BDCaM: Big Data for Context-aware Monitoring - A Personalized Knowledge Discovery Framework for Assisted Healthcare

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    BDCaM: Big Data for Context-aware Monitoring - A Personalized Knowledge

    Discovery Framework for Assisted Healthcare

    Abstract:

    Context-aware monitoring is an emerging technology that provides real-time

    personalized health-care services and a rich area of big data application. In this

    paper, we propose a knowledge discovery-based approach that allows the context-

    aware system to adapt its behavior in runtime by analyzing large amounts of data

    generated in ambient assisted living (AAL) systems and stored in cloud

    repositories. The proposed BD CaM model facilitates analysis of big data inside a

    cloud environment. It first mines the trends and patterns in the data of an individual

    patient with associated probabilities and utilizes that knowledge to learn proper

    abnormal conditions. The outcomes of this learning method are then applied in

    context-aware decision-making processes for the patient. A use case is

    implemented to illustrate the applicability of the framework that discovers the

    knowledge of classification to identify the true abnormal conditions of patients

    having variations in blood pressure (BP) and heart rate (HR). The evaluation shows

    a much better estimate of detecting proper anomalous situations for different types

    of patients.

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    Existing System:

    A cloud-oriented context-aware middleware (Co Ca MAAL) and proved its

    advantage for processing and managing large amount of contexts gathered from

    multiple AAL systems. In Co Ca MAAL, we described the context-aware service

    identification process using high level generalized medical rules. The model lacked

    an important feature such as personalized knowledge discovery which could be

    derived from a large amount of patient data stored in the cloud repositories.

    Therefore we developed BD CaM, an extended version of the Co Ca MAAL

    model. This includes the functionalities of learning and the knowledge discovery

    process to find patient-specific anomalies using large amounts of data.

  • Ki-Tech Solutions offering IT courses(C, C++, Java, Asp.Net, C# and Android) for everyone-

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    Disadvantages:

    Typical architecture involves body sensors, ambient and smart sensors,

    devices, actuators and software services that collect data from a target user

    who lives alone and has some kind of disability.

    The data is collected continuously at different times of the day and also, in

    some cases, on a demand basis.

    The data can be physiological, environmental conditions that affect a

    patients health condition and activities related to a patients behavior that

    can be inferred by processing data from sensors, cameras, RFIDs etc.

  • Ki-Tech Solutions offering IT courses(C, C++, Java, Asp.Net, C# and Android) for everyone-

    from a beginner who wants to learn computer basics to a software engineer who wishes to take

    a global certification exam.

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    WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB

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    Mail to: kitechsolutions.in@gmail.com

    Proposed System:

    An important feature of remote monitoring applications is to identify the

    abnormal conditions of a patient accurately and so send appropriate alerts to the

    care givers.

    For that reason build an innovative architectural model for context-aware

    monitoring, BD CaM that uses cloud computing platforms. Every generated

    context of AAL systems is sent to the cloud. A number of distributed servers in the

    cloud store and process those contexts to extract required information for decision-

    making using this novel technique.

    Develop a 2-step learning methodology. In the first step, the system

    identifies the correlations between context attributes and the threshold values of

    vital signs. Using Map Reduce A priori algorithm, over a long term context data of

    a particular patient, the system generates a set of association rules that are specific

    to that patient. In the second step, the system uses supervised learning over a new

    large set of context data generated using the rules discovered in the first step. In

    this way, the system becomes more robust.

  • Ki-Tech Solutions offering IT courses(C, C++, Java, Asp.Net, C# and Android) for everyone-

    from a beginner who wants to learn computer basics to a software engineer who wishes to take

    a global certification exam.

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    WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB

    PROJECTS AND IPT TRAINING IN RAJAPALAYAM, VIRUDHUNAGAR DISTRICTS, AND TAMILNADU.

    CELL: +91 888-379-0633 | +91 979-000-9190

    Mail to: kitechsolutions.in@gmail.com

    Advantages:

    o Our system refines patient specific rules from big data and simplifies

    the job of healthcare professionals by providing early detection of

    anomalous situations with good accuracy.

    o Our system is to classify a situation correctly to send proper alerts to

    the right RMS.

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