Pub Date : 2020-07-31DOI: 10.1108/ijpcc-09-2019-0066
Majid H. Alsulami, M. Alsaqer, A. Atkins
Purpose Technology plays an important role in assisting elderly people to live independently, longer and improve their quality of life and health, in supporting their daily activities, etc. The ageing population becomes a global phenomenon. The population of Saudi Arabia continues to age (>60 years of age) currently (5%) compared to other group ages. In 2050, it will increase rapidly to 20.9% of the Saudi population. The current research aims at examining the barriers that health-care providers in the Kingdom of Saudi Arabia are experiencing in the adoption of ambient assisted living (AAL) technologies among the elderly. The study aims to identify a challenging issue with the increasing the number of elderly among the population in the country, which has highlighted the need to use AAL technology to improve the quality of life among the elderly. Design/methodology/approach This study involved a community of practice (CoP) study as a method of data collection where data collected were presented and discussed in line with the existing literature review findings. Findings In total, 14 factors were identified in this study and discussed in the context of Saudi Arabia, which resulted in developing a decision-making framework for using AAL by health-care providers. Those factors are essential in boosting the usage of technology in improving elderly health in Saudi Arabia. Research limitations/implications This study includes implications for developing a decision-making framework for using AAL. Social implications This study clarifies that technology can connect elderly people with society. Originality/value In total, 14 factors were identified in this study and discussed in the context of Saudi Arabia.
{"title":"Decision-making framework for using ambient assisted living","authors":"Majid H. Alsulami, M. Alsaqer, A. Atkins","doi":"10.1108/ijpcc-09-2019-0066","DOIUrl":"https://doi.org/10.1108/ijpcc-09-2019-0066","url":null,"abstract":"\u0000Purpose\u0000Technology plays an important role in assisting elderly people to live independently, longer and improve their quality of life and health, in supporting their daily activities, etc. The ageing population becomes a global phenomenon. The population of Saudi Arabia continues to age (>60 years of age) currently (5%) compared to other group ages. In 2050, it will increase rapidly to 20.9% of the Saudi population. The current research aims at examining the barriers that health-care providers in the Kingdom of Saudi Arabia are experiencing in the adoption of ambient assisted living (AAL) technologies among the elderly. The study aims to identify a challenging issue with the increasing the number of elderly among the population in the country, which has highlighted the need to use AAL technology to improve the quality of life among the elderly.\u0000\u0000\u0000Design/methodology/approach\u0000This study involved a community of practice (CoP) study as a method of data collection where data collected were presented and discussed in line with the existing literature review findings.\u0000\u0000\u0000Findings\u0000In total, 14 factors were identified in this study and discussed in the context of Saudi Arabia, which resulted in developing a decision-making framework for using AAL by health-care providers. Those factors are essential in boosting the usage of technology in improving elderly health in Saudi Arabia.\u0000\u0000\u0000Research limitations/implications\u0000This study includes implications for developing a decision-making framework for using AAL.\u0000\u0000\u0000Social implications\u0000This study clarifies that technology can connect elderly people with society.\u0000\u0000\u0000Originality/value\u0000In total, 14 factors were identified in this study and discussed in the context of Saudi Arabia.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456504","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 : 2020-07-29DOI: 10.1108/ijpcc-07-2020-0077
Jagadeesan Dhanapal, Badrinath Narayanamurthy, Vijayakumar Shanmugam, A. Gangadharan, Magesh S.
