Dempster–Shafer theory-based human activity recognition in smart home environments

Sebbak, Faouzi; Benhammadi, Farid; Chibani, Abdelghani; Amirat, Yacine; Mokhtari, A.

doi:10.1007/s12243-013-0407-2

Cited by 20 publications

(13 citation statements)

References 33 publications

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“…Analyzing the papers in Table S3, it can be observed that, according to the authors of these papers, all of the studies focused on smart homes. The authors of these scientific articles made use in their analyses of different types of sensors, including: biomedical sensors [11]; ambient data sensors [11,34,68]; acoustic sensor networks [67]; WiFi-enabled sensors [36]; Passive Infrared (PIR) sensors [30,34]; binary sensors [31,69]; and motion sensors [30,70].…”

Section: Classificationmentioning

confidence: 99%

“…With respect to the reasons for using the Naïve Bayes method with sensor equipment in smart buildings, one can observe that the recognition of human activity was the main subject of the identified papers summarized in Table S3, being addressed in papers [11,30,31,34,[68][69][70]. Meanwhile, several of the above-mentioned scientific papers that use the Naïve Bayes integrated with sensor devices in Smart Buildings also addressed issues regarding assisted living [11,30,31,34,36].…”

Section: Classificationmentioning

confidence: 99%

“…The authors of [31] made use of the Naïve Bayes (NB), Support Vector Machine (SVM), Evidence-Theoretic K-Nearest Neighbor (ET-KNN), Probabilistic Neural Network (PNN), and K-Nearest Neighbor (KNN) methods. In [69], the Dempster-Shafer theory was implemented, and was subsequently compared with the Naïve Bayes classifier and J48 Decision Tree. In [70], the authors applied a hybrid approach based on the Naïve Bayes classifier, Hidden Markov Model and Viterbi algorithm.…”

Section: Classificationmentioning

confidence: 99%

“…The performance metrics that were chosen by the authors of the scientific papers that use the Naïve Bayes method integrated with sensor devices in smart buildings include: Accuracy [11,31,34,36,68,70]; Precision [11,30,69]; Recall [11,69]; F-measure [11,30,69]; Mean Value and Standard Deviation [67]; Accuracy, True Positive Rate, True Negative Rate with a view to assessing the performance in detecting the occupancy, along with the Mean Absolute Error and the Root Mean Square Error, for establishing the number of occupants [68]; and Error Rate [34].…”

Section: Classificationmentioning

confidence: 99%

“…In these papers, the reasons for using the Decision Tree integrated with sensor devices in smart buildings were mainly related to human activity recognition [1,5,14,17,19,21,30,50,53,68,69,74,[78][79][80][81][82][83][84]. In some of these papers, human activity recognition was just a first step, subsequently focusing on: analyzing and improving the energy prediction performance [1,80]; analyzing and ensuring the thermal comfort of the occupants [50,53]; forecasting energy consumption [21]; estimating the number of occupants [78]; identifying behavioral patterns [79]; detecting deviating human behavior [82]; monitoring the activities of elderly people living alone [14]; classifying the gender of occupants [5]; and improving home-based assisted living [30].…”

Section: Regressionmentioning

confidence: 99%

See 4 more Smart Citations

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

See 3 more Smart Citations

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Evidence theory‐based framework for improving automation in home automation system

Chemoun

Gilg

Laghrouche

et al. 2018

Int J Communication

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Summary An easy and better way of life is the topic of the day and stays an important theme for the future. New researches focusing on this aspect are increasingly emerging. In recent years, improving automation in home automation systems and making homes even smarter seem to be a major issue. Automation of daily life functioning of home devices makes life easier for inhabitants, and then saves them from manual adjustments. Home automation systems encompass different communication and networking technologies. They are embedded with sensor networks that perceive information about the environment and occupants. The collected data can mainly be processed to infer sequential patterns defining the home behavior. However, the imperfection, particularly uncertainty of sensor readings, is a significant factor to consider. Also, time is an important aspect when reasoning about home behavior, as each pattern (scenario) performs at a given time throughout the day. Evidence theory is recently gaining great attention. It is suitable for dealing with imperfection of sensor data and for including temporal features into the reasoning process to discover the appropriate pattern to launch. This paper presents a new framework based on evidence theory with the inclusion of time features, for improving automation in home automation systems. The effectiveness of the proposed approach is evaluated by conducting experiments on synthetic dataset. The results show the efficiency of the discovery process when multiple working sensors are available. The results demonstrate also that reliability and accuracy of the process improve significantly when temporal information is provided.

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An Evidential Fusion Rule for Ambient Intelligence for Activity Recognition

Sebbak

Benhammadi

Bouznad

et al. 2014

Belief Functions: Theory and Applications

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Dempster–Shafer theory-based human activity recognition in smart home environments

Cited by 20 publications

References 33 publications

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Evidence theory‐based framework for improving automation in home automation system

An Evidential Fusion Rule for Ambient Intelligence for Activity Recognition

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