Strauven Hannelore, Wang Chunzhuo, Hallez Hans, Vanden Abeele Vero, Vanrumste Bart
e-Media Research Lab/STADIUS, Department of Electrical Engineering, KU Leuven, Andreas Vesaliusstraat 13, Leuven, 3000, Belgium, +32 16377662.
Research Group M-Group/DistriNet, Department of Computer Science, KU Leuven, Brugge, Belgium.
JMIR Nurs. 2024 Dec 24;7:e58094. doi: 10.2196/58094.
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.
老年人尿失禁（UI）的患病率不断上升，尤其是那些住在养老院（NHs）的老年人，这凸显了对创新型失禁护理解决方案的需求。实施一种不显眼的传感器系统可能有助于夜间监测NH居民的活动，更具体地说，监测可能与排尿事件相关的躁动。
本研究旨在探索一种不显眼的传感器系统在夜间活动监测中的应用，该系统集成到一张护理床上，加速度计传感器连接到压力再分布护理床垫上。
共有6名参与者遵循了一个7步方案。将获得的数据集分割成20秒的窗口，重叠率为50%。每个窗口用4个选定活动类别中的1个进行标记：在床上、躁动、翻身和下床。总共选择了1416个特征，并使用XGBoost算法进行分析。最后，使用留一受试者交叉验证（LOSOCV）对模型进行验证。
训练后的模型对所有类别获得了可信的总体F1分数79.56%，更具体地说，“躁动”类别的F1分数为79.67%。
本研究结果为不显眼的夜间活动监测提供了有前景的见解。该研究强调了通过基于连接到粘弹性护理床垫的加速度计数据的机器学习模型提高NH居民护理质量的潜力，从而推动失禁护理和老年人人工智能支持医疗保健领域的进展。