PurposeThe purpose of this paper is to provide a model for prediction of respiratory symptoms in the progression of COVID-19, social distancing, frequent hand washes, wearing of face mask in public are some of the potential measures of preventing the disease from further spreading. In spite of the effects and efforts taken by governments, the pandemic is still uncontrolled in major cities of the world. The proposed technique in this paper introduces a non-intrusive and major screening of vital symptoms and changes in the respiratory organs.Design/methodology/approachThe novel coronavirus or Covid-19 has become a serious threat to social and economic growth of many nations worldwide. The pace of progression was significantly higher in the past two months. Identified by severe respiratory illness, fever and coughs, the disease has been threatening the lives of human society. Early detection and prognosis is absolutely necessary to isolate the potential spreaders of the disease and to control the rate of progression.FindingsRecent studies have highlighted the changes observed in breathing characteristics of infected patients. Respiratory pattern of Covid-19 patients can be differentiated from the respiratory pattern of normal cold/flu affected patients. Tachypnoea is one among the vital signs identified to be distinguishing feature of Covid-19. The proposed respiratory data capture will commence with facial recognition, use of infrared sensors and machine-learning approaches to classify the respiratory patterns, which finally narrows down as a symptom of Covid-19.Originality/valueProposed system produced outcome of 94% accuracy, precision, recall and a F1-measure as an average in the conducted experiments. This method also proves to be a fruitful solution for large-scale monitoring and categorisation of people based on the symptoms.
{"title":"Pervasive computational model and wearable devices for prediction of respiratory symptoms in progression of COVID-19","authors":"Jagadeesan Dhanapal, Badrinath Narayanamurthy, Vijayakumar Shanmugam, A. Gangadharan, Magesh S.","doi":"10.1108/ijpcc-07-2020-0077","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0077","url":null,"abstract":"PurposeThe purpose of this paper is to provide a model for prediction of respiratory symptoms in the progression of COVID-19, social distancing, frequent hand washes, wearing of face mask in public are some of the potential measures of preventing the disease from further spreading. In spite of the effects and efforts taken by governments, the pandemic is still uncontrolled in major cities of the world. The proposed technique in this paper introduces a non-intrusive and major screening of vital symptoms and changes in the respiratory organs.Design/methodology/approachThe novel coronavirus or Covid-19 has become a serious threat to social and economic growth of many nations worldwide. The pace of progression was significantly higher in the past two months. Identified by severe respiratory illness, fever and coughs, the disease has been threatening the lives of human society. Early detection and prognosis is absolutely necessary to isolate the potential spreaders of the disease and to control the rate of progression.FindingsRecent studies have highlighted the changes observed in breathing characteristics of infected patients. Respiratory pattern of Covid-19 patients can be differentiated from the respiratory pattern of normal cold/flu affected patients. Tachypnoea is one among the vital signs identified to be distinguishing feature of Covid-19. The proposed respiratory data capture will commence with facial recognition, use of infrared sensors and machine-learning approaches to classify the respiratory patterns, which finally narrows down as a symptom of Covid-19.Originality/valueProposed system produced outcome of 94% accuracy, precision, recall and a F1-measure as an average in the conducted experiments. This method also proves to be a fruitful solution for large-scale monitoring and categorisation of people based on the symptoms.","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115063554","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 : 2020-07-27DOI: 10.1108/ijpcc-06-2020-0069
Sreelakshmi C.C., S. Prathap
Purpose Shifting to mobile-based banking transactions from physical banking transactions can be considered as a social distancing mechanism, which helps to prevent the spread of Covid-19 virus. As the spread of Covid-19 is expected to continue for long, the continued usage of mobile-based payment services as a strategy to maintain social distancing has to prevail. Hence, this study aims to propose an integrated framework of mobile payments adoption and its continuance intention by integrating health belief model (HBM) and expectation confirmation model (ECM) of information system continuance. Design/methodology/approach The subject of the study constitutes new adopters of mobile payments. A total of 654 respondents participated in the survey. The conceptual model was empirically validated using structural equation modeling and serial mediation analysis. Findings The study found that the HBM constructs, namely, perceived severity, perceived susceptibility and self-efficacy significantly influenced adoption/confirmation of mobile-based payment services. The continuance intention was significantly predicted by perceived usefulness and perceived satisfaction. Furthermore, the perceived health threat (comprising perceived severity and perceived susceptibility) indirectly affects continuance intention through confirmation, perceived usefulness and satisfaction. Practical implications There are short-term and long-term implications for the study. Short-term implications include triggering the HBM at policy levels, to adopt mobile payments/banking as a means of social distancing in the wake of the increasing threat of Covid-19 in India. Long-term implication for service providers is to convert adopters into loyal consumers by enhancing usefulness and satisfaction. Originality/value The study proposes a novel attempt to explain the adoption and continuance of mobile-based payment as a preventive health behavior to contain the spread of Covid-19 outbreak. The study proposes an integrated framework of HBM and ECM to explain pre-adoption and post-adoption behavior of consumers with respect to mobile-based payment services during Covid-19 context.
