Ontology-driven semantic unified modelling for concurrent activity recognition (OSCAR)

Safyan, Muhammad; Qayyum, Zia Ul; Sarwar, Sohail; García‐Castro, Raúl; Ahmed, Mehtab

doi:10.1007/s11042-018-6318-5

Cited by 17 publications

(16 citation statements)

References 33 publications

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“…Compared with rest of the approaches, ontology‐based models provide higher degree of automation, better reasoning ability, and solid technological foundations but still lacking the self‐evolution. Here, we extend our work described in [7] for ontology evolution. Proposed ontological model for AR adopts hybrid activity modelling approach (knowledge‐driven and data‐driven) in which seed knowledge about activities is modelled in an ontology.…”

Section: Introductionmentioning

confidence: 94%

Ontology evolution for personalised and adaptive activity recognition

Safyan¹,

Qayyum

Sarwar

et al. 2019

IET wirel. sens. syst.

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Ontology-based knowledge driven Activity Recognition (AR) models play a vital role in realm of Internet of Things (IoTs). However, these models suffer the shortcomings of static nature, inability of self-evolution and lack of adaptivity. Also, AR models cannot be made comprehensive enough to cater all the activities and smart home inhabitants may not be restricted to only those activities contained in AR model. So, AR models may not rightly recognize or infer new activities. In this paper, a framework has been proposed for dynamically capturing the new knowledge from activity patterns to evolve behavioural changes in AR model (i.e. ontology based model). This ontology based framework adapts by learning the specialized and extended activities from existing user-performed activity patterns. Moreover, it can identify new activity patterns previously unknown in AR model, adapt the new properties in existing activity models and enrich ontology model by capturing change representation to enrich ontology model. The proposed framework has been evaluated comprehensively over the metrics of accuracy, statistical heuristics and Kappa Coefficient. A well-known dataset named DAMSH has been used for having an empirical insight to the effectiveness of proposed framework that shows a significant level of accuracy for AR models.

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Section: Introductionmentioning

confidence: 94%

Ontology evolution for personalised and adaptive activity recognition

Safyan¹,

Qayyum

Sarwar

et al. 2019

IET wirel. sens. syst.

Self Cite

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“…Similarly, the other obtrusive sensor objects have the properties hasAccelerometer, hasBLESensor with the hasRSSI data property. All these sensor objects define the ADL with open intervals without any prior knowledge of Start-time or End-time [1]. The temporal relations for each sensor object are obtained using object properties hasStartTime and hasEndTime.…”

Section: Taxonomy Constructionmentioning

confidence: 99%

“…Over the past few decades, a rapid advancement has been observed in pervasive computing for the assessment of cognitive and physical well-being of older adults. For this purpose, monitoring of Activities of Daily Living (ADLs) is often performed over extended periods of time [1]. This is generally carried out in intelligent environments containing various pervasive computing and sensing solutions.…”

Section: Introductionmentioning

confidence: 99%

“…The key objectives being addressed in this study are to: (1) design and development of a practical scheme for modeling time-series data into an ontology, (2) perform semantic data expansion using the semantic properties, (3) identify suitable semantic data imputation measure, (4) design and train an effective deep learning model for Human Activity Recognition (HAR), and (5) undertake a comparative analysis using public datasets with each having different rates of missing data and imputation challenges.…”

Section: Introductionmentioning

confidence: 99%

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SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Razzaq

Cleland

Nugent

et al. 2020

Sensors

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The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.

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“…The approach of Salguero et al [19] was focused on a time-based pre-segmentation process that, dynamically, defined window sizes, and the Knowledge-based Collaborative Active Learning proposal [20] addressed the possibility of considering the segmentation of the flows of sensor events in real-time. OSCAR [21] is another hybrid framework of knowledge-driven techniques based on ontological constructs and temporal formalisms by means of segmentation processes, complemented with data-driven algorithms for the recognition of parallel and interleaved activities. The semantics-based approach to sensor data segmentation in real-time proposed by Triboan et al [22] is also a good example of combining several perspectives in activity recognition along with the proposal by Liu et al [23] about timely daily activity recognition from incomplete streams of sensor events.…”

Section: Related Workmentioning

confidence: 99%

Balancing Timing and Accuracy Requirements in Human Activity Recognition Mobile Applications

Jabla

Buendía

Khemaja

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Timing requirements are present in many current context-aware and ambient intelligent applications. These kinds of applications usually demand a timing response according to needs dealing with context changes and user interactions. The current work introduces an approach that combines knowledge-driven and data-driven methods to check these requirements in the area of human activity recognition. Such recognition is traditionally based on machine learning classification algorithms. Since these algorithms are highly time consuming, it is necessary to choose alternative approaches when timing requirements are tight. In this case, the main idea consists of taking advantage of semantic ontology models that allow maintaining a level of accuracy during the recognition process while achieving the required response times. The experiments performed and their results in terms of checking such timing requirements along with keeping acceptable recognition levels confirm this idea as shown in the final section of the work.

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Ontology-driven semantic unified modelling for concurrent activity recognition (OSCAR)

Cited by 17 publications

References 33 publications

Ontology evolution for personalised and adaptive activity recognition

Ontology evolution for personalised and adaptive activity recognition

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Balancing Timing and Accuracy Requirements in Human Activity Recognition Mobile Applications

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