A predictive approach for efficient e-Health monitoring

Mshali, Haider; Lemlouma, Tayeb; Magoni, Damien

doi:10.1109/healthcom.2015.7454510

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Moreover, using HMMs as a time series prediction model requires a large and growing number of time sequences. In this respect, other models, such as the Grey model [9,10], provide the prediction of future conditions with a minimum or even incomplete data. The work in [11] applies the Grey theory to the prediction of future physiological status including body temperature, blood pressure, and glucose level.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive monitoring system for e-health smart homes

Mshali

Lemlouma

Magoni

2018

Pervasive and Mobile Computing

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Existing e-health monitoring systems mainly operate in isolation from the requirements of modern healthcare institutions. They do not include optimized techniques which learn the patient's behavior for predicting future important changes. We propose a new context-aware e-health monitoring system targeted at the elderly and isolated persons living alone. It monitors daily living activities and evaluates dependency based on geriatric scales used by health professionals. Its adaptive framework collects only relevant contextual data for evaluating health status. By monitoring the achievement of daily activities, the system learns the behavior of the monitored person. It is then able to detect risky behavioral changes by using our novel forecasting approach based on the extension of the Grey model GM(1, 1). In order to evaluate our system, we use a Markovian model built for generating long term realistic scenarios. By simulation, we compare the performances of our system to traditional monitoring approaches with various synthetically generated scenarios and profiles. Results show that with minimal sensing and data collection, our system accurately evaluates a person's dependency, predicts its health condition, and detects abnormal situations while preserving system resources.

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Section: Related Workmentioning

confidence: 99%

“…Despite the unstable intraday behavior of persons and the uncertainty of the environment, data series including the person's history help to forecast future behaviors. To that end, we use the Grey theory [9,10]. We aim to learn the behavioral patterns with the minimum and most related data.…”

Section: Prediction Of Person's Behaviormentioning

confidence: 99%

Adaptive monitoring system for e-health smart homes

Mshali

Lemlouma

Magoni

2018

Pervasive and Mobile Computing

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“…Selected algorithms and methods may need to process a huge amount of data to learn and detect human's behaviour and context. Therefore, such methods can provide a good performance but at a high computational cost [140].…”

Section: Computational Intelligence Techniquesmentioning

confidence: 99%

A survey on health monitoring systems for health smart homes

Mshali

Lemlouma

Moloney

et al. 2018

International Journal of Industrial Ergonomics

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193

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Aging population ratios are rising significantly. Health monitoring systems (HMS) in smart environments have evolved rapidly to become a viable alternative to traditional healthcare solutions. The aim of HMS is to not only reduce costs but to also provide timely e-health services to individuals wishing to maintain their independence. In this way, elderly people can avoid, for as long as possible, any interaction with healthcare institutions (e.g. nursing homes and hospitals), which in turn reduces pressure on the health system. To fully realise this vision of seamless e-health services supporting people in need of them, a number of challenges that need further investigation still exist. To this end, we provide an overview of the current state of the art for smart health monitoring systems. We review HMS in smart environments from a general perspective and with a particular focus on systems for the elderly and dependent people. We look at the challenges for these systems from the perspective of developing the technology itself, system requirements, system design and modelling. We present a consolidated picture of the most important functions and services offered by HMS for monitoring and detecting human behaviour including its concepts, approaches, and processing techniques. Moreover, we provide an extensive, in-depth analysis and evaluation of the existing research findings in the area of e-health systems. Finally, we present challenges and open issues facing the smart HMS field and we make recommendations on how to improve future systems.

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“…Mshali et al [8] presented monitoring system that recognizing activity daily living (ADL), it using threshold based method and neural network. Mshali et al [9] presented study of context-aware computing in health monitoring system. For development the ontology, we must used many tools, for example description logic formalisms (TBox and ABox ) to analyze and define the vocabulary of cases domains [10], [11].…”

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confidence: 99%

Design a monitoring system for COVID-19 patients

Hendi

Mshali²

2022

IJEECS

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This paper <span lang="EN-US">considers to developing application software that can assist COVID-19 patients in-home quarantine to know their situations and call the emergence center when the patient needs it. It includes a smart band as well as an application on the smartphone, the smart band can determine blood oxygen levels, the temperature of the patient, environmental temperature and humidity, also daily activities that affect the decision to go to the hospital or stay at home. The core of the proposed project is using ontology and semantics web to process the data that coming from sensors (physiology and environment), and the information of patients stored in the database on the mobile application. The response depends on the dataset of affect sensors parameters and type of activity the patient at the time. There are three types of response to proposed program is (normal, alert, and emergency).</span>

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A predictive approach for efficient e-Health monitoring

Cited by 5 publications

References 18 publications

Adaptive monitoring system for e-health smart homes

Adaptive monitoring system for e-health smart homes

A survey on health monitoring systems for health smart homes

Design a monitoring system for COVID-19 patients

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