A semantics-based approach to sensor data segmentation in real-time Activity Recognition

Triboan, Darpan; Chen, Liming; Chen, Feng; Wang, Zumin

doi:10.1016/j.future.2018.09.055

Cited by 45 publications

(22 citation statements)

References 39 publications

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“…A non-intrusive, heterogeneous ambient and embedded object-based sensing approach is also proposed for the microserver architecture. Darpan Triboan et al provide semiotic theory based on the ontological model, capturing generic information and residential expectations for the performance of ADLs to help the segmentation process [16]. The comprehensive study of fusion data/sensors and multiple HAR classification systems with focus on mobile and wearable devices is given by Nweke et al [17] (2019).…”

Section: Existing Surveysmentioning

confidence: 99%

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Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Kumar¹

2021

Preprint

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Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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Section: Existing Surveysmentioning

confidence: 99%

“…However, in the mostly mobile scenarios of recognition of human activity, it is often very difficult to obtain data annotations like this. This also results in a degree of ambiguity on labels.This area can be explored more extensively [16].…”

Section: Conclusion and Future Directionmentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Kumar¹

2021

Preprint

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“…It not only maps sensor streams but also captures structure, preserving the associations within the sensor state instances using a data-driven approach. A structure-preserving transformation encompasses each sensor object, their associations, and subsumptions relating to different concurrent activities [27]. These preserved semantics and associations are separated by understanding the complex activity structures.…”

Section: Semantic Data Expansionmentioning

confidence: 99%

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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“…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. Therefore, there are several proposals focused on combining different approaches to improve the efficiency of traditional classification algorithms with the support of semantic models and also dealing with checking timing requirements in the HAR area.…”

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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A semantics-based approach to sensor data segmentation in real-time Activity Recognition

Cited by 45 publications

References 39 publications

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

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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