Pub Date : 2014-10-01DOI: 10.1109/HealthCom.2014.7001837
F. Zakaria, C. Toulouse, M. Mohamed el Badaoui, C. Servière, M. Khalil
There have been numerous studies involving research and development, for detecting falls exhibited by the elderly. Considering that the prevention of a falling elderly is much more complex to address and estimate, very little research has been done. In fact research is often strictly limited resourceful medical organizations that have specialized clinical tools. Human locomotion, particularly “Walking” is defined by sequences of cyclic and repeated gestures. The variability of such sequences can reveal information about drive failure and motor / motor-neuron disorders. Studying and exploiting the Cyclostationary (CS) properties of such sequences, offers a complementary way to quantify human locomotion and its changes with progressing aging and the development of diseases. This quantization may provide an insight into the neural function and the neural control of walking which would be altered by changes associated with aging and the presence of certain diseases. As part of the collaboration between LASPI and CHU Saint Etienne, we decided to focus on certain advanced signal processing theory and methods, to study very complex phenomena of human walking, which is often subject to numerous motor and / or motor-neurons malfunctions, such as in the case of the falling elderly population, that often has serious and severe consequences. Furthermore, this paper also examined the effects on walking in elderly subjects in three task conditions: (a) single task (MS) and (b) dual task: walking by performing a fluency task(MF) and (c) walking while backward counting (MD). Results show that the conditions of walking impacted the Cyclostationarity and its known indicator: the cyclic autocorrelation function. Such indicator also evolved between fallers and non-fallers and between the fallers who have history of falls and those who haven't.
已经有大量的研究和开发，用于检测老年人所表现出的跌倒。考虑到老年人摔倒的预防要复杂得多，难以处理和估计，因此做的研究很少。事实上，研究往往受到严格限制，资源丰富的医疗机构有专门的临床工具。人类的运动，特别是“行走”是由一系列循环和重复的手势来定义的。这些序列的可变性可以揭示驱动故障和运动/运动神经元疾病的信息。研究和利用这些序列的循环静止(CS)特性，为量化人类运动及其随年龄增长和疾病发展的变化提供了一种补充方法。这种量化可以为神经功能和行走的神经控制提供一种见解，这些神经功能和神经控制将被与衰老和某些疾病相关的变化所改变。作为LASPI和CHU Saint Etienne之间合作的一部分，我们决定将重点放在某些先进的信号处理理论和方法上，以研究人类行走的非常复杂的现象，这些现象通常受到许多运动和/或运动神经元故障的影响，例如在老年人跌倒的情况下，这通常会产生严重和严重的后果。此外，本文还研究了三种任务条件对老年人行走的影响:(a)单任务(MS)和(b)双任务:通过执行流畅性任务(MF)和(c)边走边倒数(MD)。结果表明，步行条件对循环平稳性及其已知指标循环自相关函数有影响。这一指标在跌倒者和非跌倒者之间以及有跌倒史和没有跌倒史的人之间也会发生变化。
{"title":"Contribution of the cyclic correlation in gait analysis: Variation between fallers and non-fallers","authors":"F. Zakaria, C. Toulouse, M. Mohamed el Badaoui, C. Servière, M. Khalil","doi":"10.1109/HealthCom.2014.7001837","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001837","url":null,"abstract":"There have been numerous studies involving research and development, for detecting falls exhibited by the elderly. Considering that the prevention of a falling elderly is much more complex to address and estimate, very little research has been done. In fact research is often strictly limited resourceful medical organizations that have specialized clinical tools. Human locomotion, particularly “Walking” is defined by sequences of cyclic and repeated gestures. The variability of such sequences can reveal information about drive failure and motor / motor-neuron disorders. Studying and exploiting the Cyclostationary (CS) properties of such sequences, offers a complementary way to quantify human locomotion and its changes with progressing aging and the development of diseases. This quantization may provide an insight into the neural function and the neural control of walking which would be altered by changes associated with aging and the presence of certain diseases. As part of the collaboration between LASPI and CHU Saint Etienne, we decided to focus on certain advanced signal processing theory and methods, to study very complex phenomena of human walking, which is often subject to numerous motor and / or motor-neurons malfunctions, such as in the case of the falling elderly population, that often has serious and severe consequences. Furthermore, this paper also examined the effects on walking in elderly subjects in three task conditions: (a) single task (MS) and (b) dual task: walking by performing a fluency task(MF) and (c) walking while backward counting (MD). Results show that the conditions of walking impacted the Cyclostationarity and its known indicator: the cyclic autocorrelation function. Such indicator also evolved between fallers and non-fallers and between the fallers who have history of falls and those who haven't.","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":"114714507","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.2014.7001824
G. Kalogridis, Saraansh Dave
