Collaborative Research: Continuous, Non-Invasive Gait Analysis and Fall-Risk Assessment

合作研究：连续、非侵入性步态分析和跌倒风险评估

• 批准号: 0756645
• 负责人: John Lach
• 金额: $ 22.5万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0756645&HistoricalAwards=false
• 关键词: Collaborative; Research; Continuous; Non; Invasive

CBET-0756058/CBET-0756645Lockhart/Lach (Collaborative)Falls are the most common cause of elderly individuals being forced to transition from independent living to assisted care. With this transition often comes a decrease in quality of life and always comes a tremendous increase in healthcare costs, which are not sustainable given the forecasted rise in the number of elderly people over the coming decades. Reducing fall rates is therefore an important goal for both individuals and the entire healthcare industry. Therapeutic techniques are being developed to help reduce fall risk, but their success depends both on accurately diagnosing fall prone individuals and on precisely assessing the benefits the techniques provide. Researchers are working towards providing the capabilities by studying the relationship between gait, posture, and falls. Much of this work is done in modern motion capture laboratories, which collect precise gait and posture data but are expensive and immobile, limiting their capabilities for continuous, long-term data collection in natural settings. As a result, gait researchers have had limited success achieving accurate fall risk assessment in diverse patient sets. The proposed project addresses these limitations by developing and validating a custom-designed wearable wireless sensor system that collects accurate and precise gait and posture data continuously and non-invasively in any location. The system will be built on top of the TEMPO (Technology Enabled Medical Precision Observation) system, an accelerometer-based wearable sensor system developed at the University of Virginia that is currently being used with success by medical researchers to monitor and assess tremor in Parkinson?s Disease and Essential Tremor patients. Preliminary results from a pilot study at Virginia Tech show great promise in such a system?s ability to provide high application fidelity, even if the raw motion data is less precise than a laboratory-based motion capture laboratories.

CBET-0756058/CBET-0756645洛克哈特/拉赫(合作)瀑布是老年人被迫从独立生活过渡到辅助护理的最常见原因。这种转变往往伴随着生活质量的下降，并总是伴随着医疗成本的大幅上升，考虑到未来几十年老年人口数量预计将会上升，这是不可持续的。因此，降低跌倒发生率是个人和整个医疗保健行业的一个重要目标。治疗技术正在开发中，以帮助降低跌倒的风险，但它们的成功既取决于准确诊断易摔倒的个人，也取决于准确评估这些技术提供的好处。研究人员正致力于通过研究步态、姿势和跌倒之间的关系来提供这种能力。这项工作的大部分是在现代运动捕捉实验室完成的，这些实验室收集精确的步态和姿势数据，但昂贵且不能移动，限制了他们在自然环境中连续、长期收集数据的能力。因此，步态研究人员在不同的患者组中实现准确的跌倒风险评估的成功有限。拟议的项目通过开发和验证定制设计的可穿戴无线传感器系统来解决这些限制，该系统可以在任何位置连续和非侵入性地收集准确和精确的步态和姿势数据。该系统将建立在TEMPO(科技驱动的医疗精密观测)系统的基础上，TEMPO是弗吉尼亚大学开发的一种基于加速度计的可穿戴传感器系统，目前医学研究人员正在成功地使用该系统来监测和评估帕金森氏症、S病和特发性震颤患者的震颤。弗吉尼亚理工大学试点研究的初步结果显示，在这样的系统中前景广阔-S能够提供高应用保真度，即使原始运动数据不如基于实验室的运动捕捉实验室精确。