Real-time human behaviour monitoring using hybrid ambient assisted living framework

Patel, Ashish; Shah, Jigarkumar

doi:10.1007/s40860-020-00100-7

Cited by 16 publications

(7 citation statements)

References 34 publications

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“…Those consider both the environment and its users processing real-time information to offer tailored services. The growing interest in the AAL domain has enabled the development of state-of-the-art CB systems for humancentered home environments (De Paola, et al, 2017) (Rafferty, et al, 2017) (Calderita, et al, 2020) (Patel & Shah, 2020). Those are able to learn at scale, reason with purpose and co-operate with users in a natural way.…”

Section: Digital Twin In Ambient Assisted Livingmentioning

confidence: 99%

Development of digital twin models supporting ambient assisted living

Leonardo¹,

Corneli²,

Vaccarini³

et al. 2022

Proceedings of the 2022 European Conference on Computing in Construction

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World population aging requires finding solutions to improve independent living options. Ambient Assisted Living (AAL) is making step forward developing services supporting the elderly, but the implementation of predictive environments is still far away. Besides, the emerging Digital Twin (DT) concept has begun to shape the first cognitive environments that integrate users into assessments, improving efficiency, prevention, and prediction of likely events through realtime AI computing. This paper provides a prototype of a Cognitive Building framework based on DT models that develop high-level knowledge to achieve real-time Scenario Awareness and offer appropriate AAL services once anomalous scenarios are detected.

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Section: Digital Twin In Ambient Assisted Livingmentioning

confidence: 99%

Development of digital twin models supporting ambient assisted living

Leonardo¹,

Corneli²,

Vaccarini³

et al. 2022

Proceedings of the 2022 European Conference on Computing in Construction

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“…In this section we describe various methods that have been proposed by researchers over the years to predict human behaviour parameters for different kinds of scenarios. The work in [14] proposed by Peter G.…”

Section: Analysis Of Parameters For Human Behavioural Predictionmentioning

confidence: 99%

Human Behavior Prediction and Analysis Using Machine Learning-A Review

Gulhane¹

2021

TURCOMAT

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Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.

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“…It can play a major role by its ability to perform data processing which preventing network congestion and reducing the amount of transmitted data. Most of the recent studies [7][8][9][10][11][12][13][14][15][16][17] in remote healthcare systems focus on using body sensors to monitor the patient status and provide the assistance to them. These sensors can be positioned on or embedded inside the body to measure certain signs of the patient.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Sun et al [16] developed a video-based method for automated detection of the infants' discomfort. Chaichulee et al [17] proposed an approach for detecting the presence of the patient and extracting cardio-respiratory signals from the patient skin with no clinical intervention using a video camera. A hybrid framework, in human behavior modeling in ambient assisted living systems, is proposed by Patel and Shah [18].…”

Section: B Camera-based Systems In Healthcarementioning

confidence: 99%

Fog-based Remote in-Home Health Monitoring Framework

Elgendy¹,

Sarhan²,

Alshewimy³

2021

IJACSA

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As a result of what happened to the world during the past and current year of the spread of the Covid-19 epidemic, it was necessary to have a reliable health care system for remote observation, especially in care homes for the elderly. There are many research works have been done in this field, but still have limitations in terms of latency, security, response delay, and long execution times. To remove these limitations, this paper introduces a Smart Healthcare Framework called Remote in-Home Health Monitoring (RHHM), which provides architecture and functionalities in order to facilitate the control of patients' conditions when they are at home. The framework exploits the benefits of fog layers with high-level services such as local storage, local real-time data processing, and embedded data mining for taking responsibility for handling some burdens of the sensor network and the cloud and to become a decision maker. In addition to, it incorporates camera with body sensors in diagnosis for more reliability and efficiency with privacy preserving. The performance of the proposed framework was evaluated using the popular iFogSim toolkit. The results show the proposed system's ability to reduce latency, energy consumption, network communications, and overall response time. The efforts of this work will help support the overall goal to establish a high performance, secured and reliable smart Healthcare system.

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Real-time human behaviour monitoring using hybrid ambient assisted living framework

Cited by 16 publications

References 34 publications

Development of digital twin models supporting ambient assisted living

Development of digital twin models supporting ambient assisted living

Human Behavior Prediction and Analysis Using Machine Learning-A Review

Fog-based Remote in-Home Health Monitoring Framework

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