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AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults

In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older...

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Autores principales: Nakaoku, Yuriko, Ogata, Soshiro, Murata, Shunsuke, Nishimori, Makoto, Ihara, Masafumi, Iihara, Koji, Takegami, Misa, Nishimura, Kunihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473035/
https://www.ncbi.nlm.nih.gov/pubmed/34577455
http://dx.doi.org/10.3390/s21186249
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author Nakaoku, Yuriko
Ogata, Soshiro
Murata, Shunsuke
Nishimori, Makoto
Ihara, Masafumi
Iihara, Koji
Takegami, Misa
Nishimura, Kunihiro
author_facet Nakaoku, Yuriko
Ogata, Soshiro
Murata, Shunsuke
Nishimori, Makoto
Ihara, Masafumi
Iihara, Koji
Takegami, Misa
Nishimura, Kunihiro
author_sort Nakaoku, Yuriko
collection PubMed
description In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were −13.5 min for induction heating in the spring, −1.80 min for microwave oven in the winter, and −0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment.
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spelling pubmed-84730352021-09-28 AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults Nakaoku, Yuriko Ogata, Soshiro Murata, Shunsuke Nishimori, Makoto Ihara, Masafumi Iihara, Koji Takegami, Misa Nishimura, Kunihiro Sensors (Basel) Article In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were −13.5 min for induction heating in the spring, −1.80 min for microwave oven in the winter, and −0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment. MDPI 2021-09-17 /pmc/articles/PMC8473035/ /pubmed/34577455 http://dx.doi.org/10.3390/s21186249 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nakaoku, Yuriko
Ogata, Soshiro
Murata, Shunsuke
Nishimori, Makoto
Ihara, Masafumi
Iihara, Koji
Takegami, Misa
Nishimura, Kunihiro
AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
title AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
title_full AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
title_fullStr AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
title_full_unstemmed AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
title_short AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
title_sort ai-assisted in-house power monitoring for the detection of cognitive impairment in older adults
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473035/
https://www.ncbi.nlm.nih.gov/pubmed/34577455
http://dx.doi.org/10.3390/s21186249
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