of training and test set provided. To ensure the novel and more accurate scenario different set of data set have been in consideration for different test and train images providing higher and reliable accuracy for the proposed model as part of CNN applying as Transfer learning. Different scenarios of the plant disease image have been considered as data set of 15617 images under restricted cases improvising a train model on CNN with transfer learning. The accuracy observed from the design model is observed 98.56% on the considered test vectors providing required feasibility. These designs also provide a better and convenient solutions for the people utilizing the current technologies.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125569223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182336
C. Sidharth, S. Hiremath, S. K. Patra
Millimeter Wave (mmWave) and massive MIMO (Multiple Input Multiple Output) are promising solutions for 5G communications. Hybrid precoding architecture (analog and digital) is generally employed to resolve high hardware complexity and energy consumption issues. The current hybrid precoding architectures are computationally complex. This proposes a novel deep neural network based precoding architecture named ‘ComcepNet’. The network combines the features of Complex Convolution blocks and Inception Network. The network is observed to deliver superior performance in terms of accuracy and achievable datarate compared to the present Autoprecoder network.
{"title":"Deep Learning based Hybrid Precoding for mmWave Massive MIMO system using ComcepNet","authors":"C. Sidharth, S. Hiremath, S. K. Patra","doi":"10.1109/ICCSP48568.2020.9182336","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182336","url":null,"abstract":"Millimeter Wave (mmWave) and massive MIMO (Multiple Input Multiple Output) are promising solutions for 5G communications. Hybrid precoding architecture (analog and digital) is generally employed to resolve high hardware complexity and energy consumption issues. The current hybrid precoding architectures are computationally complex. This proposes a novel deep neural network based precoding architecture named ‘ComcepNet’. The network combines the features of Complex Convolution blocks and Inception Network. The network is observed to deliver superior performance in terms of accuracy and achievable datarate compared to the present Autoprecoder network.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126377142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biological signals from the human body play a significance role in monitoring health condition of person. Among these signals which are derived from heart are coined as Electrocardiogram (ECG). The ECG signals allow cardiologist physician to know about the condition of the heart such as stroke and arrhythmia. But the problem in existing ECG unit in hospital care unit have three to twelve electrodes system with the wet Ag/AgCl electrode which needs well trained person. The research objective is to develop and design self-monitoring ECG system with dual electrode from the finger site for people who are suffering from and have a history of a cardio abnormality at home or workplace. Since all biological signals have noise and low frequency so the acquired signal is passed through designed filter and amplifiers. Further acquired signal are display and analyzed interfacing with NI myDAQ and biomedical workbench. 20 subjects of age under 30year ECG signal are acquired using developed prototype and heart rate is calculated. The ECG signals from developed prototype are compared with conventional ECG unit and almost similar results are obtained. Hence, the developed prototype can be used for monitor cardiovascular disease status for people suffering from arrhythmia as well as the athletes and soldiers can benefit to keep track of their heart condition. The developed ECG system is economical and safe to use.
{"title":"Dual Electrodes System for acquisition of ECG Waveform","authors":"Pema Wangmo, Mannat Uppal, Oinam Robita Chanu, Amritaa Easwaran, Goli Ramyavani","doi":"10.1109/ICCSP48568.2020.9182278","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182278","url":null,"abstract":"Biological signals from the human body play a significance role in monitoring health condition of person. Among these signals which are derived from heart are coined as Electrocardiogram (ECG). The ECG signals allow cardiologist physician to know about the condition of the heart such as stroke and arrhythmia. But the problem in existing ECG unit in hospital care unit have three to twelve electrodes system with the wet Ag/AgCl electrode which needs well trained person. The research objective is to develop and design self-monitoring ECG system with dual electrode from the finger site for people who are suffering from and have a history of a cardio abnormality at home or workplace. Since all biological signals have noise and low frequency so the acquired signal is passed through designed filter and amplifiers. Further acquired signal are display and analyzed interfacing with NI myDAQ and biomedical workbench. 20 subjects of age under 30year ECG signal are acquired using developed prototype and heart rate is calculated. The ECG signals from developed prototype are compared with conventional ECG unit and almost similar results are obtained. Hence, the developed prototype can be used for monitor cardiovascular disease status for people suffering from arrhythmia as well as the athletes and soldiers can benefit to keep track of their heart condition. The developed ECG system is economical and safe to use.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"72 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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