A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living

Context-aware monitoring is an emerging technology that provides real-time personalised 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 behaviour in runtime by analysing large amounts of data generated in ambient assisted living (AAL) systems and stored in cloud repositories . The proposed BDCaM 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. The accuracy and efficiency obtained for the implemented case study demonstrate the effectiveness of the proposed model.

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

Pervasive computing has emerged as a viable solution capable of providing technology-driven assistive living for elderly. The pervasive healthcare system, Context-Aware Real-time Assistant (CARA), is designed to provide personalized healthcare services for elderly in a timely and appropriate manner by adapting the healthcare technology to fit in with normal activities of the elderly and working practices of the caregivers. The work in this paper introduces a personalized, flexible, and extensible hybrid reasoning framework for CARA system in a smart home environment which provides context-aware sensor data fusion as well as anomaly detection mechanisms that supports activity of daily living analysis and alert generation. We study how the incorporation of rule-based and case-based reasoning enables CARA to become more robust and to adapt to a changing environment by continuously retraining with new cases. Noteworthy about the work is the use of case-based reasoning to detect conditional anomalies for home automation, and the use of hierarchical fuzzy rule-based reasoning to deal with exceptions and to achieve query-sensitive case retrieval and case adaptation. Case study for evaluation of this hybrid reasoning framework is carried out under simulated but realistic smart home scenarios. The results indicate the feasibility of the framework for effective at-home monitoring.

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Context-aware hybrid reasoning framework for pervasive healthcare

ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data

Fuzzy CARA - A Fuzzy-Based Context Reasoning System For Pervasive Healthcare

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 patient's conditions.

Intelligent context-aware monitoring of hypertensive patients

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.

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

Objetivo: concepcao de um sistema computacional inteligente para interligar o paciente em sua casa e os profissionais de saude, por meio da integracao de diversos servicos relacionados ao monitoramento remoto da saude do paciente. Metodo: analise das principais experiencias e trabalhos relacionados a area de computacao ubiqua na assistencia domiciliar a saude, em conjunto com especialistas da area de saude, com o objetivo de identificar os requisitos essenciais que devem ser tratados para a viabilizacao de um sistema dessa classe. Resultados: um sistema com uma arquitetura flexivel que integra uma infraestrutura de sensores, dispositivos, e servicos inteligentes para (i) continua identificacao da situacao de saude do paciente e envio de alertas; (ii) envio de notificacoes e lembretes direcionados ao paciente e associados ao plano de cuidados, elaborado pelo profissional de saude. Um prototipo implementado com foco na manipulacao de dados de pressao arterial e frequencia cardiaca e apresentado para demonstrar a viabilidade da proposta. Conclusao: o sistema proposto executa servicos integrados que permitem o acompanhamento continuo da evolucao do tratamento do paciente em sua casa.

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Sistema de computação ubíqua na assistência domiciliar à saúde

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 patient's 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 patient's treatment compliance and quality of life.

Applying context-aware techniques to design remote assisted living applications

Pervasive home health care applications require an infrastructure of specialized hardware and software capable of collecting and processing data from the patient and the environment. We present here a remote monitoring home health care prototype, whose patients, following a prescribed care plan, have their data (physiological and activity) collected by sensors and analyzed to identify their health status. The information is stored in a local database and transmitted to a central supervisory station, which is able to respond to emergency situations. This prototype is part of a Remote Assisted Living Health Care System project, which integrates various key aspects regarding the telemonitoring of patients at home. Resumo. Aplicacoes pervasivas de assistencia domiciliar a saude exigem uma infraestrutura especializada de hardware e software capaz de coletar e processar dados do paciente e do ambiente. Apresentamos neste artigo um prototipo de monitoramento remoto em ambiente domiciliar, cujo paciente, seguindo prescricoes definidas num plano de cuidados, tem seus dados (fisiologicos e atividade) coletados por sensores e analisados para a identificacao da sua situacao de saude. As informacoes sao armazenadas localmente e transmitidas para uma central de supervisao capaz de atender a situacoes de emergencia. Este prototipo e parte do projeto de um Sistema de Assistencia Domiciliar a Saude, o qual integra diversos aspectos relevantes ao telemonitoramento do paciente em sua casa.

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Alessandro Copetti

Papers

Intelligent context-aware monitoring of hypertensive patients

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

Sistema de computação ubíqua na assistência domiciliar à saúde

Applying context-aware techniques to design remote assisted living applications

Monitoramento Remoto de Pacientes em Ambiente Domiciliar