Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2020

Abstract

Nocturia, or the need to void (or urinate) one or more times in the middle of night time sleeping, represents a significant economic burden for individuals and healthcare systems. Although it can be diagnosed in the hospital, most people tend to regard nocturia as a usual event, resulting in underreported diagnosis and treatment. Data from self-reporting via a voiding diary may be irregular and subjective especially among the elderly due to memory problems. This study aims to detect the presence of nocturia through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental health of community-dwelling elderly living alone. With continuous and objective data from motion sensors installed in each zone of the apartment (bedroom, living room, kitchen, and bathroom) and a contact sensor on the main door from 39 elderly, we derive a sensor-based nocturia classification model, where nocturia labeling is done based on psychosocial survey data. Our evaluation of the model reveals that (i) the use of sensor-derived features (e.g., bedroom and living room occupancy and activity level as well as going out patterns) beyond nocturia events and (ii) the extraction and use of usual sleep location as a feature improves the classification performance, where perfect accuracy can be achieved with support vector machine. Further analysis on the survey findings also reveals that elderly with nocturia are more likely to have poor sleep quality, and suffer from conditions related to physical frailty. Our findings lend support to the efficacy of passive in-home monitoring as a digital biomarker for detection of nocturia and related conditions in live-alone elderly.

Keywords

elderly, IoT, nocturia, sensors, unobtrusive

Discipline

Gerontology | Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2020 IEEE International Conference on Big Data: Virtual conference, December 10-13: Proceedings

First Page

4929

Last Page

4937

ISBN

9781728162515

Identifier

10.1109/BigData50022.2020.9377949

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

5-6-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/BigData50022.2020.9377949

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