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Composite Activity Recognition

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Human Activity Recognition and Behaviour Analysis

Abstract

Activity recognition is essential in providing activity assistance for users in smart homes. While significant progress has been made for single-user single-activity recognition, it still remains a challenge to carry out real-time progressive composite activity recognition. This Chapter introduces a hybrid approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach with temporal modelling and reasoning methods. It combines and describes in details ontological activity modelling which establishes relationships between activities and their involved entities, and temporal activity modelling which defines relationships between constituent activities of a composite activity, thus providing powerful representation capabilities for composite activity modelling. The Chapter describes an integrated architecture for composite activity recognition, and elaborates a unified activity recognition algorithm for the recognition of simple and composite activities. As an essential part of the model, the Chapter also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. An example case study has been undertaken using a number of experiments to evaluate and demonstrate the proposed approach in a feature-rich multi-agent prototype system.

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Authors and Affiliations

  1. School of Computer Science and Informatics, De Montfort University, Leicester, UK

    Liming Chen

  2. School of Computing, Ulster University, Belfast, UK

    Chris D. Nugent

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  1. Liming Chen
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  2. Chris D. Nugent
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Correspondence to Liming Chen .

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Chen, L., Nugent, C.D. (2019). Composite Activity Recognition. In: Human Activity Recognition and Behaviour Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-19408-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-19408-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19407-9

  • Online ISBN: 978-3-030-19408-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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