A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home

Liouane, Zaineb; Lemlouma, Tayeb; Roose, Philippe; Weis, Frédéric; Messaoud, Hassani

doi:10.1007/978-3-319-53480-0_73

Cited by 5 publications

(6 citation statements)

References 15 publications

(15 reference statements)

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“…Many of the studies use freely available datasets from research universities such as MIT or Washington State. These are Mavhome 8 , MavLab 9 , CASAS 10 , LIARA 11 , REDD 12 , and PlaceLab 13 .…”

Section: Datasets and Input Data Typesmentioning

confidence: 99%

Computational methods for predicting human behaviour in smart environments

Dunne

Matthews

Vega

et al. 2023

AIS

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This systematic literature review presents the computational methods of human behaviour prediction research from Pentland and Liu’s seminal 1999 paper on human behaviour prediction to the latest research to date. The PRISMA framework for systematic reviews was used as the review methodology to structure this information aggregation. This review provides a high-level summary of the field with key areas identified for new research. The results show that there are frequently used datasets for training predictive models: MavHome, MavLab, LIARA, CASAS, PlaceLab, and REDD. Accuracies in the range of 43.9% to 100% for predictions of varying complexity. Common data structures for modelling behavioural data: Vectors, tables, trees, Markov models, and graphs. Algorithms that fall into three distinct categories: Machine Learning (NN, RL, LSTM), Probabilistic Graphical Models (namely Bayesian and Markov variants), and Statistical and Trend Analysis (ARIMA, Prophet). Additionally, we document other notably useful algorithms that fall outside of these three main categories including Jaro-Winkler and Levenshtein distances. Opportunities identified for further research include the use of audio as the data source for behaviour prediction methods, and applying times-series prediction machine learning algorithms (RNN, LSTM) to the smart home problem space.

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Section: Datasets and Input Data Typesmentioning

confidence: 99%

Computational methods for predicting human behaviour in smart environments

Dunne

Matthews

Vega

et al. 2023

AIS

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“…Using machine learning and artificial intelligence methods from sensor data can track and detect changes in individuals' behavioral pattern and lifestyle [20]. By adopting an unsupervised clustering algorithm, recurrent output neural network model, and genetic algorithm, AI systems can constantly monitor the elderly in smart homes and send an alert to the caregiver if any abnormal activities occur [21,22]. To achieve the goal of helping adults with cognitive impairments independently accomplish the activities of daily life, intelligent assistant agents need to recognize older adults' goals and reasons behind the further steps desired [23].…”

Section: Second-round Selection Of Literaturementioning

confidence: 99%

Review on the Application of Artificial Intelligence in Smart Homes

et al. 2019

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Smart home and artificial intelligence technologies are developing rapidly, and various smart home products associated with artificial intelligence (AI) improved the quality of living for occupants. Although some studies discussed the application of artificial intelligence in smart homes, few publications fully considered the integration of literature and products. In this paper, we aim to answer the research questions of “what is the trend of smart home technology and products” and “what is the relationship between literature and products in smart homes with AI”. Literature reviews and product reviews are given to define the functions and roles of artificial intelligence in smart homes. We determined the application status of artificial intelligence in smart home products and how it is utilized in our house so that we could understand how artificial intelligence is used to make smart homes. Furthermore, our results revealed that there is a delay between literature and products, and smart home intelligent interactions will become more and more popular.

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“…Hidden Markov-based models) (Yu et al 2018;Ronao and Cho 2017;Wu et al 2016), computational intelligence techniques (e.g. Neural networks) (Hussein et al 2014;Liu et al 2015;Liouane et al 2016c;Liouane et al 2016a), and also reasoning techniques such as fuzzy logic reasoning (Yuan and Herbert 2014;Das et al 2013).…”

Section: Introductionmentioning

confidence: 99%

An intelligent knowledge system for designing, modeling, and recognizing the behavior of elderly people in smart space

Liouane

Lemlouma

Roose

et al. 2020

J Ambient Intell Human Comput

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In this paper, a context-sensitive descriptive language is proposed to design and model the daily living activities of elderly people. The objective is to simplify and represent correctly the knowledge collected by sensors (low level) and to have a relevant recognition of the person's knowledge (high level). The proposed language is based on several rules and constraints through intelligent meaning. It is dedicated to a better understanding and semantic design and description of the behavior of elderly people. Subsequently, in order to provide a powerful knowledge recognition system, a hybrid Markov model is proposed to recognize and predict the activities designed by the proposed language. The proposed model is adapted to the reasoning of the new language. This allows providing a hierarchical and temporal relationship within the knowledge. It is responsible to recognize and predict the behavior of the elderly people e ciently. The flexibility and the intelligibility of the proposed language is proven and the accuracy of the recognition model is demonstrated which ensures the e ciency of the proposed knowledge recognition system.

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A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home

Cited by 5 publications

References 15 publications

Computational methods for predicting human behaviour in smart environments

Computational methods for predicting human behaviour in smart environments

Review on the Application of Artificial Intelligence in Smart Homes

An intelligent knowledge system for designing, modeling, and recognizing the behavior of elderly people in smart space

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