{"title":"Continuance adoption of mobile-based payments in Covid-19 context: an integrated framework of health belief model and expectation confirmation model","authors":"Sreelakshmi C.C., S. Prathap","doi":"10.1108/ijpcc-06-2020-0069","DOIUrl":"https://doi.org/10.1108/ijpcc-06-2020-0069","url":null,"abstract":"\u0000Purpose\u0000Shifting to mobile-based banking transactions from physical banking transactions can be considered as a social distancing mechanism, which helps to prevent the spread of Covid-19 virus. As the spread of Covid-19 is expected to continue for long, the continued usage of mobile-based payment services as a strategy to maintain social distancing has to prevail. Hence, this study aims to propose an integrated framework of mobile payments adoption and its continuance intention by integrating health belief model (HBM) and expectation confirmation model (ECM) of information system continuance.\u0000\u0000\u0000Design/methodology/approach\u0000The subject of the study constitutes new adopters of mobile payments. A total of 654 respondents participated in the survey. The conceptual model was empirically validated using structural equation modeling and serial mediation analysis.\u0000\u0000\u0000Findings\u0000The study found that the HBM constructs, namely, perceived severity, perceived susceptibility and self-efficacy significantly influenced adoption/confirmation of mobile-based payment services. The continuance intention was significantly predicted by perceived usefulness and perceived satisfaction. Furthermore, the perceived health threat (comprising perceived severity and perceived susceptibility) indirectly affects continuance intention through confirmation, perceived usefulness and satisfaction.\u0000\u0000\u0000Practical implications\u0000There are short-term and long-term implications for the study. Short-term implications include triggering the HBM at policy levels, to adopt mobile payments/banking as a means of social distancing in the wake of the increasing threat of Covid-19 in India. Long-term implication for service providers is to convert adopters into loyal consumers by enhancing usefulness and satisfaction.\u0000\u0000\u0000Originality/value\u0000The study proposes a novel attempt to explain the adoption and continuance of mobile-based payment as a preventive health behavior to contain the spread of Covid-19 outbreak. The study proposes an integrated framework of HBM and ECM to explain pre-adoption and post-adoption behavior of consumers with respect to mobile-based payment services during Covid-19 context.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129058408","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 : 2020-07-27DOI: 10.1108/ijpcc-04-2020-0026
S. VamseeKrishna, Sudhakara P. Reddy, S. M. Reddy
Purpose A third-order discrete time sigma delta modulator (SDM) is proposed with optimum performance by addressing instability and power dissipations issues, and a novel SDM architecture is designed and verified in behavioural modelling in MATLAB/SIMULINK environment. Simulation results show that performance parameters of proposed modulator achieved SNR of 105.41 dB, SNDR of 101.96 dB and DR of 17 bits for the signal bandwidth of 20 kHz. Design/methodology/approach This paper describes single-loop SDM design with optimum selection of integrator weights for physiological signal processing in IoT applications. Findings The proposed discrete time modulator designed with 1-bit quantizer and optimum oversampling ratio proved as power efficient. Integrator scaling coefficients are generated in LabVIEW environment for pure third-order noise shaping. Originality/value This paper contains the novelty in the work, and it is suitable for cognitive Internet of Things applications.
