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 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. Methods: 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). Results: 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.” 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 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.

Incontinence is a very common condition among nursing home residents. This not only leads to physical and mental burden for the elderly residents, but also to an increased workload for care personnel. In order to lower this imposed workload, numerous technologies exist to support incontinence care. These systems, however, are expensive for the nursing homes and inconvenient for the residents. To solve these problems, we propose a new sensor system now attached to the bed that is able to monitor and detect incontinence events. Using Bluetooth Low Energy and the time-based database InfluxDB a prototype was built. The sensor data can be stored in real-time and can be made available to other systems for further processing. In future systems, security measures can be added, and energy measurements can be done to make the system fully portable.

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.