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从运动感应床垫中获取多层信息,实现精准护理。

Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care.

机构信息

School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan.

Gerontechnology Research Center, Yuan Ze University, Taoyuan 320, Taiwan.

出版信息

Sensors (Basel). 2023 Feb 3;23(3):1736. doi: 10.3390/s23031736.

Abstract

Bed is often the personal care unit in hospitals, nursing homes, and individuals' homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users' bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents' on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients' sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers' main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users' personalized sleep-related data.

摘要

床通常是医院、疗养院和个人家庭中的个人护理单元。从床上的传感数据中可以获得丰富的护理相关信息。病人跌倒在医院是一个重大问题,其中许多与上下床有关。为了防止跌倒,我们开发了一种基于床垫中运动传感器的感测数据来检测病人是否要离开床的运动感应床垫。床垫控制盒中的芯片上部署的机器学习算法根据 30 个感测区域的开/关压力模式来识别床上姿势,以捕捉用户离开床的意图。本研究旨在探索如何使用源自运动感应床垫 30 个感测区域的开/关状态的睡眠相关数据,为包括仪表板上的健康状态和用于生活模式聚类的大数据分析在内的多个层面的精准护理信息提供支持。本研究描述了如何从运动感应床垫进一步获得多个层面的个性化护理相关信息,包括实时卧床/离床状态、日常记录、睡眠质量、长时间受压区域和长期生活模式。24 张床垫和智能床垫护理系统(SMCS)被安装在台湾的一家痴呆症疗养院进行现场试验。从 2021 年 8 月到 10 月,收集了 12 周居民的卧床/离床数据。SMCS 旨在通过集成的仪表板显示护理相关信息,并在检测到事件(如离开床和病人睡眠和生活模式变化)时向护理人员发送提醒。最终目标是为护理人员提供精准护理支持,减轻他们的护理负担,提高护理质量。在现场试验结束时,我们采访了四名护理人员,询问他们对 SMCS 的看法,包括他们是否认为 SMCS 有助于他们的工作,以及如果有帮助,具体是如何帮助的。护理人员的主要反馈包括,SMCS 帮助护理人员注意痴呆症患者的异常情况,与居民的家属沟通,确认药物调整,以及标准护理程序是否得到适当执行。未来的研究建议集中在基于用户个性化睡眠相关数据的综合护理策略建议上。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fa5/9919926/7a2a19862796/sensors-23-01736-g006.jpg

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