Paulauskaite-Taraseviciene Agne, Siaulys Julius, Sutiene Kristina, Petravicius Titas, Navickas Skirmantas, Oliandra Marius, Rapalis Andrius, Balciunas Justinas
Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania.
Department of Mathematical Modeling, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania.
Healthcare (Basel). 2023 Apr 17;11(8):1152. doi: 10.3390/healthcare11081152.
The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.
老年护理数字化是指利用新兴技术，通过电子方式收集患者数据，并利用这些数据简化护理流程，从而为老年人提供以患者为中心的护理，进而提高医疗保健的整体质量、准确性和效率。在许多国家，医疗服务提供者仍依赖手动测量生物参数、不一致的监测以及纸质护理计划来管理和为老年患者提供护理。这可能导致一系列问题，包括记录不完整和不准确、错误以及在识别和解决健康问题方面的延误。本研究的目的是开发一种老年护理管理系统，该系统结合来自各种可穿戴传感器、非接触测量设备和图像识别技术的信号，以监测和检测个人健康状况的变化。该系统依靠深度学习算法和物联网（IoT）来识别患者及其六个最相关的姿势。此外，还开发了算法来监测患者在较长时间段内的位置变化，这对于及时发现健康问题并采取适当措施可能很重要。最后，基于集成在基于决策树的模型中的专家知识和先验规则，生成关于护理计划状态的自动最终决策，以支持护理人员。
