FAAL: Fog computing-based patient monitoring system for ambient assisted living

Vora, Jayneel; Tanwar, Sudeep; Tyagi, Sudhanshu; Kumar, Neeraj; Rodrigues, Joel J. P. C.

doi:10.1109/healthcom.2017.8210825

Cited by 74 publications

(24 citation statements)

References 9 publications

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“…The proposed solution integrates softwarebased hardware equipment and enables rapid and reliable data transmission. In [18], a fog-based framework of health surveillance, FAAL, is introduced for patients with chronic neurological disease. Patients' movement data is gathered and transferred to the fog layer where a clustering algorithm is employed to reduce the load on the fog nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Mukherjee et al [20] proposed a three-tier mobility-aware Internet of Health Things (IoHT) framework, which consists of sensor nodes, fog nodes for parametric health control, and a cloud server for processing in the case of abnormal health status. In [17,18,19,20], researchers validated their proposed fog computing-based architectures of health monitoring systems by comparing only against the cloud computing-based implementation. Instead, we compared our proposed fog-enabled health monitoring system with two other fog-based implementations i.e.…”

Section: Related Workmentioning

confidence: 99%

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Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

et al. 2021

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

et al. 2021

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“…The sensed data are continuously transmitted to the fog devices till subject is identified. Jayneel et al [22] proposed similar technique of patient monitoring using fog computational devices for assisting patients in their home environments. The results obtained showed better performance in terms of latency and data overloading for fog computing-based environment instead of non-fog computing-based environment.…”

Section: Phase 1: Rnn-based Alzheimer Predictionmentioning

confidence: 99%

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma

Dudeja

Aujla

et al. 2020

Neural Comput & Applic

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Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

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“…Diferentes fontes de dados são combinadas em cada nível para produzir resultados oportunos e precisos. Em [Vora et al 2017]é proposto um sistema de monitoramento de pacientes baseado em computação em nevoa. Os dados de movimento dos pacientes são coletados usando sensores vestíveis e são transmitidos usando os gateways da nevoa.…”

Section: Trabalhos Relacionadosunclassified

Fusão Hierárquica de Dados para Sistemas de Computação em Névoa

Bezerra¹,

Cordeiro²,

Rocha³

et al. 2020

Anais XXXVIII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2020)

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O paradigma da computação em névoa possibilita que os recursos computacionais sejam levados para a borda da rede, de forma que a latência e a largura de banda resultantes da computação em nuvem possam ser reduzidas. A combinação desse paradigma com técnicas de fusão de dados e processamento de eventos, a partir da aquisição de dados brutos, tem grande potencial de uso para gerar conhecimento de valor e ajudar a tomada de decisão em sistemas de IoT. Nesse contexto, foi proposta uma arquitetura de fusão de dados em três níveis (nó sensor, névoa e nuvem) que, aliada ao uso de técnicas de processamento de eventos complexos, promove a tomada de decisões com rapidez, enquanto reduz o custo de transmissão de dados na rede. Os experimentos mostram uma redução de 77% no tempo médio de envio de mensagens na rede e 82% de melhoria na vazão média com a utilização da arquitetura proposta.

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FAAL: Fog computing-based patient monitoring system for ambient assisted living

Cited by 74 publications

References 9 publications

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Fusão Hierárquica de Dados para Sistemas de Computação em Névoa

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