{"title":"Single-loop sigma delta modulator design and verification for cognitive IoT applications","authors":"S. VamseeKrishna, Sudhakara P. Reddy, S. M. Reddy","doi":"10.1108/ijpcc-04-2020-0026","DOIUrl":"https://doi.org/10.1108/ijpcc-04-2020-0026","url":null,"abstract":"\u0000Purpose\u0000A third-order discrete time sigma delta modulator (SDM) is proposed with optimum performance by addressing instability and power dissipations issues, and a novel SDM architecture is designed and verified in behavioural modelling in MATLAB/SIMULINK environment. Simulation results show that performance parameters of proposed modulator achieved SNR of 105.41 dB, SNDR of 101.96 dB and DR of 17 bits for the signal bandwidth of 20 kHz.\u0000\u0000\u0000Design/methodology/approach\u0000This paper describes single-loop SDM design with optimum selection of integrator weights for physiological signal processing in IoT applications.\u0000\u0000\u0000Findings\u0000The proposed discrete time modulator designed with 1-bit quantizer and optimum oversampling ratio proved as power efficient. Integrator scaling coefficients are generated in LabVIEW environment for pure third-order noise shaping.\u0000\u0000\u0000Originality/value\u0000This paper contains the novelty in the work, and it is suitable for cognitive Internet of Things applications.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123693640","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 : 2020-07-24DOI: 10.1108/ijpcc-06-2020-0060
Mohammad Khalid Pandit, S. A. Banday
Purpose: Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID-19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays. Design/methodology/approach: The authors used a data set containing confirmed COVID-19 positive, common bacterial pneumonia and healthy cases (no infection). A collection of 1,428 x-ray images is used in this study. The authors used a pre-trained VGG-16 model for the classification task. Transfer learning with fine-tuning was used in this study to effectively train the network on a relatively small chest x-ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID-19 detection. Findings: The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x-ray images as possible diagnostic tool for faster COVID-19 detection to complement the already testing and diagnosis methods. Originality/value: The proposed method can be used as initial screening which can help health-care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.
{"title":"SARS n-CoV2-19 detection from chest x-ray images using deep neural networks","authors":"Mohammad Khalid Pandit, S. A. Banday","doi":"10.1108/ijpcc-06-2020-0060","DOIUrl":"https://doi.org/10.1108/ijpcc-06-2020-0060","url":null,"abstract":"Purpose: Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID-19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays. Design/methodology/approach: The authors used a data set containing confirmed COVID-19 positive, common bacterial pneumonia and healthy cases (no infection). A collection of 1,428 x-ray images is used in this study. The authors used a pre-trained VGG-16 model for the classification task. Transfer learning with fine-tuning was used in this study to effectively train the network on a relatively small chest x-ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID-19 detection. Findings: The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x-ray images as possible diagnostic tool for faster COVID-19 detection to complement the already testing and diagnosis methods. Originality/value: The proposed method can be used as initial screening which can help health-care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125391662","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 : 2020-07-23DOI: 10.1108/ijpcc-06-2020-0056
Vijay Anand R., Prabhu J., Kumar P.J., Manivannan S.S., Sukumar Rajendran, K.R. Kumar, Susi S., R. Jothikumar
This paper aims to address the role of Internet of Things (IoT) in preventing COVID-19. The IoT devices can be used in various ways to track the patients and suspected person. Remote data collection can be done with the help of IoT and sensors. Later, the data can be analyzed with the help of data science engineers and researchers to predict and prevent the COVID-19.,IoT is a creative mean of amalgamating clinical gadgets and their applications to associate with the human services and data innovation frameworks. An investigation on the conceivable outcomes of defying progressive COVID-19 pandemic by implementing the IoT approach while offering treatment to all classes of patient without any partiality in poor and rich. The information sharing, report checking, patient tracking, data social affair, investigation, cleanliness clinical consideration and so forth are the different cloud-based administrations of IoT. It can totally change the working format of the medical services while rewarding the huge volume of patients with a predominant degree of care and more fulfilment, particularly during this pandemic of COVID-19 lockdown. Health workers can quickly focus on patient zero and identify everyone who has come into contact with the infected person and move these people to quarantine/isolation. As