Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors

Diabetes, a chronic disease that occurs when the pancreas does not produce enough insulin or when the body cannot effectively utilize its insulin, is increasingly recognized as a significant health burden and affects many older adults. Poor sleep quality in diabetic seniors worsens the diabetes cond...

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Main Authors: NUQOBA, Barry, TAN, Hwee-pink
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/9823
https://ink.library.smu.edu.sg/context/sis_research/article/10823/viewcontent/Nuqoba_Tan2021_Chapter_PredictionOfSleepQualityInLive___Copy.pdf
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spelling sg-smu-ink.sis_research-108232024-12-24T03:39:27Z Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors NUQOBA, Barry TAN, Hwee-pink Diabetes, a chronic disease that occurs when the pancreas does not produce enough insulin or when the body cannot effectively utilize its insulin, is increasingly recognized as a significant health burden and affects many older adults. Poor sleep quality in diabetic seniors worsens the diabetes condition, but most seniors are tend to regard poor sleep quality as a usual event and do not seek treatment. This study aims to detect poor sleep quality in diabetic seniors through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental health of diabetic seniors. We derive sensor-based classification models using data from motion sensors installed in each apartment zone (bedroom, living room, kitchen, and bathroom) and a contact sensor on the main door from 39 seniors. Diabetes and poor sleep quality labeling are done based on psychosocial survey data. Our evaluation of the model reveals that (i) diabetes classification using features related to kitchen activity achieved perfect precision, (ii) sleep quality classification in diabetic seniors achieved the best results using Naïve Bayes and features related to night activity. Correlation analysis also reveals that seniors with diabetes are more likely to have poor sleep quality due to frequently voiding at night. Our findings can help community caregivers to monitor the sleep quality of diabetic seniors. 2021-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9823 info:doi/10.1007/978-3-030-78111-8_21 https://ink.library.smu.edu.sg/context/sis_research/article/10823/viewcontent/Nuqoba_Tan2021_Chapter_PredictionOfSleepQualityInLive___Copy.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Diabetes Sleep quality Sensors Databases and Information Systems Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Diabetes
Sleep quality
Sensors
Databases and Information Systems
Graphics and Human Computer Interfaces
spellingShingle Diabetes
Sleep quality
Sensors
Databases and Information Systems
Graphics and Human Computer Interfaces
NUQOBA, Barry
TAN, Hwee-pink
Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors
description Diabetes, a chronic disease that occurs when the pancreas does not produce enough insulin or when the body cannot effectively utilize its insulin, is increasingly recognized as a significant health burden and affects many older adults. Poor sleep quality in diabetic seniors worsens the diabetes condition, but most seniors are tend to regard poor sleep quality as a usual event and do not seek treatment. This study aims to detect poor sleep quality in diabetic seniors through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental health of diabetic seniors. We derive sensor-based classification models using data from motion sensors installed in each apartment zone (bedroom, living room, kitchen, and bathroom) and a contact sensor on the main door from 39 seniors. Diabetes and poor sleep quality labeling are done based on psychosocial survey data. Our evaluation of the model reveals that (i) diabetes classification using features related to kitchen activity achieved perfect precision, (ii) sleep quality classification in diabetic seniors achieved the best results using Naïve Bayes and features related to night activity. Correlation analysis also reveals that seniors with diabetes are more likely to have poor sleep quality due to frequently voiding at night. Our findings can help community caregivers to monitor the sleep quality of diabetic seniors.
format text
author NUQOBA, Barry
TAN, Hwee-pink
author_facet NUQOBA, Barry
TAN, Hwee-pink
author_sort NUQOBA, Barry
title Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors
title_short Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors
title_full Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors
title_fullStr Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors
title_full_unstemmed Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors
title_sort prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/9823
https://ink.library.smu.edu.sg/context/sis_research/article/10823/viewcontent/Nuqoba_Tan2021_Chapter_PredictionOfSleepQualityInLive___Copy.pdf
_version_ 1821237239730929664