The societal need for better public healthcare calls for granular, continuous, nationwide instrumentation and data fusion technologies. However, the current trend of centralised (database) health analytics gives rise to data privacy issues. This paper proposes sensor data mining algorithms that help infer health/well-being related lifestyle patterns and anomalous (or privacy-sensitive) events. Such algorithms enable a user-centric context awareness at the network edge, which can be used for decentralised eHealth decision making and privacy protection by design. The main hypothesis of this work involves the detection of atypical behaviours from a given stream of energy consumption data recorded at eight houses over a period of a year for cooking, microwave, and TV activities. Our initial exploratory results suggest that in the case of an unemployed single resident, the day-by-day variability of TV or microwave operation, in conjunction with the variability of the absence of other cooking activity, is more significant as compared with the variability of other combinations of activities. The proposed methodology brings together appliance monitoring, privacy, and anomaly detection within a healthcare context, which is readily scalable to include other health-related sensor streams.
{"title":"Privacy and eHealth-enabled smart meter informatics","authors":"G. Kalogridis, Saraansh Dave","doi":"10.1109/HealthCom.2014.7001824","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001824","url":null,"abstract":"The societal need for better public healthcare calls for granular, continuous, nationwide instrumentation and data fusion technologies. However, the current trend of centralised (database) health analytics gives rise to data privacy issues. This paper proposes sensor data mining algorithms that help infer health/well-being related lifestyle patterns and anomalous (or privacy-sensitive) events. Such algorithms enable a user-centric context awareness at the network edge, which can be used for decentralised eHealth decision making and privacy protection by design. The main hypothesis of this work involves the detection of atypical behaviours from a given stream of energy consumption data recorded at eight houses over a period of a year for cooking, microwave, and TV activities. Our initial exploratory results suggest that in the case of an unemployed single resident, the day-by-day variability of TV or microwave operation, in conjunction with the variability of the absence of other cooking activity, is more significant as compared with the variability of other combinations of activities. The proposed methodology brings together appliance monitoring, privacy, and anomaly detection within a healthcare context, which is readily scalable to include other health-related sensor streams.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"72 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":"130277705","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.2014.7001809
S. Tokunaga, S. Matsumoto, S. Saiki, Masahide Nakamura
The goal of this paper is to find an answer that how remote monitoring sensor should be accurate. To achieve the goal, we propose three methods, generalization by three-actor model, design the algorithm of the three-actor and development of RMS simulator. With the three-actor model, we can generalize RMS by interactions among three actors. As the second step, we design the algorithms that how to work the actor in RMS. So we could express how often the elderly become ill. Moreover, using the developed simulator, we could simulate with many patterns of conditions. The result of simulations shows that if the accuracy of the sensor is greater than 0.9990, then the RMS has much more detectionPower.
{"title":"How should remote monitoring sensor be accurate?","authors":"S. Tokunaga, S. Matsumoto, S. Saiki, Masahide Nakamura","doi":"10.1109/HealthCom.2014.7001809","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001809","url":null,"abstract":"The goal of this paper is to find an answer that how remote monitoring sensor should be accurate. To achieve the goal, we propose three methods, generalization by three-actor model, design the algorithm of the three-actor and development of RMS simulator. With the three-actor model, we can generalize RMS by interactions among three actors. As the second step, we design the algorithms that how to work the actor in RMS. So we could express how often the elderly become ill. Moreover, using the developed simulator, we could simulate with many patterns of conditions. The result of simulations shows that if the accuracy of the sensor is greater than 0.9990, then the RMS has much more detectionPower.","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":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131091856","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.2014.7001832
A. Manirabona, L. Chaari, S. Boudjit
Wireless Body Area Network (WBAN) consists of a set of sensor nodes deployed on or implanted in the body and these nodes send sensed physiological data to the personal assistant. Some of these sensor nodes can be located far from the personal assistant or due to body posture the link between the node and the personal assistant is obstructed and so require an intermediate node to help relay their data. As defined in the IEEE 802.15.6 standard, a node can initiate the two-hop extension cooperative communication to relay other nodes data. However, it is impossible to accept relaying for more than one node at the same time. In addition, when a relay node has data to send too, it has to choose either to leave the relaying mode or to maintain it. In this paper we propose a Decode and Merge technique that maintains the relaying mode by merging frames from relayed and relaying nodes. By doing so, a MAC format resizing is required. Apart from maintaining cooperative communication, this technique increases the general throughput without increasing the energy consumption, management and control flows. Furthermore, it increases the ability to resist against interference.
