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

Hannelore Strauven1, Chunzhuo Wang1, Hans Hallez2

  • 1e-Media Research Lab/STADIUS, Department of Electrical Engineering, KU Leuven, Andreas Vesaliusstraat 13, Leuven, 3000, Belgium, +32 16377662.

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Summary
This summary is machine-generated.

This study shows an unobtrusive sensor system can monitor nighttime agitation in nursing home residents. This machine learning approach offers potential for improved continence care and AI-supported healthcare for older adults.

Keywords:
accelerometeragitationenuresisincontinencenursing homesensor technologyunobtrusive

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Urinary incontinence (UI) is increasingly prevalent in older adults, especially in nursing homes (NHs).
  • Innovative continence care solutions are needed to support NH residents.
  • Unobtrusive sensor systems may aid nighttime monitoring of resident movements and agitation linked to voiding.

Purpose of the Study:

  • To explore the use of an unobtrusive sensor system for monitoring nighttime movement in NH residents.
  • To integrate accelerometer sensors into a care bed and mattress system.
  • To analyze movement data for identifying specific activities related to continence care.

Main Methods:

  • A 7-step protocol was followed by 6 participants.
  • Data was segmented into 20-second windows with 50% overlap and labeled into four activity classes.
  • An XGBoost algorithm analyzed 1416 features, with validation using leave-one-subject-out cross-validation (LOSOCV).

Main Results:

  • The trained machine learning model achieved an overall F1-score of 79.56%.
  • The model specifically achieved an F1-score of 79.67% for detecting "Agitation."
  • This demonstrates reliable performance in classifying nighttime activities.

Conclusions:

  • Unobtrusive nighttime movement monitoring shows promising potential for enhancing NH resident care.
  • A machine learning model using accelerometer data from care mattresses can improve continence care.
  • This technology advances AI-supported healthcare for older adults.