COVID-19 has emerged from the Wuhan province of China, IoT tools such as geographic information system could be used as an effective tool to curb the spread of pandemics by acting as an early warning system. Scanners at airports across the world could be used to monitor temperature and other symptoms. This paper addresses the role of IoT in preventing COVID-19.,In the period of continuous pandemic of COVID-19, IoT offers many propelled cloud-based administrations and offices to serve a greater number of patients effectively. The remote medicinal services framework provides a lot of significance in such a crucial time of lockdown. The powerful interconnected arrangement of gadgets, applications, Web, database and so on encourages the consumers to benefit the administrations in smart way. IoT additionally advances its administrations by building up the quality culture of perceptive medicinal services or portable centre. It is a “distinct advantage innovation,” which may totally change the practices universally. Indeed, even its quality administrations in this extreme time make this methodology progressively productive and beneficial. IoT helps in observing and tracking more recognized people and patients in remote areas for their human service prerequisites. The customary medicinal services are probably going to observe a huge change in perspective sooner rather than later, as the computerized revolution would place cutting-edge innovation and its associated items in the possession of the patients and give both patients and doctors in remote areas better access to quality clinical services.,The contemporary exploration study focuses on the proposed IoT sys
{"title":"IoT role in prevention of COVID-19 and health care workforces behavioural intention in India - an empirical examination","authors":"Vijay Anand R., Prabhu J., Kumar P.J., Manivannan S.S., Sukumar Rajendran, K.R. Kumar, Susi S., R. Jothikumar","doi":"10.1108/ijpcc-06-2020-0056","DOIUrl":"https://doi.org/10.1108/ijpcc-06-2020-0056","url":null,"abstract":"This paper aims to address the role of Internet of Things (IoT) in preventing COVID-19. The IoT devices can be used in various ways to track the patients and suspected person. Remote data collection can be done with the help of IoT and sensors. Later, the data can be analyzed with the help of data science engineers and researchers to predict and prevent the COVID-19.,IoT is a creative mean of amalgamating clinical gadgets and their applications to associate with the human services and data innovation frameworks. An investigation on the conceivable outcomes of defying progressive COVID-19 pandemic by implementing the IoT approach while offering treatment to all classes of patient without any partiality in poor and rich. The information sharing, report checking, patient tracking, data social affair, investigation, cleanliness clinical consideration and so forth are the different cloud-based administrations of IoT. It can totally change the working format of the medical services while rewarding the huge volume of patients with a predominant degree of care and more fulfilment, particularly during this pandemic of COVID-19 lockdown. Health workers can quickly focus on patient zero and identify everyone who has come into contact with the infected person and move these people to quarantine/isolation. As COVID-19 has emerged from the Wuhan province of China, IoT tools such as geographic information system could be used as an effective tool to curb the spread of pandemics by acting as an early warning system. Scanners at airports across the world could be used to monitor temperature and other symptoms. This paper addresses the role of IoT in preventing COVID-19.,In the period of continuous pandemic of COVID-19, IoT offers many propelled cloud-based administrations and offices to serve a greater number of patients effectively. The remote medicinal services framework provides a lot of significance in such a crucial time of lockdown. The powerful interconnected arrangement of gadgets, applications, Web, database and so on encourages the consumers to benefit the administrations in smart way. IoT additionally advances its administrations by building up the quality culture of perceptive medicinal services or portable centre. It is a “distinct advantage innovation,” which may totally change the practices universally. Indeed, even its quality administrations in this extreme time make this methodology progressively productive and beneficial. IoT helps in observing and tracking more recognized people and patients in remote areas for their human service prerequisites. The customary medicinal services are probably going to observe a huge change in perspective sooner rather than later, as the computerized revolution would place cutting-edge innovation and its associated items in the possession of the patients and give both patients and doctors in remote areas better access to quality clinical services.,The contemporary exploration study focuses on the proposed IoT sys","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124997014","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 : 2020-07-22DOI: 10.1108/ijpcc-07-2020-0074
