Pub Date : 2014-10-01DOI: 10.1109/HealthCom.2014.7001887
C. El-Gemayel, F. Jumel, N. Abouchi, J. Constantin, Doumit Zaouk
A mathematical representation of patient or device is presented by a number of variables which are defined to represent the inputs, outputs and a set of equations describing the interaction of these variables. This paper proposes a global methodology for modeling and simulation medical systems and human body, in order to analyze the performance and the quality of services of all system components. Beginning by defining a new prototype of a global and flexible architecture of mathematical model of human body, that is able to contain required data. Then, describing the simulations representation, by mentions in details the core simulator components, analyzer, and the quality of services indicators. Simulation of mathematical models provides useful tools for diagnosis and analyzing the interactions between efficacy, therapies, side-effects, and outcomes. This will help to better understand the human organism control, to analyze experimental data, to identify and quantify relevant biophysical parameters and to design clinical trials.
{"title":"A global methodology for modeling and simulating medical systems","authors":"C. El-Gemayel, F. Jumel, N. Abouchi, J. Constantin, Doumit Zaouk","doi":"10.1109/HealthCom.2014.7001887","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001887","url":null,"abstract":"A mathematical representation of patient or device is presented by a number of variables which are defined to represent the inputs, outputs and a set of equations describing the interaction of these variables. This paper proposes a global methodology for modeling and simulation medical systems and human body, in order to analyze the performance and the quality of services of all system components. Beginning by defining a new prototype of a global and flexible architecture of mathematical model of human body, that is able to contain required data. Then, describing the simulations representation, by mentions in details the core simulator components, analyzer, and the quality of services indicators. Simulation of mathematical models provides useful tools for diagnosis and analyzing the interactions between efficacy, therapies, side-effects, and outcomes. This will help to better understand the human organism control, to analyze experimental data, to identify and quantify relevant biophysical parameters and to design clinical trials.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124868226","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001885
Lei Song, Yongcai Wang, Jijiang Yang, Jianqiang Li
With the global trend of population aging in industrialized countries, efficient information and communication technologies (ICT) for aiding elders or patients' healthcare have attracted great research attentions. Among these technologies, health state sensing by wearable sensors and mobile phones is an important foundation. It monitors the real-time body states; stores, or sends the result to remote family members or doctors. In this way, it can either help people to pay more attention to the overlooked phenomenon, such as the clue of dangerous disease, or help people to issue panic alert when emergency happens. There are many critical issues in health sensing. First, the sensors must be non-intrusive to people's comfort and safety, while providing good accuracy. At the same time, because of being worn by people, numerous noises posed by body motions must be efficiently processed for reducing false alarming. At last, different health or decease signals generally require different sensing technologies and instrument. To tease out the technology advantages that address these challenges and diversities, this paper presented a survey on the state of the art of health sensing technologies using body sensor networks and mobile phones. It classify related works by their application goals, including i) fall detection, ii) gait analyzing, iii) activity qualification, iv) heart state sensing, and v) sleep sensing. It also conducts summary and comparison of related sensing systems and algorithms, to reveal the development lines in each subarea.
{"title":"Health sensing by wearable sensors and mobile phones: A survey","authors":"Lei Song, Yongcai Wang, Jijiang Yang, Jianqiang Li","doi":"10.1109/HealthCom.2014.7001885","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001885","url":null,"abstract":"With the global trend of population aging in industrialized countries, efficient information and communication technologies (ICT) for aiding elders or patients' healthcare have attracted great research attentions. Among these technologies, health state sensing by wearable sensors and mobile phones is an important foundation. It monitors the real-time body states; stores, or sends the result to remote family members or doctors. In this way, it can either help people to pay more attention to the overlooked phenomenon, such as the clue of dangerous disease, or help people to issue panic alert when emergency happens. There are many critical issues in health sensing. First, the sensors must be non-intrusive to people's comfort and safety, while providing good accuracy. At the same time, because of being worn by people, numerous noises posed by body motions must be efficiently processed for reducing false alarming. At last, different health or decease signals generally require different sensing technologies and instrument. To tease out the technology advantages that address these challenges and diversities, this paper presented a survey on the state of the art of health sensing technologies using body sensor networks and mobile phones. It classify related works by their application goals, including i) fall detection, ii) gait analyzing, iii) activity qualification, iv) heart state sensing, and v) sleep sensing. It also conducts summary and comparison of related sensing systems and algorithms, to reveal the development lines in each subarea.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122136591","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001843
A. Michalas, Nicolae Paladi, C. Gehrmann
As adoption of e-health solutions advances, new computing paradigms - such as cloud computing - bring the potential to improve efficiency in managing medical health records and help reduce costs. However, these opportunities introduce new security risks which can not be ignored. Based on our experience with deploying part of the Swedish electronic health records management system in an infrastructure cloud, we make an overview of major requirements that must be considered when migrating e-health systems to the cloud. Furthermore, we describe in-depth a new attack vector inherent to cloud deployments and present a novel data confidentiality and integrity protection mechanism for infrastructure clouds. This contribution aims to encourage exchange of best practices and lessons learned in migrating public e-health systems to the cloud.
