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Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment

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  • Lee-Nam Kwon

    (Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
    Future Information Research Center, Korea Institute of Science and Technology Information, Seoul 02456, Korea
    Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Dong-Hun Yang

    (Department of Data and HPC Science, University of Science and Technology, Daejeon 34113, Korea
    Artificial Intelligence Technology Research Center, Korea Institute of Science and Technology Information, Daejeon 34141, Korea)

  • Myung-Gwon Hwang

    (Department of Data and HPC Science, University of Science and Technology, Daejeon 34113, Korea
    Artificial Intelligence Technology Research Center, Korea Institute of Science and Technology Information, Daejeon 34141, Korea)

  • Soo-Jin Lim

    (Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
    Future Information Research Center, Korea Institute of Science and Technology Information, Seoul 02456, Korea)

  • Young-Kuk Kim

    (Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Jae-Gyum Kim

    (Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea)

  • Kwang-Hee Cho

    (Department of Biomedical Research Center, Korea University Anam Hospital, Seoul 02841, Korea)

  • Hong-Woo Chun

    (Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
    Future Information Research Center, Korea Institute of Science and Technology Information, Seoul 02456, Korea)

  • Kun-Woo Park

    (Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea)

Abstract

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.

Suggested Citation

  • Lee-Nam Kwon & Dong-Hun Yang & Myung-Gwon Hwang & Soo-Jin Lim & Young-Kuk Kim & Jae-Gyum Kim & Kwang-Hee Cho & Hong-Woo Chun & Kun-Woo Park, 2021. "Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment," IJERPH, MDPI, vol. 18(24), pages 1-24, December.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13235-:d:703253
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    References listed on IDEAS

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    Cited by:

    1. Yi-Kyung Hong & Ze-Yu Wang & Ji Young Cho, 2022. "Global Research Trends on Smart Homes for Older Adults: Bibliometric and Scientometric Analyses," IJERPH, MDPI, vol. 19(22), pages 1-24, November.

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