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Mitigating Injurious Falls in Older Adults Through Non-Injurious Fall and Gait Analysis From Floor Vibrations

通过非伤害性跌倒和地板振动的步态分析来减轻老年人的伤害性跌倒

基本信息

批准号：
10495263
负责人：
Stacy Lynne Fritz
金额：
$ 96.84万
依托单位：
ADVANCED SMART SYSTEMS/EVALUATION TECHNO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-30 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10495263
关键词：
Accelerometer Adopted Adoption Adult Age Alzheimer&apos s disease related dementia Area Assisted Living Facilities Back Beds Cause of Death Communities Data Detection Devices Disasters Discipline of Nursing Effectiveness Elderly Financial Hardship Fire - disasters Floor Future Gait Gait speed Generations Goals Hand Health Health Status Health care facility Healthcare Home Hospitals Impaired cognition Individual Industry Standard Injury Insurance Insurance Carriers Intelligence Left Legal Legal patent Letters Lutheran Church Measurement Measures Medical Medical center Methods Modeling Monitor Motion Nursing Homes Operating System Patient Monitoring Patients Patients&apos Rooms Penetration Personal Satisfaction Persons Phase Physicians&apos Offices Preventive healthcare Privacy Property Quality of life Recording of previous events Reporting Risk Risk Assessment Savings South Carolina System Technology Time TimeLine United States Department of Veterans Affairs Universities Ursidae Family Work aging in place base care providers combat cost design detection platform experience fall injury fall risk falls feature detection gait examination human old age (65+)improved injury-related death innovation innovative technologies insight operation predictive modeling pressure prisma sensor tool vibration wearable device wearable sensor technology

项目摘要

Project Summary and Abstract Falls are the leading cause of death due to injury. Falls are so common that 30% of community dwelling older adults, and 50% of residents in Care Facilities will experience a fall in the coming year. The risk of falling substantially increases for those having Alzheimer’s disease and related dementias. The financial burden is significant with fall-related costs being $50 billion. Care Facilities, who are often liable for the well-being of their patients, bear a substantial portion of the cost. A fall can cost $10,484 per case for Care Facilities. Commercially available fall detection systems operate via wearable pendant-based devices that patients press after experiencing a fall. Newer generations of these systems also incorporate accelerometers that are reportedly able to detect falls. These systems are patient-dependent, meaning that a patient must be wearing the pendant for it to work which older adults, particularly those with cognitive impairments, often do not. Furthermore, the patient has to be cognizant to press the button to call for aid if the pendant does not activate during a fall. This is unlikely to occur as even when people are not cognitively impaired, they will only activate the system 20% of the time. There is a clear need for an automated, patient-independent fall detection system to fill the gaps left by current approaches. Better yet would be a system that can detect non-injurious falls or changes in gait parameters, both of which are predictors of oncoming injurious falls. ASSET, in partnership with the University of South Carolina, has developed a patented, floor vibration monitoring system that can detect falls and collect gait information whilst being patient independent. The innovative product has the ability to firmly place control of liability back into the hands of Care Facilities much like what a fire alarm does for property damage from fires, and potentially saving ~$2.2 billion in fall-related costs with just 5% market adoption. During Phase II our overall goals are two-fold, first to further develop a system that does not rely on the patient to operate, overcoming the limitation of wearable systems and can additionally capture falls that are a predictor of oncoming injurious falls. We will monitor common areas with our vibration sensor system in places where Care Facility staff report the majority of falls occur. To accomplish the methods, we will use the Care Facilities’ common area video camera system to corroborate sensor fall activations are actual falls. Second, we will use the same passive system technology to explore gait measurement as an additional indicator of an oncoming health changes such as a fall. We will use gait parameter measuring technology in a Care Facility medical office for regular vital monitoring. We will use gait measurements with Facility fall reports to explore the effectiveness of our predictive fall risk model against industry-standard fall risk assessments. Future directions will include ASSET launching Beta trials of the product among Care Facilities for final refinement of the product before full release to the public.

项目摘要和摘要跌倒是因伤致死的主要原因。跌倒非常普遍，30%的社区居民年龄较大成年人和50%的护理机构的居民在来年将经历下降。跌倒的风险对于患有阿尔茨海默病和相关痴呆症的人来说，这一数字大幅增加。财政负担是与跌倒相关的成本为500亿美元，这是一个巨大的数字。护理机构，他们经常对他们的健康负责患者承担了很大一部分费用。一次跌倒可能会让护理机构的每箱花费10,484美元。商业上可用的跌倒检测系统通过患者按下的可穿戴式吊坠设备进行操作在经历了一次跌倒之后。新一代的这些系统还采用了加速计，据报道，它能够探测到瀑布。这些系统取决于患者，这意味着患者必须穿着这是老年人，特别是那些有认知障碍的人经常做不到的。此外，如果吊坠没有激活，患者必须意识到按下按钮来呼救在一次跌倒中。这不太可能发生，因为即使当人们没有认知障碍时，他们也只会激活系统有20%的时间。显然需要一种自动化的、独立于患者的跌倒检测系统来填补当前接近了。更好的是有一个系统可以检测到非损伤性的跌倒或步态参数的变化，这两者都是即将到来的伤害性跌倒的预测因素。ASSET，与南方大学合作卡罗莱纳州开发了一种获得专利的地板振动监测系统，可以检测跌倒并收集步态信息，同时保持患者的独立性。创新产品有能力牢牢地控制责任回到护理机构手中，就像火灾警报对火灾造成的财产损失所起的作用一样，只需5%的市场采用率，就有可能节省约22亿美元的秋季相关成本。在第二阶段，我们的总体目标有两个，第一，进一步开发一个不依赖于患者的系统以操作，克服可穿戴系统的限制，并可以额外捕获作为预测指标的跌落对即将到来的伤害性坠落的恐惧。我们将在以下地方使用我们的振动传感器系统监测公共区域护理机构的工作人员报告说，跌倒的大部分发生在那里。为了实现这些方法，我们将使用护理机构的公共区域摄像机系统证实传感器坠落的激活是真实的坠落。第二，我们将使用同样的被动系统技术，探索步态测量作为迎面而来的额外指示器健康方面的变化，如摔倒。我们将在医疗保健设施中使用步态参数测量技术定期生命监测办公室。我们将使用步态测量和设施坠落报告来探索我们的预测跌倒风险模型相对于行业标准跌倒风险评估的有效性。未来方向将包括在护理机构中启动产品的Beta试用，以进行产品的最终改进在完全向公众发布之前。