M. Thangamani, M. Ganthimathi, R. SridharS., M. Akila, R. Keerthana, S. RameshP.
Purpose The purpose of this paper is to identify coronavirus contact using internet of things The disease is said to be highly contagious with the contact of infected persons Feared to be air-borne, droplets of body fluids can transmit the disease in a matter of hours The predominant symptoms of the COVID-19 are high fever, cough, breathing problem, etc Recent studies have demonstrated the evolution of the disease to hide its symptoms As it is highly transmissible, this disease might spread at an exponential rate costing the lives of thousands of people The chain of transmission has to be detected with utmost priority through early detection and isolation of infected people Automated internet of things (IoT) devices can be used in design and implementation of a prediction scheme for reporting the health-care risks of the patients with various parameters such as temperature, humidity and blood pressure Design/methodology/approach IoT is a configuration of multiple autonomous and embedded wireless devices for serving a purpose Every object possesses an individual identity and will serve to register critical events as entries for future learning and decisions IoT plays an inevitable role in medical industries, detection of vital signs of diseases and monitoring Among other life-threatening diseases, a new pandemic is on rise among world nations COVID-19, a novel severe acute respiratory syndrome virus originated from animals in December 2019 and is becoming a serious menace to Governments, despite serious measures of lockdowns Findings In this paper, the authors defined an architecture of an IoT system to predict the Covid-19 disease by getting the data from the human through sensors and send the data to the doctor using mobile, computer, etc The main goal is early health surveillance by predicting COVID-19 Accordingly, the authors are able to identify both symptomatic and asymptomatic patients, which will help in the early prediction of disease Originality/value Using the proposed method, the authors can save the time of both patient and doctor by ensuring timely medical treatment and contribute toward breaking the transmission chain In so doing, the method also contributes toward avoiding unnecessary expenses and saving human lives
{"title":"Detecting coronavirus contact using internet of things","authors":"M. Thangamani, M. Ganthimathi, R. SridharS., M. Akila, R. Keerthana, S. RameshP.","doi":"10.1108/ijpcc-07-2020-0074","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0074","url":null,"abstract":"Purpose The purpose of this paper is to identify coronavirus contact using internet of things The disease is said to be highly contagious with the contact of infected persons Feared to be air-borne, droplets of body fluids can transmit the disease in a matter of hours The predominant symptoms of the COVID-19 are high fever, cough, breathing problem, etc Recent studies have demonstrated the evolution of the disease to hide its symptoms As it is highly transmissible, this disease might spread at an exponential rate costing the lives of thousands of people The chain of transmission has to be detected with utmost priority through early detection and isolation of infected people Automated internet of things (IoT) devices can be used in design and implementation of a prediction scheme for reporting the health-care risks of the patients with various parameters such as temperature, humidity and blood pressure Design/methodology/approach IoT is a configuration of multiple autonomous and embedded wireless devices for serving a purpose Every object possesses an individual identity and will serve to register critical events as entries for future learning and decisions IoT plays an inevitable role in medical industries, detection of vital signs of diseases and monitoring Among other life-threatening diseases, a new pandemic is on rise among world nations COVID-19, a novel severe acute respiratory syndrome virus originated from animals in December 2019 and is becoming a serious menace to Governments, despite serious measures of lockdowns Findings In this paper, the authors defined an architecture of an IoT system to predict the Covid-19 disease by getting the data from the human through sensors and send the data to the doctor using mobile, computer, etc The main goal is early health surveillance by predicting COVID-19 Accordingly, the authors are able to identify both symptomatic and asymptomatic patients, which will help in the early prediction of disease Originality/value Using the proposed method, the authors can save the time of both patient and doctor by ensuring timely medical treatment and contribute toward breaking the transmission chain In so doing, the method also contributes toward avoiding unnecessary expenses and saving human lives","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114507334","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 : 2020-07-22DOI: 10.1108/ijpcc-06-2020-0067
S. JosephineM., L. Lakshmanan, Resmi R. Nair, P. Visu, R. Ganesan, R. Jothikumar
Purpose The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable This field of interest has evolved into a wide area of investigation with today's advancement in technology of internet of things for immediate sensing and processing information about profound pain A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV) A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis Design/methodology/approach The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19 Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries Findings Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10 Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle Originality/value The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients
