Unobtrusive Nighttime Movement Monitoring to Support Nursing Home Continence Care: Algorithm Development and Validation Study

The rising prevalence of urinary incontinence (UI) among older adults, particularly those living in nursing homes (NHs), underscores the need for innovative continence care solutions. The implementation of an unobtrusive sensor system may support nighttime monitoring of NH residents' movements...

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Vydané v:JMIR nursing Ročník 7; s. e58094
Hlavní autori: Strauven, Hannelore, Wang, Chunzhuo, Hallez, Hans, Vanden Abeele, Vero, Vanrumste, Bart
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Canada JMIR Publications 24.12.2024
Predmet:
Accelerometry - methods
Aged
Aged, 80 and over
Aging in Place
AI in Older Adult Care
Algorithms
Assisted Living for the Elderly and Nursing Home Care
Female
Humans
Male
Monitoring, Physiologic - instrumentation
Monitoring, Physiologic - methods
Movement - physiology
Nursing Homes
Nursing in a Long-Term Care Facility / Nursing Home for the Elderly
Original Paper
Sensors at Home and Domotics
Textile Sensors and Smart Textiles
Urinary Incontinence - diagnosis
agitation
incontinence
unobtrusive
accelerometer
nursing home
enuresis
sensor technology
ISSN:2562-7600, 2562-7600
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Shrnutí:The rising prevalence of urinary incontinence (UI) among older adults, particularly those living in nursing homes (NHs), underscores the need for innovative continence care solutions. The implementation of an unobtrusive sensor system may support nighttime monitoring of NH residents' movements and, more specifically, the agitation possibly associated with voiding events. This study aims to explore the application of an unobtrusive sensor system to monitor nighttime movement, integrated into a care bed with accelerometer sensors connected to a pressure-redistributing care mattress. A total of 6 participants followed a 7-step protocol. The obtained dataset was segmented into 20-second windows with a 50% overlap. Each window was labeled with 1 of the 4 chosen activity classes: in bed, agitation, turn, and out of bed. A total of 1416 features were selected and analyzed with an XGBoost algorithm. At last, the model was validated using leave one subject out cross-validation (LOSOCV). The trained model attained a trustworthy overall F1-score of 79.56% for all classes and, more specifically, an F1-score of 79.67% for the class "Agitation." The results from this study provide promising insights in unobtrusive nighttime movement monitoring. The study underscores the potential to enhance the quality of care for NH residents through a machine learning model based on data from accelerometers connected to a viscoelastic care mattress, thereby driving progress in the field of continence care and artificial intelligence-supported health care for older adults.
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None declared.
ISSN:2562-7600
2562-7600
DOI:10.2196/58094