Employing Grey Model forecasting GM(1,1) to historical medical sensor data towards system preventive in smart home e-health for elderly person

Jouini, Rim; Lemlouma, Tayeb; Maalaoui, Karima; Saı̈dane, Leı̈la Azouz

doi:10.1109/iwcmc.2016.7577210

Cited by 11 publications

(11 citation statements)

References 14 publications

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“…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. Results show that the Grey Model is more efficient with more accurate forecasting values than the conventional forecasting model called Box-Jenkins model [11].…”

Section: Related Workmentioning

confidence: 99%

“…The work in [11] applies the Grey theory to the prediction of future physiological status including body temperature, blood pressure, and glucose level. Results show that the Grey Model is more efficient with more accurate forecasting values than the conventional forecasting model called Box-Jenkins model [11]. Bayesian belief networks are also used in the recognition of activities in [12] and the monitoring and detection of daily activities in [13].…”

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%

Section: Related Workmentioning

confidence: 99%

Adaptive monitoring system for e-health smart homes

Mshali

Lemlouma

Magoni

2018

Pervasive and Mobile Computing

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“…Mao [37] used GM (1,1) in vehicle fatality risk estimation and got accurate prediction. Jouini et al [38] applied GM(1,1) model to forecast historical medical sensor data towards system preventive in smart home e-health for elderly person. Jiang et al [39] applied grey model to the operating energy performance of air cooled water chillers.…”

Section: Review Of Grey Modelmentioning

confidence: 99%

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Han

2019

Energies

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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

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“…Quantitative approaches may be also classified into causal relationship methods and time series ones. While the first category tries to make a relationship between many factors in order to generate the forecasted values, the second is limited to the statistical data that was observed and collected over regular time intervals such as hourly, daily, weekly, monthly, etc [13].…”

Section: Related Workmentioning

confidence: 99%

“…Thanks to its ability to estimate the possible data behavior based only on a few information samples even if they are incomplete, Grey theory becomes one of the most popular prediction approaches used in the research world [17]. The core and the most commonly used model of Grey is known as GM(1,1) [13]. The main task of this model is to identify the mathematical relationship between different points to learn about the behavior of the dataset and to make the right decision about the future trend [11].…”

Section: Related Workmentioning

confidence: 99%

Optimization of the upstream bandwidth allocation in passive optical networks using internet users' behavior forecast

Frigui

Lemlouma

Gosselin

et al. 2018

2018 International Conference on Optical Network Design and Modeling (ONDM)

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The application of classification techniques based on machine learning approaches to analyze the behavior of network users has interested many researchers in the last years. In a recent work, we have proposed an architecture for optimizing the upstream bandwidth allocation in Passive Optical Network (PON) based on the traffic pattern of each user. Clustering analysis was used in association with an assignment index calculation in order to specify for PON users their upstream data transmission tendency. A dynamic adjustment of Service Level Agreement (SLA) parameters is then performed to maximize the overall customers' satisfaction with the network. In this work, we extend the proposed architecture by adding a prediction module as a complementary to the first classification phase. Grey Model GM(1,1) is used in this context to learn more about the traffic trend of users and improve their assignment. An experimental study is conducted to show the impact of the forecaster and how it can overcome the limits of the initial model.

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Employing Grey Model forecasting GM(1,1) to historical medical sensor data towards system preventive in smart home e-health for elderly person

Cited by 11 publications

References 14 publications

Adaptive monitoring system for e-health smart homes

Adaptive monitoring system for e-health smart homes

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Optimization of the upstream bandwidth allocation in passive optical networks using internet users' behavior forecast

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