{"title":"Monitoring and sensing COVID-19 symptoms as a precaution using electronic wearable devices","authors":"S. JosephineM., L. Lakshmanan, Resmi R. Nair, P. Visu, R. Ganesan, R. Jothikumar","doi":"10.1108/ijpcc-06-2020-0067","DOIUrl":"https://doi.org/10.1108/ijpcc-06-2020-0067","url":null,"abstract":"Purpose The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable This field of interest has evolved into a wide area of investigation with today's advancement in technology of internet of things for immediate sensing and processing information about profound pain A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV) A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis Design/methodology/approach The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19 Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries Findings Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10 Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle Originality/value The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121247815","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 : 2020-07-15DOI: 10.1108/ijpcc-03-2020-0012
K. Priya, P. Rajendran
Purpose The authors consider parallel four-state tandem open queueing network. The queue capacity is infinite. Passenger arrival rate is Poisson distribution and service rate is exponential distribution. The queue is constructed in the form of tandem queue, and each and every queue of tandem queue is single server (M/M/1) queue. In tandem queue, passengers will leave the system once they receive service from both the states. The purpose of this paper is to provide performance analysis for four-state tandem open queue network, and a governing equation is formulated with the help of transition diagram. Using Burke theorem, the authors formulated equation for average number of passenger in the system, average waiting time of passenger in the system, average number of passenger in the queue and average waiting time of passenger in the queue. Design/methodology/approach This paper used Burke’s theorem. Findings In this paper, performance analysis is done for parallel four-state tandem open queueing network and performance measure solved using Burkes theorem formula. K. Sreekanth et al. has done performance analysis for single tandem queue with three states. In this paper, the authors have done performance analysis for two tandem queues parallel with four states. This four-state tandem open queueing network is suitable for real world applications. This paper can extend for more number of service states and multi-server states according to the application, and in such case, the authors have to prove and explain with numerical examples. This analysis is more useful for the applications such as airports, railway stations, bus-stands and banks. Originality/value In this paper, parallel four-state tandem open queueing network and performance measure has been solved using Burke’s theorem formula.
{"title":"Performance measures of parallel tandem open queueing network","authors":"K. Priya, P. Rajendran","doi":"10.1108/ijpcc-03-2020-0012","DOIUrl":"https://doi.org/10.1108/ijpcc-03-2020-0012","url":null,"abstract":"\u0000Purpose\u0000The authors consider parallel four-state tandem open queueing network. The queue capacity is infinite. Passenger arrival rate is Poisson distribution and service rate is exponential distribution. The queue is constructed in the form of tandem queue, and each and every queue of tandem queue is single server (M/M/1) queue. In tandem queue, passengers will leave the system once they receive service from both the states. The purpose of this paper is to provide performance analysis for four-state tandem open queue network, and a governing equation is formulated with the help of transition diagram. Using Burke theorem, the authors formulated equation for average number of passenger in the system, average waiting time of passenger in the system, average number of passenger in the queue and average waiting time of passenger in the queue.\u0000\u0000\u0000Design/methodology/approach\u0000This paper used Burke’s theorem.\u0000\u0000\u0000Findings\u0000In this paper, performance analysis is done for parallel four-state tandem open queueing network and performance measure solved using Burkes theorem formula. K. Sreekanth et al. has done performance analysis for single tandem queue with three states. In this paper, the authors have done performance analysis for two tandem queues parallel with four states. This four-state tandem open queueing network is suitable for real world applications. This paper can extend for more number of service states and multi-server states according to the application, and in such case, the authors have to prove and explain with numerical examples. This analysis is more useful for the applications such as airports, railway stations, bus-stands and banks.\u0000\u0000\u0000Originality/value\u0000In this paper, parallel four-state tandem open queueing network and performance measure has been solved using Burke’s theorem formula.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130068344","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 : 2020-07-13DOI: 10.1108/ijpcc-06-2020-0054
