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由于受伤而导致死亡的主要原因。瀑布是如此普遍，以至于30％的社区居住成人和50％的护理机构居民将在来年下降。跌倒的风险患有阿尔茨海默氏病和相关痴呆症的人大大增加。金融伯恩是与跌倒有关的重要成本为500亿美元。护理设施，他们通常对自己的福祉负责患者有很大一部分费用。跌倒的费用可能为每例护理设施的$ 10,484。市售的秋季检测系统通过基于可穿戴吊坠的设备运行，患者按下经历了秋天。这些系统的新一代还包含了加速度计据报道能够检测到跌倒。这些系统依赖于患者，这意味着患者必须佩戴吊坠的工作，老年人，尤其是那些认知障碍的老年人，通常不做。此外，如果吊坠未激活在秋天。即使人们没有认知受损，这也不太可能发生，他们只会激活该系统有20％的时间。显然需要一个自动，与患者无关的秋季检测系统来填补当前留下的空白方法。更好的是一个可以检测到无兴伤的跌倒或获取参数变化的系统，这两者都是即将到来的有害瀑布的预测指标。资产与南大学合作卡罗来纳州，已经开发了一种专利的地板振动监控系统，可以检测到跌倒并收集步态在患者独立的同时，信息。创新产品具有第五位控制的能力责任回到护理设施的手中，就像火灾对火灾造成财产损害所做的事情一样，并有可能节省约22亿美元的秋季相关成本，而市场采用了5％。在第二阶段，我们的总体目标是两个方面，首先要进一步开发不依赖患者的系统操作，克服可穿戴系统的局限性，并可以捕获预测因子即将来临的有害瀑布。我们将使用我们的振动传感器系统监视公共区域护理机构工作人员报告大多数瀑布发生。为了完成这些方法，我们将使用护理设施公共区域摄像机系统以证实传感器跌落激活是实际跌倒。第二，我们将使用相同的被动系统技术以探索符合度量作为迎面的附加指标健康变化，例如跌倒。我们将在护理机构医疗中使用收集参数测量技术定期监控的办公室。我们将使用与设施秋季报告的满足测量结果探索我们预测性跌落风险模型对行业标准秋季风险评估的有效性。未来的方向将包括在护理设施中启动该产品的Beta试验以最终完善产品的资产试验在全部发布给公众之前。