Alessandro Copetti

Intelligent context-aware monitoring of hypertensive patients

We present a decision-level data fusion technique for monitoring and reporting critical health conditions of a hypertensive patient at home. Variables associated to the patient (physiological and behavioral) and to the living environment are considered in the solution, contributing to improve the confidence on the system outputs. In the paper, we model the problem variables as fuzzy, aiming to capture their intrinsic essence, and draw rules based on medical recommendations to identify the health condition of the patient. This initiative move towards to build an abstract framework for context-aware telemonitoring applications. We also describe the relevant components of the framework and provide an initial evaluation of its decision component. Our results demonstrate that a principled choice of rules and variables may lead to a consistent identification of critical patients conditions.

A decision-making mechanism for context inference in pervasive healthcare environments

This paper presents a Fuzzy approach to health-monitoring of patients in pervasive computing environments. A decision model considers three classes of variables that represent the context information being collected: environmental, physiological, and behavioral. A case study of blood pressure monitoring was developed to identify critical situations based on medical knowledge. The solution maintains the interpretability of the decision rules, even after a learning phase which may propose adjustments in these rules. In this phase, the Fuzzy c-Means clustering was chosen to adjust membership functions, using the cluster centers. A medical team evaluated data from 24-h monitoring of 30 patients and the rating was compared with the results of the system. The proposed approach proved to be individualized, identifying critical events in patients with different levels of blood pressure with an accuracy of 90% and low number of false negatives.

Applying context-aware techniques to design remote assisted living applications

We present an architecture that includes two essential services to compose the supporting infrastructure required by context-aware applications: a Context Service, that provides access to context information, and a Discovery Service. A reference implementation, based on Web Services technology, was used to develop a remote assisted living application, which relies on ambient sensors, placed in each room, to capture context information and on a combo medical appliance to perform blood pressure measurements on the patient, according to a care plan prescribed by a doctor. The collected set of context data may be transmitted to a monitoring centre and also interpreted locally using medical knowledge; the identification of a patients abnormal condition can activate local actions or send an emergency message to a monitoring centre. This infrastructure can help a doctor to monitor and assist the patient while he or she is performing daily activities and may also help improving the patients treatment compliance and quality of life.

Um mecanismo de decisão para inferência de contexto em ambientes pervasivos de tratamento de saúde

This paper presents a Fuzzy approach to monitor the health of a patient in pervasive computing environments. A decision model considers three classes of variables that represent the context information being collected: environmental, physiological and behavioral. A case study of blood pressure monitoring was developed to identify critical situations based on medical knowledge. The solution maintains the interpretability of a set of defined rules, even after a learning phase that proposes adjustments to them. In this phase, the Fuzzy C-Means clustering was chosen to adjust membership functions, using the cluster centers. A medical team evaluated data from 24-hour monitoring of 30 patients and the rating was compared with the results of the system. The proposed approach proved to be individualized, identifying critical events in patients with different levels of blood pressure with an accuracy of 90% and low rate of false negatives.

Using Discovery and Monitoring Services to Support Context-Aware Remote Assisted Living Applications

Ubiquitous and pervasive applications are aware of the context of the used resources. This class of application can benefit from mechanisms to discover resources (devices and sensors) that meet their requirements and mechanisms to monitor the state and provide access to the functionalities of these resources. We present an architecture that includes two essential services to compose the supporting infrastructure required by the mentioned applications: a Context Service, that provides access to context information, and a Discovery Service. Resource Agents, which encapsulate and provide access to the actual resources and sensors, are also included in the proposal. A reference implementation, based on Web Services technology, was used to develop a remote assisted living application, which relies on ambient sensors, placed in each room, to capture context information and on a combo medical appliance to perform blood pressure measurements on the patient, according to a care plan prescribed by a doctor. The collected set of context data may be transmitted to a monitoring center and also interpreted locally using medical knowledge; the identification of a patients abnormal condition can activate local actions or send an emergency message to a monitoring center. The local infrastructure can help a doctor to monitor and assist the patient while he or she is performing daily activities. It may also help reducing hospitalization time and improving the patient’s treatment compliance and quality of life.