Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2021
Abstract
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.
Keywords
Diabetes, Sleep quality, Sensors
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Software and Cyber-Physical Systems
Publication
Human Aspects of IT for the Aged Population. Supporting Everyday Life Activities. Proceedings of 7th International Conference (ITAP 2021), held as part of the 23rd International Conference on Human-Computer Interaction (HCII 2021) Virtual Event, July 24–29
First Page
307
Last Page
321
Identifier
10.1007/978-3-030-78111-8_21
Publisher
Springer
City or Country
Switzerland
Citation
NUQOBA, Barry and TAN, Hwee-pink.
Prediction of sleep quality in live-alone diabetic seniors using unobtrusive in-home sensors. (2021). Human Aspects of IT for the Aged Population. Supporting Everyday Life Activities. Proceedings of 7th International Conference (ITAP 2021), held as part of the 23rd International Conference on Human-Computer Interaction (HCII 2021) Virtual Event, July 24–29. 307-321.
Available at: https://ink.library.smu.edu.sg/sis_research/9823
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-78111-8_21
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons
Comments
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