Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Jurek, Anna; Nugent, Chris; Bi, Yaxin; Wu, Shengli

doi:10.3390/s140712285

Cited by 40 publications

(24 citation statements)

References 29 publications

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“…Meanwhile, activity modelling is merely a representation of computational activity models in a computer interpretable format [17]. Data processing usually involves data segmentation and feature extraction while pattern recognition builds activity models based on generated smart home data [18]. Generally speaking, there are three main approaches used in the activity recognition, known as data-driven, knowledge-driven and hybrid approaches.…”

Section: Related Workmentioning

confidence: 99%

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Sukor

Zakaria

Rahim

et al. 2019

IFS

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Longzhi 2019. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes.Abstract. Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users' activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users' particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users' varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.

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Section: Related Workmentioning

confidence: 99%

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Sukor

Zakaria

Rahim

et al. 2019

IFS

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“…Analyzing the scientific articles summarized in Table S7, presented in the Supplementary Materials file, it can be observed that 40% of them analyze smart buildings in general, while the remaining 60% take smart homes into consideration. The authors of these scientific articles make use of different types of sensors in their analyses, including smartphone sensors [16,20]; accelerometers providing inertial information of human activity [16]; Light-Emitting Diode (LED) luminaires used as light sensors [3]; and sensors associated with different objects [85,86]. In all of the papers selected and summarized in Table S7, the reason for using the Ensemble Methods integrated with the sensor devices in smart buildings was the recognition of human activity.…”

Section: Regressionmentioning

confidence: 99%

“…In all of the papers selected and summarized in Table S7, the reason for using the Ensemble Methods integrated with the sensor devices in smart buildings was the recognition of human activity. The performance metrics chosen by the authors of the scientific papers that use Ensemble Methods integrated with sensor devices in smart buildings include Accuracy [3,16,20,86]; Recall [16,85,86]; Precision and F-measure [85,86]; Mean Squared Error (MSE) [3]; and Confusion Matrix presenting a number of true Positives, True Negatives, False Positives and False Negatives [85].…”

Section: Regressionmentioning

confidence: 99%

“…In article [85], Jurek et al aimed to recognize human activity in smart homes by proposing a cluster-based classifier ensemble method, using numeric and binary data collected by means of wireless sensors attached to different objects. After conducting the experimental tests and analyzing the results in terms of the Confusion Matrix presenting the number of True Positives, True Negatives, False Positives and False Negatives, Precision, Recall and F-Measure, the authors concluded that their proposed approach offered a higher level of performance than a range of state-of-the-art single clustering algorithms.…”

Section: Regressionmentioning

confidence: 99%

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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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“…Different techniques have been proposed in literature for smart homes [187]. Beside, vision-based techniques, a number of sensor-based techniques have also been presented in the literature [188]. In [189], a genetic programming-based classifier was proposed for activity recognition in a smart home environment.…”

Section: Ambient Assisted Livingmentioning

confidence: 99%

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

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Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

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Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Cited by 40 publications

References 29 publications

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

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 Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

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