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

CBET-0756058/CBET-0756645洛克哈特/拉赫（协作）福尔斯是老年人被迫从独立生活过渡到辅助护理的最常见原因。随着这一转变，生活质量往往会下降，医疗保健成本也会大幅增加，考虑到未来几十年老年人数量的预测增长，这是不可持续的。因此，降低跌倒率是个人和整个医疗保健行业的重要目标。正在开发治疗技术来帮助降低跌倒风险，但它们的成功取决于准确诊断容易跌倒的个体和精确评估技术提供的益处。研究人员正致力于通过研究步态、姿势和福尔斯之间的关系来提供这种能力。这些工作大部分是在现代运动捕捉实验室中完成的，这些实验室收集精确的步态和姿势数据，但价格昂贵且固定，限制了它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患者中实现准确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，该系统可在任何位置连续且非侵入性地收集准确和精确的步态和姿势数据。该系统将建立在克里思（技术使能医疗精密观察）系统之上，TEMPO系统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病患者的震颤。的疾病和原发性震颤患者。弗吉尼亚理工大学的一项试点研究的初步结果显示，这种系统有很大的前景。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，

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.