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MMOU-AR: Multimodal Obtrusive and Unobtrusive Activity Recognition Through Supervised Ontology-Based Reasoning

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Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019 (IMCOM 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 935))

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Abstract

The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major healthcare challenges. To address these unmet healthcare challenges, monitoring and Activity Recognition (AR) are considered as a subtask in pervasive computing and context-aware systems. Innumerable interdisciplinary applications exist, underpinning the obtrusive sensory data using the revolutionary digital technologies for the acquisition, transformation, and fusion of recognized activities. However, little importance is given by the research community to make the use of non-wearables i.e. unobtrusive sensing technologies. The physical state of human pervasively in daily living for AR can be seamlessly presented by acquiring health-related information by using unobtrusive sensing technologies to enable long-term health monitoring without violating an individual’s privacy. This paper aims to propose and provide supervised recognition of Activities of Daily Livings (ADLs) by observing unobtrusive sensor events using statistical reasoning. Furthermore, it also investigates their semantic correlations by defining semantic constraints with the support of ontological reasoning. Extensive experiments were performed with real-world dataset shared by the University of Jaén Ambient Intelligence (UJAmI) Smart Lab in order to recognize the human activities in the smart environment. The evaluations show that the accuracy of the supervised method (87%) is comparable to the one, state of the art semantic approach (91%).

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Acknowledgment

This research was supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00655). This work was also supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion) and NRF-2016K1A3A7A03951968.

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Authors and Affiliations

  1. Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, 446-701, South Korea

    Muhammad Asif Razzaq & Sungyoung Lee

Authors
  1. Muhammad Asif Razzaq
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  2. Sungyoung Lee
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Correspondence to Muhammad Asif Razzaq .

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Editors and Affiliations

  1. Intelligent Systems Research Institute, Sungkyunkwan University, Suwon, Korea (Republic of)

    Sukhan Lee

  2. Software Engineering, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia

    Roslan Ismail

  3. College of Software, Sungkyunkwan University, Suwon, Korea (Republic of)

    Hyunseung Choo

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Razzaq, M.A., Lee, S. (2019). MMOU-AR: Multimodal Obtrusive and Unobtrusive Activity Recognition Through Supervised Ontology-Based Reasoning. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_75

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