{"title":"Decode and merge cooperative MAC protocol for intra WBAN communication","authors":"A. Manirabona, L. Chaari, S. Boudjit","doi":"10.1109/HealthCom.2014.7001832","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001832","url":null,"abstract":"Wireless Body Area Network (WBAN) consists of a set of sensor nodes deployed on or implanted in the body and these nodes send sensed physiological data to the personal assistant. Some of these sensor nodes can be located far from the personal assistant or due to body posture the link between the node and the personal assistant is obstructed and so require an intermediate node to help relay their data. As defined in the IEEE 802.15.6 standard, a node can initiate the two-hop extension cooperative communication to relay other nodes data. However, it is impossible to accept relaying for more than one node at the same time. In addition, when a relay node has data to send too, it has to choose either to leave the relaying mode or to maintain it. In this paper we propose a Decode and Merge technique that maintains the relaying mode by merging frames from relayed and relaying nodes. By doing so, a MAC format resizing is required. Apart from maintaining cooperative communication, this technique increases the general throughput without increasing the energy consumption, management and control flows. Furthermore, it increases the ability to resist against interference.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"28 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":"128689649","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.2014.7001880
N. Rodríguez, J. Lilius, Sebu Björklund, J. Majors, K. Rautanen, Riitta Danielsson-Ojala, Hanna Pirinen, Lotta Kauhanen, S. Salanterä, T. Salakoski, Ilona Tuominen
Filling medication trays and dispensing them at hospital wards is a painstaking, time-consuming and tedious task involving searching for medication in large shelves, double checking in the daily filled tray that the appearance, amount and concentration of each medication corresponds to the prescription, as well as analysing the timing conditions, among other details. Finally, if needed, finding equivalent compounds containing no secondary effects is also crucial, as well as being aware of the dynamically changing treatments in patients located, e.g., in surgery wards. Once the tray is filled, similar concerns and checks need to be done before dispensing the medication to the patient. We conducted a pilot in two university hospital wards using eye-tracking glasses and stress response to assess the tasks that take time the most and are most meticulous or stressing for the nurses. The aim is to use the findings to implement a mobile application that helps saving time and proneness to errors daily in such complex nursing procedures.
{"title":"Can IT health-care applications improve the medication tray-filling process at hospital wards? An exploratory study using eye-tracking and stress response","authors":"N. Rodríguez, J. Lilius, Sebu Björklund, J. Majors, K. Rautanen, Riitta Danielsson-Ojala, Hanna Pirinen, Lotta Kauhanen, S. Salanterä, T. Salakoski, Ilona Tuominen","doi":"10.1109/HEALTHCOM.2014.7001880","DOIUrl":"https://doi.org/10.1109/HEALTHCOM.2014.7001880","url":null,"abstract":"Filling medication trays and dispensing them at hospital wards is a painstaking, time-consuming and tedious task involving searching for medication in large shelves, double checking in the daily filled tray that the appearance, amount and concentration of each medication corresponds to the prescription, as well as analysing the timing conditions, among other details. Finally, if needed, finding equivalent compounds containing no secondary effects is also crucial, as well as being aware of the dynamically changing treatments in patients located, e.g., in surgery wards. Once the tray is filled, similar concerns and checks need to be done before dispensing the medication to the patient. We conducted a pilot in two university hospital wards using eye-tracking glasses and stress response to assess the tasks that take time the most and are most meticulous or stressing for the nurses. The aim is to use the findings to implement a mobile application that helps saving time and proneness to errors daily in such complex nursing procedures.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"64 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":"126467890","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.2014.7001879
M. Schapranow, K. Klinghammer, Cindy Fähnrich, H. Plattner
Latest medical diagnostics generate increasing amounts of big medical data. Specific software tools optimized for the use by healthcare experts and researchers as well as systematic processes for data processing and analysis in clinical and research environments are still missing. Our work focuses on the integration of high-throughput next-generation sequencing data and its systematic processing and its instantaneous analysis to use them in the course of precision medicine. We share our research results on designing a generic research process for drug response analysis including specific software tools built on top of our distributed in-memory computing platform for processing of big medical data. Furthermore, we present our technical foundations as well as process aspects of integrating and combining heterogeneous data sources, such as genome, patient, and experimental data.
