Unobtrusive Nighttime Movement Monitoring to Support Nursing Home Continence Care: Machine Learning Study (Preprint)

BACKGROUND 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. OBJECTIVE This study explores 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. METHODS Six participants followed a seven-step protocol. The obtained dataset was segmented into 20s windows with a 50% overlap. Each window was labelled with one of the four 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). RESULTS The trained model attained a trustworthy overall F1-score of 81.51% for all classes, and, more specifically, an F1-score of 83.87% for the class 'Agitation'. CONCLUSIONS 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, via 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 AI-supported healthcare for older adults. CLINICALTRIAL Ethical approval to conduct the research was obtained from the KU Leuven Social and Societal Ethics Committee with protocol number G-2020-2214.