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

• Published in: International journal of environmental research and public health Vol. 18; no. 24; p. 13235
• Main Authors: Kwon, Lee-Nam, Yang, Dong-Hun, Hwang, Myung-Gwon, Lim, Soo-Jin, Kim, Young-Kuk, Kim, Jae-Gyum, Cho, Kwang-Hee, Chun, Hong-Woo, Park, Kun-Woo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.12.2021; MDPI
• Subjects: Activities of Daily Living; Aged; Aging; Alzheimer's disease; Automation; Classification; Cognition; Cognitive ability; Customization; Data acquisition; Dementia; Dementia - diagnosis; Dementia - epidemiology; Dementia disorders; Diagnosis; Geriatrics; Home Environment; Households; Humans; Laboratories; Learning algorithms; Machine learning; Neuropsychology; Older people; Patients; Population; Smart buildings; Smart sensors; Statistical methods; United States > US; South Korea; personalization; machine learning; aging population; early-stage dementia; instrumental ADL; activities of daily living
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph182413235

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.