Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Nowadays, it has become convenient to record data related to an individual using a wearable device. However, it is difficult to utilize the data according to the individual, especially to anomaly detection. Anomaly detection is very important for healthcare, e.g., early detecting of illness. In our previous study, we proposed an approach to specifying latent factors using Structural Equation Modeling (SEM). In this paper, we propose an improved approach for anomaly detection taking account of personal status based on latent factors. To estimate the states, we adopt Hidden Markov Model (HMM). Moreover, we use Hotelling's theory to detect abnormal data statistically. By using our approach, even if states can not be explicitly obtained from a device, hidden states can be estimated to perform anomaly detection in more details.

    Original languageEnglish
    Title of host publicationProceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages137-141
    Number of pages5
    ISBN (Electronic)9781538677438
    DOIs
    Publication statusPublished - 2018 Dec 26
    Event9th International Conference on Information Technology in Medicine and Education, ITME 2018 - Hangzhou, Zhejiang, China
    Duration: 2018 Oct 192018 Oct 21

    Publication series

    NameProceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018

    Conference

    Conference9th International Conference on Information Technology in Medicine and Education, ITME 2018
    CountryChina
    CityHangzhou, Zhejiang
    Period18/10/1918/10/21

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    Keywords

    • Anomaly detection
    • Health data
    • Hidden Markov Model (HMM)
    • Structural Equation Modeling (SEM)

    ASJC Scopus subject areas

    • Computer Science Applications
    • Medicine (miscellaneous)
    • Information Systems
    • Health Informatics
    • Education

    Cite this

    Tago, K., & Jin, Q. (2018). Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM. In Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018 (pp. 137-141). [8589272] (Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITME.2018.00040