{"title":"Security aspects of e-Health systems migration to the cloud","authors":"A. Michalas, Nicolae Paladi, C. Gehrmann","doi":"10.1109/HealthCom.2014.7001843","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001843","url":null,"abstract":"As adoption of e-health solutions advances, new computing paradigms - such as cloud computing - bring the potential to improve efficiency in managing medical health records and help reduce costs. However, these opportunities introduce new security risks which can not be ignored. Based on our experience with deploying part of the Swedish electronic health records management system in an infrastructure cloud, we make an overview of major requirements that must be considered when migrating e-health systems to the cloud. Furthermore, we describe in-depth a new attack vector inherent to cloud deployments and present a novel data confidentiality and integrity protection mechanism for infrastructure clouds. This contribution aims to encourage exchange of best practices and lessons learned in migrating public e-health systems to the cloud.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129364713","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001904
Germanno Teles, C. Oliveira, R. Braga, L. O. M. Andrade, Ronaldo F. Ramos, Paulo Cunha, Mauro Oliveira
This paper proposes the use of Bayesian networks to support the decision-making process in public health systems. In particular, this paper presents LARIISA_Bay, a new component based on Bayesian networks that works together with LARIISA, a context-aware platform to support applications in public health systems. The main goal of the proposed component is to assist teams of health specialists in order to better diagnose diseases through data collected from users of LARIISA. As a case study, we focus on scenarios of dengue fever disease. We classify dengue cases into one of the following levels: normal, grave or emergency. Based on this classification, teams of health specialists can accurately make decisions, for example, to alert a health care agent to visit locations with a high incidence of the disease, to send a team of health specialists when a dengue emergency case has occurred, as well as give technical instructions on how to deal with specific cases. We present a prototype of LARIISA_Bay as well as the corresponding interfaces to support the interactions with the component. We compare the obtained results with real diagnosis of general practitioners. The results presented show the efficiency of the proposed approach.
{"title":"Using Bayesian networks to improve the decision-making process in public health systems","authors":"Germanno Teles, C. Oliveira, R. Braga, L. O. M. Andrade, Ronaldo F. Ramos, Paulo Cunha, Mauro Oliveira","doi":"10.1109/HealthCom.2014.7001904","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001904","url":null,"abstract":"This paper proposes the use of Bayesian networks to support the decision-making process in public health systems. In particular, this paper presents LARIISA_Bay, a new component based on Bayesian networks that works together with LARIISA, a context-aware platform to support applications in public health systems. The main goal of the proposed component is to assist teams of health specialists in order to better diagnose diseases through data collected from users of LARIISA. As a case study, we focus on scenarios of dengue fever disease. We classify dengue cases into one of the following levels: normal, grave or emergency. Based on this classification, teams of health specialists can accurately make decisions, for example, to alert a health care agent to visit locations with a high incidence of the disease, to send a team of health specialists when a dengue emergency case has occurred, as well as give technical instructions on how to deal with specific cases. We present a prototype of LARIISA_Bay as well as the corresponding interfaces to support the interactions with the component. We compare the obtained results with real diagnosis of general practitioners. The results presented show the efficiency of the proposed approach.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127130873","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001851
Konda Divya Krishna, V. Akkala, R. Bharath, P. Rajalakshmi, M. Mateen
Ultrasound imaging has been widely used for preliminary diagnosis as it is non-invasive and has good scope for the doctors to analyze many diseases. Lack of trained sonographers make ultrasound imaging diagnosis time consuming to detect any abnormality. Sometimes the problem cannot exactly be identified which may lead to error in diagnosis. Hence in this paper we present computer aided automatic detection of abnormality in kidney on the ultrasound system itself, to decrease the time for reports and not to depend on the sonographer. We classified the kidney as normal and abnormal case. Segment the kidney region and extract Intensity histogram features and Haralick features from Gray Level Cooccurnace Matrix (GLCM). These features are calculated for a set of large data containing both normal and abnormal cases. Abnormal case includes kidney stone, cyst and bacterial infection. Standard deviation for each parameter is observed, considered only those features with less deviation and implemented on FPGA Kintex board. If the range of mean value is 1.08 to 1.336, skewness is 2.882 to 7.708, Kurtosis is 1.06 to 71.152, Cluster Shade is 72 to 243, Homogeneity is 0.993 to 0.998, the observed kidney image is normal otherwise abnormal.
