A Novel System to Detect Falls in Real-life Conditions

 描述(由申请人提供)：跌倒是老年人(65岁)致命性和非致命性伤害的主要原因，每年导致300亿美元的医疗费用。医疗警报设备通常作为吊坠佩戴，可以在跌倒时发出求救信号。最近，开发了具有自动跌倒检测功能的医疗警报设备。这些设备使用加速度计来检测跌倒，如果佩戴者忘记或无法按下警报按钮，则可以发出求救信号。这些设备的广泛采用受到了未检测到的跌倒和错误警报的普遍存在的限制，以及缺乏公开可用的研究来记录在现实世界环境下商业可获得的跌倒检测设备的灵敏度和误警率。BioSensics与亚利桑那大学高级运动性能跨学科联盟和亚利桑那州老龄化中心合作，开发了一种医疗警报挂件(ActivePERSTM)，通过国家老龄研究所的第一阶段和第二阶段STTR，具有自动跌倒检测、活动监测和不合规警报。ActivePERS是使用实验室环境中模拟的瀑布和模拟的日常生活活动的数据开发的。在这种情况下，ActivePERS具有100%的敏感性和特异性。然而，这些跌倒检测算法在现实条件下还没有得到充分的表征。该方案的主要目标是在真实环境中测试ActivePERS，并改进ActivePERS跌倒检测算法，以基于真实世界跌倒的加速数据在灵敏度和虚警率之间实现最佳折衷。此外，我们打算将ActivePERS的使用扩展到探测近距离瀑布。近距离跌倒的检测可以使旨在评估旨在减少老年人跌倒和近距离跌倒的干预措施的有效性的新的结果衡量标准成为可能。为了实现这些目标，200名居住在社区的老年人将在12个月的时间里佩戴跌倒探测传感器。该传感器将配置为在现有算法的基础上检测跌倒，并记录原始的三轴加速度计信号，用于算法改进和开发检测近距离跌倒的新算法。检测到的坠落和近距离坠落将与自我报告的坠落和近距离坠落进行比较。考虑到SBIR第二阶段的预算限制，这一雄心勃勃的项目通常是不可能的。然而，目前的提案代表了BioSensics和Partners Healthcare(新英格兰最大的医疗保健提供商)之间的独特合作伙伴关系。Partner Healthcare是一项由以患者为中心的结果研究所(PCORI)资助的持续、多地点、价值3000万美元的研究拨款中的一个试验站点，旨在寻找有效和循证的预防跌倒策略。通过利用广泛的正在进行的患者招募，并依靠正在进行的研究收集自我报告的秋季日志，我们将能够在SBIR预算限制的范围内实现所述目标。这项拟议的研究将提供迄今为止最大的真实跌倒数据集，并将BioSensics独特地定位于将可靠的跌倒检测技术商业化。

 DESCRIPTION (provided by applicant): Falls are the leading cause of fatal and nonfatal injuries among older adults (>65 years) and result in $30 billion in annual medical costs. Medical alert devices, commonly worn as a pendant, can be used to signal for help in the event of a fall. More recently, medical alert devices with automatic fall detection functionality have been developed. These devices use accelerometry to detect a fall and can signal for help if the wearer forgets to, or is incapable of, pressing the alert button. Widespread adoption of these devices has been limited by the prevalence of undetected falls and false alerts, and by the lack of publically available studies documenting the sensitivity and false alarm rate of commercially-available fall detection devices under real-world settings. BioSensics, in collaboration with the Interdisciplinary Consortium on Advanced Motion Performance and the Arizona Center on Aging at the University of Arizona, developed a medical alert pendant (ActivePERSTM) with automatic fall detection, activity monitoring, and non-compliance alerts through a Phase I & II STTR from the National Institute on Aging. ActivePERS was developed using data from simulated falls and simulated activities of daily living in a laboratory setting. In this setting, ActivePERS has 100% sensitivity and specificity. However, these fall detection algorithms have not been adequately characterized under real-world conditions. The primary objectives of this proposal are to test ActivePERS in a real-world setting, and to improve the ActivePERS fall detection algorithm to achieve an optimal trade-off between sensitivity and false alarm rate, based on acceleration data from real-world falls. In addition, we intend to extend the use of ActivePERS to the detection of near falls. The detection of near falls could enable novel outcome measures aimed to evaluate the effectiveness of interventions designed to achieve a decrease in falls and near falls in older adults. To accomplish these objectives, 200 community-dwelling older adults will wear a fall detection sensor for a period of 12 months. The sensor will be configured to detect falls based on existing algorithms, as well as to record raw tri-axial accelerometer signals for th purposes of algorithm improvements and development of a novel algorithm to detect near falls. Detected falls and near falls will be compared to self-reported falls and near falls. This ambitiou project would not typically be possible given the budget constraints of a Phase II SBIR. However, the present proposal represents a unique partnership between BioSensics and Partners Healthcare (the largest healthcare provider in New England). Partners Healthcare is a trial site in an ongoing, multi-site, $30 million research grant, funded by the Patient-Centered Outcomes Research Institute (PCORI), to find effective and evidence-based strategies for falls prevention. By leveraging the extensive ongoing patient recruitment and relying on the ongoing study for collection of self-reported fall logs, we will be able to achieve the stated objectives within the SBIR budget constraints. The proposed study will provide the largest dataset to date of real-world falls and uniquely position BioSensics to commercialize a reliable fall detection technology.