{"title":"In-memory technology enables interactive drug response analysis","authors":"M. Schapranow, K. Klinghammer, Cindy Fähnrich, H. Plattner","doi":"10.1109/HealthCom.2014.7001879","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001879","url":null,"abstract":"Latest medical diagnostics generate increasing amounts of big medical data. Specific software tools optimized for the use by healthcare experts and researchers as well as systematic processes for data processing and analysis in clinical and research environments are still missing. Our work focuses on the integration of high-throughput next-generation sequencing data and its systematic processing and its instantaneous analysis to use them in the course of precision medicine. We share our research results on designing a generic research process for drug response analysis including specific software tools built on top of our distributed in-memory computing platform for processing of big medical data. Furthermore, we present our technical foundations as well as process aspects of integrating and combining heterogeneous data sources, such as genome, patient, and experimental data.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"1 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":"123083102","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.2014.7001831
A. Benharref, M. Serhani, R. Mizouni
Nowadays, mobile applications/devices have become the trends, especially, when they were gradually shifted from basic communication services to supporting more sophisticated service provisioning. Mobile applications are usually very light, are nowadays likely to be often connected to the Internet, and can be used quite easily. However, these applications exhibit some challenges related to limited resources they have access to, including limited processing power, memory, storage size, battery power, and intermittent network connection. In fact, these considerations have to be taken seriously into consideration when developing mobile applications especially if those applications will be used for critical services, for example, to collect and report vital health data over a long period of time. In this paper, we study the use of mobile applications for monitoring patient's vital. Mobile devices, through an application, are connected to body-strapped biosensors to collect and synchronize these parameters with information systems. This synchronization should be done in such a way that the cost of synchronization is kept low and urgent readings are delivered as soon as possible. To optimize the synchronization process and reduce its cost, we propose and validate cost-oriented algorithms. A case study is developed to illustrate the applicability and effectiveness of our innovative techniques in making continuous monitoring an efficient process.
{"title":"Smart data synchronization in m-Health monitoring applications","authors":"A. Benharref, M. Serhani, R. Mizouni","doi":"10.1109/HealthCom.2014.7001831","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001831","url":null,"abstract":"Nowadays, mobile applications/devices have become the trends, especially, when they were gradually shifted from basic communication services to supporting more sophisticated service provisioning. Mobile applications are usually very light, are nowadays likely to be often connected to the Internet, and can be used quite easily. However, these applications exhibit some challenges related to limited resources they have access to, including limited processing power, memory, storage size, battery power, and intermittent network connection. In fact, these considerations have to be taken seriously into consideration when developing mobile applications especially if those applications will be used for critical services, for example, to collect and report vital health data over a long period of time. In this paper, we study the use of mobile applications for monitoring patient's vital. Mobile devices, through an application, are connected to body-strapped biosensors to collect and synchronize these parameters with information systems. This synchronization should be done in such a way that the cost of synchronization is kept low and urgent readings are delivered as soon as possible. To optimize the synchronization process and reduce its cost, we propose and validate cost-oriented algorithms. A case study is developed to illustrate the applicability and effectiveness of our innovative techniques in making continuous monitoring an efficient process.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"43 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":"131356825","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.2014.7001875
W. Jiang, S. Gao, P. Wittek, Li Zhao
Photoplethysmography (PPG) can be carried out through facial video recording by a smart phone camera in ambient light. The main challenge is to eliminate motion artifacts and ambient noise. We describe a real-time algorithm to quantify the heart beat rate from facial video recording captured by the camera of a smart phone. We extract the green channel from the video. Then we normalize it and use a Kalman filter with a particular structure to eliminate ambient noise. This filter also enhances the heart pulse component in the signal distorted by Gaussian noise and white noise. After that we employ a band-pass FIR filter to remove the remaining motion artifacts. This is followed by peak detection or Lomb periodogram to estimate heart rate. The algorithm has low computational overhead, low delay and high robustness, making it suitable for real-time interaction on a smart phone. Finally we describe an Android application based on this study.
