EAGER: Data Analysis for Nursing Care Assistance

EAGER：护理援助数据分析

• 批准号: 1258335
• 负责人: Jing Xiao
• 金额: $ 5.54万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1258335&HistoricalAwards=false
• 关键词: EAGER; Data; Analysis; Nursing; Care

The rising cost of long-term patient care, the shortage of nurses, and the increasing number of seniors in the United States make it imperative to investigate the possibility of intelligent systems for elder/patient care. One challenge is how to prevent falls, which often result in serious injury and considerable hospital and patient costs. In this exploratory project the PI and her team will focus on analyzing sensory data of patient actions in an effort to develop algorithms for the automatic detection and prediction of falls among elderly patients. Their goal is to gain a good understanding of how multimodal sensory data combined with domain knowledge of falls can be used to characterize pre-fall patient actions, in order to determine the feasibility of developing automatic alert systems that incorporate machine learning algorithms to assist human nurses and robotic caregivers by warning of potential falls. Broader Impacts: Project outcomes will pave the way for future development of intelligent systems to reduce the incidence of patient falls, which is a major societal concern. The project will provide a rich spectrum of interdisciplinary training for graduate student researchers, and will also strengthen UNC Charlotte's existing programs in broadening participation in computing and in research experiences for undergraduates (REU) by deepening involvement of women and minority undergraduate students in research.

长期患者护理的成本上升，护士短缺以及美国越来越多的老年人的成本使得必须调查对老年/患者护理的智能系统的可能性。 一个挑战是如何防止跌倒，这通常会导致严重伤害，医院和患者费用可观。 在这个探索性项目中，PI和她的团队将专注于分析患者行动的感觉数据，以开发老年患者自动检测和预测跌倒的算法。 他们的目标是对多模式的感觉数据与跌倒的领域知识结合在一起来表征前卫前患者的行为，以确定开发自动警报系统的可行性，从而通过警告潜在跌倒来帮助人类护士和机器人护理人员。 更广泛的影响：项目成果将为智能系统的未来开发铺平道路，以减少患者瀑布的发生率，这是一个主要的社会问题。 该项目将为研究生研究人员提供丰富的跨学科培训，还将加强UNC夏洛特现有的计划，以通过加深对妇女和少数群体在研究中的研究来扩大本科生（REU）的研究经验（REU）。