BDCAM: big data for context-aware Monitoring

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

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CELL: +91 888-379-0633 | +91 979-000-9190
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
patient’s health condition and activities related to a patient’s behavior that
can be inferred by processing data from sensors, cameras, RFIDs etc.

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

BDCAM: big data for context-aware Monitoring

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