Depending on the previous data, the central unit will be able to detect any potential
risk or abnormal situations by comparing the current value of each factor in the substates with its normal range, which had been adapted by every sub adaptation unit.
When an abnormal situation is detected, the central unit will detect what action
should be taken to prevent an exacerbation in the patient’s health state.
5 Validation
In order to test and validate our proposed system, we implemented a simulation app
using some data obtained from medical records to simulate the streamed data and a set
of COPD rules extracted in [25] to create some testing scenarios. The main focus of the
validation process was on the efficiency of the system to provide continuous monitoring of the patient status, and the ability to apply and adapt the required changes to
prevent any dangerous exacerbation. The testing scenarios we had performed, proofed
the ability of our system to handle the complexity of monitoring the enormous amount
of contextual data, and keep track of the latest updates in the global state. Also,
following an aspect-oriented approach facilitates the implementation of the adaptation
logic, by separating the categories of data that each Adaptation Unit needs to be
responsible for observing. After testing some rules that lead to call a sequential set of
actions and multiple updates in the state units, the system was able to adapt the safe
ranges for the different environmental and biometrical factors and detect suitable action
in an abnormal situation. Nevertheless, our system still needs to be tested when it is
connected to the whole rules engine when all COPD rules are inserted into the engine,
which will be done in future work.
6 Conclusion
In this paper, we have presented an architecture for a context-aware self-adaptive
system that is used to develop a COPD healthcare telemonitoring system. The system is
backed out by a medical rules engine in the COPD domain that is used as the
knowledge base to determine the safe ranges for patient’s biomarkers and external
factors, then detect the precluding actions needed to be taken to prevent severe exacerbations in patient’s health state.
Our main contribution in this work is providing a context-aware self-adaptive
system architecture that is dealing with the huge variety and complexity of contextual
data and different sets of services by implementing a decentralized adaptation unit,
which makes the monitoring and adaptation task easier and less complex by applying
the separation of concerns principle.
Context-Aware Healthcare Adaptation Model for COPD Diseases
313
risk or abnormal situations by comparing the current value of each factor in the substates with its normal range, which had been adapted by every sub adaptation unit.
When an abnormal situation is detected, the central unit will detect what action
should be taken to prevent an exacerbation in the patient’s health state.
5 Validation
In order to test and validate our proposed system, we implemented a simulation app
using some data obtained from medical records to simulate the streamed data and a set
of COPD rules extracted in [25] to create some testing scenarios. The main focus of the
validation process was on the efficiency of the system to provide continuous monitoring of the patient status, and the ability to apply and adapt the required changes to
prevent any dangerous exacerbation. The testing scenarios we had performed, proofed
the ability of our system to handle the complexity of monitoring the enormous amount
of contextual data, and keep track of the latest updates in the global state. Also,
following an aspect-oriented approach facilitates the implementation of the adaptation
logic, by separating the categories of data that each Adaptation Unit needs to be
responsible for observing. After testing some rules that lead to call a sequential set of
actions and multiple updates in the state units, the system was able to adapt the safe
ranges for the different environmental and biometrical factors and detect suitable action
in an abnormal situation. Nevertheless, our system still needs to be tested when it is
connected to the whole rules engine when all COPD rules are inserted into the engine,
which will be done in future work.
6 Conclusion
In this paper, we have presented an architecture for a context-aware self-adaptive
system that is used to develop a COPD healthcare telemonitoring system. The system is
backed out by a medical rules engine in the COPD domain that is used as the
knowledge base to determine the safe ranges for patient’s biomarkers and external
factors, then detect the precluding actions needed to be taken to prevent severe exacerbations in patient’s health state.
Our main contribution in this work is providing a context-aware self-adaptive
system architecture that is dealing with the huge variety and complexity of contextual
data and different sets of services by implementing a decentralized adaptation unit,
which makes the monitoring and adaptation task easier and less complex by applying
the separation of concerns principle.
Context-Aware Healthcare Adaptation Model for COPD Diseases
313