{"title":"FPGA based preliminary CAD for kidney on IoT enabled portable ultrasound imaging system","authors":"Konda Divya Krishna, V. Akkala, R. Bharath, P. Rajalakshmi, M. Mateen","doi":"10.1109/HealthCom.2014.7001851","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001851","url":null,"abstract":"Ultrasound imaging has been widely used for preliminary diagnosis as it is non-invasive and has good scope for the doctors to analyze many diseases. Lack of trained sonographers make ultrasound imaging diagnosis time consuming to detect any abnormality. Sometimes the problem cannot exactly be identified which may lead to error in diagnosis. Hence in this paper we present computer aided automatic detection of abnormality in kidney on the ultrasound system itself, to decrease the time for reports and not to depend on the sonographer. We classified the kidney as normal and abnormal case. Segment the kidney region and extract Intensity histogram features and Haralick features from Gray Level Cooccurnace Matrix (GLCM). These features are calculated for a set of large data containing both normal and abnormal cases. Abnormal case includes kidney stone, cyst and bacterial infection. Standard deviation for each parameter is observed, considered only those features with less deviation and implemented on FPGA Kintex board. If the range of mean value is 1.08 to 1.336, skewness is 2.882 to 7.708, Kurtosis is 1.06 to 71.152, Cluster Shade is 72 to 243, Homogeneity is 0.993 to 0.998, the observed kidney image is normal otherwise abnormal.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125331213","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001863
Vitor Pinheiro de Almeida, M. Endler, E. Haeusler
We present a means of generating a mobile clientserver application that enables customized remote monitoring of patients with chronic diseases. The customization is based on parameters and formal descriptions of: type of chronic disease, patient preferences, the monitoring procedure required by the doctor, prescribed medication and information about the current context (i.e., environment) of the patient, obtained from sensors. Based on this, the system determines which pieces of information should be regularly obtained from the patient through questionnaires and/or sensors of the smart phone and sensors connected to it. Relevant information are the ones that help to detect possible changes in the patient's health condition and the monitoring process of a patient by the doctor. The medical treatment and the kind of chronic disease will define the set of data to be collected. It should be stressed that the goal is not to support automatic medical diagnosis, but only to provide means for physicians to obtain updated information about their patients, so as to allow remote monitoring of patients.
