Technology to Automatically Detect Falls and Assess Fall Risk in Senior Housing

描述（由申请人提供）：每三个65岁或以上的人中就有一个每年福尔斯，这使得福尔斯成为老年人受伤和因创伤住院的最常见原因，也是受伤导致死亡的主要原因（CDC，2006）。研究人员研究了福尔斯、跌倒风险评估和预防福尔斯的干预措施。然而，迄今为止，他们的方法要求研究人员或临床医生完成跌倒风险的多因素评估，并且研究受试者保持福尔斯日志，佩戴测量可能指示跌倒的位置变化或激活他们需要帮助的警报的设备。基于我们目前的工作，我们建议验证和部署一种创新的技术方法，该方法可以自动检测何时发生福尔斯或何时福尔斯的风险正在增加。受试者无需按按钮、拉绳或佩戴任何设备。这种在家中使用传感器的新“被动”方法可以彻底改变检测和预防福尔斯以及测量跌倒风险的方法。通过早期检测福尔斯或增加跌倒风险，这项新技术可以作为老年人、家庭成员或医疗保健提供者的触发器，以改善身体功能或更好地管理导致福尔斯跌倒的疾病。这项研究的产品可以通过在任何私人住宅或公寓以及老年中心，教堂或零售店部署新的传感器系统来改善跌倒风险措施。在这样的设置中，人可以去可访问的区域以执行将由本申请中开发的传感器网络测量的引导运动。在短短几分钟内，一个人将有一个可靠的跌倒风险指标，以警告不断增加的跌倒风险。检测福尔斯的自动感测系统在老年人住宅、长期护理环境、私人社区住宅以及福尔斯是主要风险的急性护理环境中具有很大的潜力。经过实验室验证，使用567福尔斯由训练有素的戏剧特技演员表演，我们将部署原型传感系统的数据收集两年的10个公寓的老人在一个独立的生活设置，以完成验证和现场测试（再次使用960福尔斯由特技演员表演）。该应用程序不仅整合了医疗保健科学学科（护理，物理治疗，社会工作，医学）的专业人才和观点，还整合了电气和计算机工程，计算机科学和信息学。这个应用程序将是感兴趣的AHRQ和可能的创新和新兴的 领域组合，“将促进和培育的想法和项目，有可能导致高度创新的解决方案，可能会导致医疗保健实践的重大进步. "

DESCRIPTION (provided by applicant): One in every three people age 65 or older falls each year, making falls the most common cause of injuries and hospitalizations for trauma in older adults and the leading cause of death due to injury (CDC, 2006). Researchers have studied falls, fall risk assessment, and interventions to prevent falls. However, to date, their methods require that research staff or clinicians complete multi-factorial assessments of fall risk and that research subjects maintain logs of falls, wear devices that measure changes in positions that could indicate a fall or activate an alarm that they need assistance. Building on our current work, we propose to validate and deploy an innovative technological approach that automatically detects when falls have occurred or when the risk of falls is increasing. Subjects will not have to press buttons, pull cords or wear any devices. This new "passive" approach using sensors in the home could revolutionize detecting and preventing falls as well as measuring fall risk. By detecting falls or increasing fall risk early, this new technology can act as a trigger for elders, family members, or health care providers to improve physical function or better manage illnesses that are precipitating falls. The products of this study can improve access to fall risk measures by deploying the new sensor system in any private house or apartment as well as senior centers, churches, or retail stores. In such settings, persons could go to an accessible area to perform the guided motions to be measured by the sensor network developed in this application. In just a few minutes, a person would have a reliable fall risk indicator to alert increasing fall risk. An automatic sensing system to detect falls has major potential in senior housing, long-term care settings, private community housing as well in acute care settings where falls are a major risk. After laboratory validation using 567 falls performed by trained theater stunt actors, we will deploy the prototype sensing system for two years of data collection in ten apartments of elders in an independent living setting to complete validation and field testing (again using 960 falls performed by stunt actors). This application integrates the specialized talents and perspectives of not only health care scientific disciplines (nursing, physical therapy, social work, medicine) but also electrical and computer engineering, computer science, and informatics. This application will be of interest to AHRQ and likely the Innovations and Emerging Areas Portfolio that "will foster and nurture ideas and projects that have potential to lead to highly innovative solutions that may lead to significant advances in healthcare practice..."