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

Lee-Nam Kwon, Dong-Hun Yang, Myung-Gwon Hwang, Soo-Jin Lim, Young-Kuk Kim, Jae-Gyum Kim, Kwang-Hee Cho, Hong-Woo Chun and Kun-Woo Park
Additional contact information
Lee-Nam Kwon: Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
Dong-Hun Yang: Department of Data and HPC Science, University of Science and Technology, Daejeon 34113, Korea
Myung-Gwon Hwang: Department of Data and HPC Science, University of Science and Technology, Daejeon 34113, Korea
Soo-Jin Lim: Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, 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
Kun-Woo Park: Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea

IJERPH, 2021, vol. 18, issue 24, 1-24

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.

Keywords: activities of daily living; aging population; early-stage dementia; instrumental ADL; machine learning; personalization (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/24/13235/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/24/13235/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:24:p:13235-:d:703253

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().