{"title":"Real-time quantifying heart beat rate from facial video recording on a smart phone using Kalman filters","authors":"W. Jiang, S. Gao, P. Wittek, Li Zhao","doi":"10.1109/HealthCom.2014.7001875","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001875","url":null,"abstract":"Photoplethysmography (PPG) can be carried out through facial video recording by a smart phone camera in ambient light. The main challenge is to eliminate motion artifacts and ambient noise. We describe a real-time algorithm to quantify the heart beat rate from facial video recording captured by the camera of a smart phone. We extract the green channel from the video. Then we normalize it and use a Kalman filter with a particular structure to eliminate ambient noise. This filter also enhances the heart pulse component in the signal distorted by Gaussian noise and white noise. After that we employ a band-pass FIR filter to remove the remaining motion artifacts. This is followed by peak detection or Lomb periodogram to estimate heart rate. The algorithm has low computational overhead, low delay and high robustness, making it suitable for real-time interaction on a smart phone. Finally we describe an Android application based on this study.","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":"123934679","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.2014.7001845
Anders Andersen, K. Y. Yigzaw, Randi Karlsen
The usage of electronic health data from different sources for statistical analysis requires a toolset where the legal, security and privacy concerns have been taken into consideration. The health data are typically located at different general practices and hospitals. The data analysis consists of local processing at these locations, and the locations become nodes in a computing graph. To support the legal, security and privacy concerns, the proposed toolset for statistical analysis of health data uses a combination of secure multi-party computation (SMC) algorithms, symmetric and public key encryption, and public key infrastructure (PKI) with certificates and a certificate authority (CA). The proposed toolset should cover a wide range of data analysis with different data distributions. To achieve this, large set of possible SMC algorithms and computing graphs have to be supported.
{"title":"Privacy preserving health data processing","authors":"Anders Andersen, K. Y. Yigzaw, Randi Karlsen","doi":"10.1109/HealthCom.2014.7001845","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001845","url":null,"abstract":"The usage of electronic health data from different sources for statistical analysis requires a toolset where the legal, security and privacy concerns have been taken into consideration. The health data are typically located at different general practices and hospitals. The data analysis consists of local processing at these locations, and the locations become nodes in a computing graph. To support the legal, security and privacy concerns, the proposed toolset for statistical analysis of health data uses a combination of secure multi-party computation (SMC) algorithms, symmetric and public key encryption, and public key infrastructure (PKI) with certificates and a certificate authority (CA). The proposed toolset should cover a wide range of data analysis with different data distributions. To achieve this, large set of possible SMC algorithms and computing graphs have to be supported.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"28 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":"117022430","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.2014.7001860
Nagla S. Alnosayan, Edward Lee, A. Alluhaidan, S. Chatterjee, L. Houston-Feenstra, M. Kagoda, W. Dysinger
MyHeart is a telehealth system designed to bridge the current gap in the Congestive Heart Failure care continuum that occurs when the patient transitions from the hospital to the home environment. The system uses wireless health devices and a mobile application on the patient's end, a rule-based expert system, and a dashboard on the clinician's end to facilitate the exchange of information pertaining to vitals, symptoms, and health risk. The system also sends messages to patients that aim to encourage self-care as per Fogg's behavior model. An experiment to evaluate MyHeart is currently underway at Loma Linda University Medical Center and encouraging initial findings are reported.
{"title":"MyHeart: An intelligent mHealth home monitoring system supporting heart failure self-care","authors":"Nagla S. Alnosayan, Edward Lee, A. Alluhaidan, S. Chatterjee, L. Houston-Feenstra, M. Kagoda, W. Dysinger","doi":"10.1109/HealthCom.2014.7001860","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001860","url":null,"abstract":"MyHeart is a telehealth system designed to bridge the current gap in the Congestive Heart Failure care continuum that occurs when the patient transitions from the hospital to the home environment. The system uses wireless health devices and a mobile application on the patient's end, a rule-based expert system, and a dashboard on the clinician's end to facilitate the exchange of information pertaining to vitals, symptoms, and health risk. The system also sends messages to patients that aim to encourage self-care as per Fogg's behavior model. An experiment to evaluate MyHeart is currently underway at Loma Linda University Medical Center and encouraging initial findings are reported.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"38 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":"115278081","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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