K. Priya, P. Rajendran, S. M, Prabhu J., Sukumar Rajendran, P. Kumar, T. P, Jabez Christopher, Jothikumar R.
Purpose The computational model proposed in this work uses the data's of COVID-19 cases in India. From the analysis, it can be observed that the proposed immunity model decides the recovery rate of COVID −19 patients; moreover, the recovery rate does not depend on the age of the patients. These analytic models can be used by public health professionals, hospital administrators and epidemiologists for strategic decision-making to enhance health requirements based on various demographic and social factors of those affected by the pandemic. Mobile-based computational model can be used to compute the travel history of the affected people by accessing the near geographical maps of the path traveled. Design/methodology/approach In this paper, the authors developed a pediatric and geriatric person’s immunity network-based mobile computational model for COVID-19 patients. As the computational model is hard to analyze mathematically, the authors simplified the computational model as general COVID-19 infected people, the computational immunity model. The model proposed in this work used the data's of COVID-19 cases in India. Findings This study proposes a pediatric and geriatric people immunity network model for COVID- 19 patients. For the analysis part, the data's on COVID-19 cases in India was used. In this model, the authors have taken two sets of people (pediatric and geriatric), both are facing common symptoms such as fever, cough and myalgia. From the analysis, it was observed and also proved that the immunity level of patients decides the recovery rate of COVID-19 patients and the age of COVID-19 patients has no significant influence on the recovery rate of the patient. Originality/value COVID-19 has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain the spread of the virus also require social distancing. The novel model in this work focus on the Indian scenario and thereby may help Indian health organizations for future planning and organization. The factors model in this work such as age, immunity level, recovery rate can be used by machine leaning models for predicting other useful outcomes.
{"title":"Pediatric and geriatric immunity network mobile computational model for COVID-19","authors":"K. Priya, P. Rajendran, S. M, Prabhu J., Sukumar Rajendran, P. Kumar, T. P, Jabez Christopher, Jothikumar R.","doi":"10.1108/ijpcc-06-2020-0054","DOIUrl":"https://doi.org/10.1108/ijpcc-06-2020-0054","url":null,"abstract":"\u0000Purpose\u0000The computational model proposed in this work uses the data's of COVID-19 cases in India. From the analysis, it can be observed that the proposed immunity model decides the recovery rate of COVID −19 patients; moreover, the recovery rate does not depend on the age of the patients. These analytic models can be used by public health professionals, hospital administrators and epidemiologists for strategic decision-making to enhance health requirements based on various demographic and social factors of those affected by the pandemic. Mobile-based computational model can be used to compute the travel history of the affected people by accessing the near geographical maps of the path traveled.\u0000\u0000\u0000Design/methodology/approach\u0000In this paper, the authors developed a pediatric and geriatric person’s immunity network-based mobile computational model for COVID-19 patients. As the computational model is hard to analyze mathematically, the authors simplified the computational model as general COVID-19 infected people, the computational immunity model. The model proposed in this work used the data's of COVID-19 cases in India.\u0000\u0000\u0000Findings\u0000This study proposes a pediatric and geriatric people immunity network model for COVID- 19 patients. For the analysis part, the data's on COVID-19 cases in India was used. In this model, the authors have taken two sets of people (pediatric and geriatric), both are facing common symptoms such as fever, cough and myalgia. From the analysis, it was observed and also proved that the immunity level of patients decides the recovery rate of COVID-19 patients and the age of COVID-19 patients has no significant influence on the recovery rate of the patient.\u0000\u0000\u0000Originality/value\u0000COVID-19 has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain the spread of the virus also require social distancing. The novel model in this work focus on the Indian scenario and thereby may help Indian health organizations for future planning and organization. The factors model in this work such as age, immunity level, recovery rate can be used by machine leaning models for predicting other useful outcomes.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129324067","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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