{"title":"A framework for customizing the mobile and remote monitoring of patients with chronic diseases","authors":"Vitor Pinheiro de Almeida, M. Endler, E. Haeusler","doi":"10.1109/HealthCom.2014.7001863","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001863","url":null,"abstract":"We present a means of generating a mobile clientserver application that enables customized remote monitoring of patients with chronic diseases. The customization is based on parameters and formal descriptions of: type of chronic disease, patient preferences, the monitoring procedure required by the doctor, prescribed medication and information about the current context (i.e., environment) of the patient, obtained from sensors. Based on this, the system determines which pieces of information should be regularly obtained from the patient through questionnaires and/or sensors of the smart phone and sensors connected to it. Relevant information are the ones that help to detect possible changes in the patient's health condition and the monitoring process of a patient by the doctor. The medical treatment and the kind of chronic disease will define the set of data to be collected. It should be stressed that the goal is not to support automatic medical diagnosis, but only to provide means for physicians to obtain updated information about their patients, so as to allow remote monitoring of patients.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125375307","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001891
Z. Aziz, U. Qureshi, F. Shaikh, Nafeesa Bohra, Abdelmajid Khelil, Emad A. Felemban
The reliable delivery of data is important in designing Wireless Body Area Networks (WBANs) employed for critical applications such as e-health. In order to communicate the data reliably from the sensors to the base station, the data transmission technique (star or multi-hop) and the transmission power plays a very important role. As transmission power is increased, transmission distance is increased and the data can be sent reliably to far nodes. However, in WBANs, there is always a limit to increase the transmission power. Keeping the power level at some low threshold and increasing the distance between a sensor and the base station results in reduced received power which ultimately degrades the data transmission. Thus, for star data transmission technique, the point to ponder is the maximum separation between a sensor and the base station to transmit the data reliably. The reliability in WBANs can be analyzed through different parameters such as received power, received signal strength indicator, link quality indicator, packet error rate, packet reception rate, etc. This paper aims at performing a reliability analysis for WBAN through the mentioned parameters to suggest an optimal sensor/base station separation using star data transmission technique. This analysis is performed employing the default routing protocol in TinyOS called the Collection Tree Protocol. Our study considers different sensor placements on different parts of the body as well as different angular offsets between sensor and base station.
{"title":"Experimental analysis for optimal separation between sensor and base station in WBANs","authors":"Z. Aziz, U. Qureshi, F. Shaikh, Nafeesa Bohra, Abdelmajid Khelil, Emad A. Felemban","doi":"10.1109/HealthCom.2014.7001891","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001891","url":null,"abstract":"The reliable delivery of data is important in designing Wireless Body Area Networks (WBANs) employed for critical applications such as e-health. In order to communicate the data reliably from the sensors to the base station, the data transmission technique (star or multi-hop) and the transmission power plays a very important role. As transmission power is increased, transmission distance is increased and the data can be sent reliably to far nodes. However, in WBANs, there is always a limit to increase the transmission power. Keeping the power level at some low threshold and increasing the distance between a sensor and the base station results in reduced received power which ultimately degrades the data transmission. Thus, for star data transmission technique, the point to ponder is the maximum separation between a sensor and the base station to transmit the data reliably. The reliability in WBANs can be analyzed through different parameters such as received power, received signal strength indicator, link quality indicator, packet error rate, packet reception rate, etc. This paper aims at performing a reliability analysis for WBAN through the mentioned parameters to suggest an optimal sensor/base station separation using star data transmission technique. This analysis is performed employing the default routing protocol in TinyOS called the Collection Tree Protocol. Our study considers different sensor placements on different parts of the body as well as different angular offsets between sensor and base station.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126333258","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001862
Xiaoshuang Liu, Guixia Kang, Ningbo Zhang, Bingning Zhu, Congcong Li, Yi Chai, Yuncheng Liu
This paper presents a healthcare monitoring architecture coupled with a wireless sensor network and wearable sensor systems which monitor chronic patients in nursing house or the elderly in their home. With this architecture, we investigate how sensor nodes are deployed in the three-dimension (3D) monitoring region to achieve uniform distribution, which directly determines the Quality of Service (QoS). Based on the existing two-dimension (2D) coverage-enhancing algorithms for wireless sensor networks, a 3D sensing model and a coverage optimization algorithm are proposed in this paper. Firstly, an intelligent optimization algorithm is utilized to adjust the position of sensor nodes. Then, we pick out the redundant nodes with the set coverage algorithm, and move them into the uncovered area to increase the coverage ratio. The simulation results show that the coverage ratio increased by the coverage optimization algorithm compared with the other coverage algorithms.
