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. Pervasive health care, context-awareness, home care, decision making I.
Resumo:No Brasil, a Análise do Impacto Regulatório (AIR) vem se consolidando nas últimas décadas nas agências reguladoras federais. A metodologia AIR tem como objetivo examinar o processo regulatório, medir os custos e os benefícios gerados, assim como outros efeitos de natureza social, política ou econômica que podem ser causados por uma regulação nova ou já existente. Ao analisar cada opção regulatória, o especialista trata variáveis de natureza qualitativa de difícil mensuração e de elevado grau de incerteza. O trabalho complementa a literatura existente, dado que poucos trabalhos no âmbito da AIR têm empregado metodologias de apoio à decisão que incorporem o conhecimento do especialista de forma independente do problema a ser tratado. Portanto, propõe-se uma abordagem exploratória usando um Sistema Especialista Fuzzy (SEF), o qual, incorporando o conhecimento tácito dos especialistas, contribui para enriquecer o processo de decisão na fase final de comparação das opções regulatórias, permitindo o acesso do tomador de decisão a um histórico de parte dos raciocínios de outros especialistas. Palavras-chave:Análise de Impacto Regulatório. Lógica Fuzzy. Sistema Especialista Fuzzy. Opção regulatória.Abstract: Regulatory Impact Analysis (RIA) has been consolidating in Brazilian regulatory agencies throughout the last decades. The RIA methodology aims to examine the regulatory process, measure the costs and benefits generated, as well as other effects of social, political or economic nature caused by a new or an existing regulation. By analysing each regulatory option, the expert or regulator faces a myriad of variables, usually of qualitative nature, that are difficult to measure and with a high degree of uncertainty. This research complements the existing literature, given the scarcity of decision support models in RIA thatregardless of the problem treated -incorporate the tacit knowledge of the regulation expert. This paper proposes an exploratory approach using a Fuzzy Expert System, which therefore helps to enrich the decision process in the final stage of comparison of the regulatory options.
Este trabalho apresenta uma abordagem utilizando sistemas Fuzzy para o monitoramento de saúde de um paciente em ambientes de computação pervasiva. Um modelo de decisão considera três classes de variáveis que constituem as informações de contexto sendo coletadas: ambientais, fisiológicas e comportamentais. Um estudo de caso de monitoramento da pressão arterial foi desenvolvido para identificar situações críticas com base em conhecimento médico. A solução mantém a interpretabilidade de um conjunto de regras definidas, mesmo após uma fase de aprendizado que propõe ajustes nessas regras. Nessa fase, a técnica de agrupamento Fuzzy C-Means foi escolhida para o ajuste das funções de pertinência, usando os centros dos agrupamentos. Uma equipe médica avaliou dados de monitoramento de 24 horas de 30 pacientes e esta avaliação foi comparada com os resultados do sistema. A abordagem proposta demonstrou ser individualizada, identificando situações críticas em pacientes com diferentes níveis de pressão arterial, com uma acurácia de 90% e baixa taxa de falsos negativos.
In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.
No monitoramento em saúde pervasivo, uma aplicação essencial no acompanhamento do dia a dia da pessoa é o reconhecimento de atividades. Apesar dos diversos estudos sobre esse tema, um parâmetro pouco considerado é o reconhecimento da intensidade. Neste trabalho, propomos o acoplamento da atividade com a intensidade, a qual denominamos Atividade-Intensidade, em dados obtidos de acelerômetros, para melhor descrever as atividades diárias de um paciente. Adicionalmente, investigamos iniciativas de Lógica Fuzzy no reconhecimento de atividades. Os resultados mostraram a viabilidade da classificação e o bom desempenho do reconhecimento Atividade-Intensidade.
The introduction of the IPv6 protocol solved the problem of providingaddresses to network devices. With the emergence of the Internetof Things (IoT), there was also the need to develop a protocolthat would assist in connecting low-power devices. The 6LoWPANprotocols were created for this purpose. However, such protocolsinherited the vulnerabilities and threats related to Denial of Service(DoS) attacks from the IPv4 and IPv6 protocols. In this paper, weprepare a network environment for low-power IoT devices usingCOOJA simulator and Contiki operating system to analyze theenergy consumption of devices. Besides, we propose an IntrusionDetection System (IDS) associated with the AES symmetric encryptionalgorithm for the detection of reflection DoS attacks. Thesymmetric encryption has proven to be an appropriate methoddue to low implementation overhead, not incurring in large powerconsumption, and keeping a high level of system security. The maincontributions of this paper are: (i) implementation of a reflectionattack algorithm for IoT devices; (ii) implementation of an intrusiondetection system using AES encryption; (iii) comparison ofthe power consumption in three distinct scenarios: normal messageexchange, the occurrence of a reflection attack, and runningIDS algorithm. Finally, the results presented show that the IDSwith symmetric cryptography meets the security requirements andrespects the energy limits of low-power sensors.
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