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.

在美国，长期病人护理费用的上升、护士的短缺以及老年人数量的增加使得研究智能老年人/病人护理系统的可能性变得势在必行。其中一个挑战是如何防止跌倒，这往往会导致严重伤害和大量的医院和病人费用。在这个探索性项目中，PI和她的团队将专注于分析患者动作的感官数据，努力开发自动检测和预测老年患者跌倒的算法。他们的目标是很好地理解如何将多模态感知数据与跌倒的领域知识相结合，用于描述跌倒前患者的行为，以确定开发包含机器学习算法的自动警报系统的可行性，该系统可以通过警告潜在的跌倒来帮助人类护士和机器人护理人员。更广泛的影响：项目成果将为未来智能系统的发展铺平道路，以减少患者跌倒的发生率，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，并将加强北卡罗来纳大学夏洛特分校现有的项目，通过深化女性和少数民族本科生参与研究，扩大对计算机和本科生研究经验（REU）的参与。