{"title":"A three-dimensional network coverage optimization algorithm in healthcare system","authors":"Xiaoshuang Liu, Guixia Kang, Ningbo Zhang, Bingning Zhu, Congcong Li, Yi Chai, Yuncheng Liu","doi":"10.1109/HealthCom.2014.7001862","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001862","url":null,"abstract":"This paper presents a healthcare monitoring architecture coupled with a wireless sensor network and wearable sensor systems which monitor chronic patients in nursing house or the elderly in their home. With this architecture, we investigate how sensor nodes are deployed in the three-dimension (3D) monitoring region to achieve uniform distribution, which directly determines the Quality of Service (QoS). Based on the existing two-dimension (2D) coverage-enhancing algorithms for wireless sensor networks, a 3D sensing model and a coverage optimization algorithm are proposed in this paper. Firstly, an intelligent optimization algorithm is utilized to adjust the position of sensor nodes. Then, we pick out the redundant nodes with the set coverage algorithm, and move them into the uncovered area to increase the coverage ratio. The simulation results show that the coverage ratio increased by the coverage optimization algorithm compared with the other coverage algorithms.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126771168","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 : 2014-09-29DOI: 10.1109/HealthCom.2014.7001848
G. Arrobo, Calvin A. Perumalla, Yang Liu, T. Ketterl, R. Gitlin, P. Fabri
This paper presents the proof-of-concept investigation for a miniaturized vectorcardiogram [VCG] system for ambulatory on-body applications that continuously monitors the electrical activity of the heart in three dimensions. We investigate the minimum distance between a pair of leads in the X, Y and Z axes such that the signals are distinguishable from the noise. The target dimensions for our VCG are 3×3×2 cm and, based on our preliminary results, it is possible to achieve these dimensions. The next step in our research is to build the miniaturized VCG system that includes processing, learning and communication capabilities.
{"title":"A novel vectorcardiogram system","authors":"G. Arrobo, Calvin A. Perumalla, Yang Liu, T. Ketterl, R. Gitlin, P. Fabri","doi":"10.1109/HealthCom.2014.7001848","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001848","url":null,"abstract":"This paper presents the proof-of-concept investigation for a miniaturized vectorcardiogram [VCG] system for ambulatory on-body applications that continuously monitors the electrical activity of the heart in three dimensions. We investigate the minimum distance between a pair of leads in the X, Y and Z axes such that the signals are distinguishable from the noise. The target dimensions for our VCG are 3×3×2 cm and, based on our preliminary results, it is possible to achieve these dimensions. The next step in our research is to build the miniaturized VCG system that includes processing, learning and communication capabilities.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115065241","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 : 2014-08-31DOI: 10.1109/HealthCom.2014.7001902
A. Black, T. Sahama
With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health information sharing requirements. Thus an opportunity to transform the beleaguered Australian PCEHR into a sustainable on-demand technology consumption model for patient safety must be explored further. Moreover, the current clerical focus of healthcare practitioners must be renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. As a conceptual paper, the goal of the authors is to deliver insights into the antecedents of usage influencing superior patient outcomes within an eHealth-as-a-Service framework. To achieve this, the paper attempts to distil key concepts and identify common themes drawn from a preliminary literature review of eHealth and cloud computing concepts, specifically cloud service orchestration to establish a conceptual framework and a research agenda. Initial findings support the authors' view that an eHealth-as-a-Service (eHaaS) construct will serve as a disruptive paradigm shift in the aggregation and transformation of health information for use as real-world knowledge in patient care scenarios. Moreover, the strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of practice based evidence and to engage market forces to create financial sustainability.
{"title":"eHealth-as-a-Service (eHaaS): The industrialisation of health informatics, a practical approach","authors":"A. Black, T. Sahama","doi":"10.1109/HealthCom.2014.7001902","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001902","url":null,"abstract":"With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health information sharing requirements. Thus an opportunity to transform the beleaguered Australian PCEHR into a sustainable on-demand technology consumption model for patient safety must be explored further. Moreover, the current clerical focus of healthcare practitioners must be renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. As a conceptual paper, the goal of the authors is to deliver insights into the antecedents of usage influencing superior patient outcomes within an eHealth-as-a-Service framework. To achieve this, the paper attempts to distil key concepts and identify common themes drawn from a preliminary literature review of eHealth and cloud computing concepts, specifically cloud service orchestration to establish a conceptual framework and a research agenda. Initial findings support the authors' view that an eHealth-as-a-Service (eHaaS) construct will serve as a disruptive paradigm shift in the aggregation and transformation of health information for use as real-world knowledge in patient care scenarios. Moreover, the strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of practice based evidence and to engage market forces to create financial sustainability.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014494","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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