Probabilistic ontology based activity recognition in smart homes using Markov Logic Network

Gayathri, K.; Easwarakumar, K. S.; Elias, Susan

doi:10.1016/j.knosys.2017.01.025

Cited by 71 publications

(33 citation statements)

References 19 publications

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“…Analyzing the papers selected and summarized in Table S13, it can be observed that 78% of them exclusively analyze smart homes, 16% take into consideration smart buildings in general, 3% analyze both smart homes and buildings, while the remaining 3% of the selected papers refer to smart workplace environments. The authors of these scientific articles make use of different types of sensors in their analyses, including wireless sensor networks [70,115,[119][120][121][122][123][124]; passive infrared motion sensors [82,97,117,118,122,125,126]; motion sensors [25,70,81,118,120,127,128]; environmental sensors [10,25,81,82,[116][117][118]123,[127][128][129][130][131][132]; temperature sensors [116,118,120,123,125,[131][132][133]; humidity sensors…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…The performance metrics that chosen by the authors of the scientific papers using the Hidden Markov Model integrated with sensor devices in smart buildings included: Accuracy [3,10,25,33,70,115,117,120,122,123,125,127,131,133,136,138]; Precision [25,118,128,133,135,137]; Recall [25,118,128,135]; F-Measure [25,81,121,130,133]; Sensitivity and Specificity [25,33,133]; F1 Score [116,133]; Confusion Matrix [116,127,129]; and Correctness [97,118]. In addition to the above-mentioned performance metrics, other methods that were used to assess the performance of the developed methods by the authors of the scientific papers selected and summarized in Table S13 included: a numerical case study highlighting the efficiency of the developed model [134]; thread latency [119]; evaluation of energy savings…”

Section: Unsupervised Learningmentioning

confidence: 99%

See 1 more Smart Citation

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: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…Gayathri et al [19] use MLN to develop an ontology that can be used to recognize activities in smart homes. The purpose is to detect an abnormal activity (or a situation) and inform the remote caretaker.…”

Section: Previous Workmentioning

confidence: 99%

Situation awareness modeling for emergency management on offshore platforms

Danial

Smith

Khan

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionThe present work proposes a model based on Markov Logic Network (MLN) [16] for representing emergency situations involving smoke and fire on offshore petroleum platforms. The model is tested for two important situations, FIRE and EVACUATE. In the FIRE situation, fire is observed due to smoke at some place on the platform, and all workers need to muster to their primary muster station. In the EVACUATE situation, the fire is escalated so that some escape routes to the primary muster station are blocked and all personnel needs to muster at the lifeboat or alternative muster station. The purpose of this work is to have a model that can be used by a software agent so that the agent can exhibit human-like situation awareness (SA). Such agents can subsequently be used, for example, in training simulators to enrich trainees' experience by showing them various scenarios in which the agent shows recognition of different situations (to makes various decisions). A participant can learn from the agent what information is important in a given scenario for correct SA.Representing the emergency response of agents operating in a virtual environment (VE) is a challenging and active research area. Emergencies on board can arise from Abstract Situation awareness is the first and most important step in emergency management. It is a dynamic step involving evolving conditions and environments. It is an area of active research. This study presents a Markov Logic Network to model SA focusing on fire accidents and emergency evacuation. The model has been trained using empirical data obtained from case studies. The case studies involved human participants who were trained for responding to emergencies involving fire and smoke using a virtual environment. The simulated (queried) and empirical findings are reasonably consistent. The proposed model enables implementing an agent that exploits environmental cues and cognitive states to determine the type of emergency currently being faced. Considering each emergency type as a situation, the model can be used to develop a repertoire of situations for agents so that the repertoire can act as an agent's experience for later decision-making. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…William Brendel et al [Brendel, Fern and Todorovic (2011)] proposed a probabilistic event logic to address the following three problems in the above field: identifying each event, locating the time and location of events; interpreting time and space relationship and semantic constraints from the perspective of domain knowledge. Gayathri et al [Gayathri, Easwarakumar and Elias (2017)] used the ontology model to deal with issues such as action granularity, contextual knowledge, and activity diversity. And simultaneously Markov logic network is used to respond to problems like action diversity and data uncertainty by probabilistic reasoning of the represented domain ontology.…”

Section: Event Recognition Based On Logical Reasoningmentioning

confidence: 99%

Research on Action Recognition and Content Analysis in videos based on DNN and MLN

Song

Zhao

et al. 2019

Computers, Materials &Amp; Continua

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In the current era of multimedia information, it is increasingly urgent to realize intelligent video action recognition and content analysis. In the past few years, video action recognition, as an important direction in computer vision, has attracted many researchers and made much progress. First, this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network. Second, we analyze the characteristics of each method and the performance from the experiment results. Then compare the emphases of these methods and discuss the application scenarios. Finally, we consider and prospect the development trend and direction of this field.

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Probabilistic ontology based activity recognition in smart homes using Markov Logic Network

Cited by 71 publications

References 19 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

Situation awareness modeling for emergency management on offshore platforms

Research on Action Recognition and Content Analysis in videos based on DNN and MLN

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