A Novel System to Detect Falls in Real-life Conditions

一种在现实生活中检测跌倒的新颖系统

• 批准号: 9144707
• 负责人: SHALENDER BHASIN
• 金额: $ 75万
• 依托单位: BIOSENSICS, LLC
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9144707
• 关键词: Acceleration; Accelerometer; Activities of Daily Living; Address; Adherence; Adoption; Aging; Algorithms; Arizona; Award; Beds; Budgets; Calendar; Caregivers; Collaborations; Collection; Communities; Data; Data Set; Detection; Development; Devices; Effectiveness of Interventions; Elderly; Emergency response; Enrollment; Event; Fall prevention; Feedback; Floor; Funding; Future; Habits; Health; Health Benefit; Health Personnel; Healthcare; Industry; Injury; Label; Laboratories; Legal patent; Licensing; Life; Marketing; Medical; Monitor; Motion; National Institute on Aging; New England; Outcome Measure; Outcomes Research; Participant; Patient Recruitments; Patient Self-Report; Patient-Focused Outcomes; Patients; Performance; Phase; Population; Positioning Attribute; Prevalence; Preventive Intervention; Protocols documentation; Public Health; Research; Research Institute; Research Project Grants; Sales; Sensitivity and Specificity; Signal Transduction; Site; Small Business Innovation Research Grant; Small Business Technology Transfer Research; Source; System; Tablets; Technology; Telephone; Testing; Time; Universities; Wireless Technology; age group; base; commercialization; cost; design; evidence base; experience; falls; follow-up; forgetting; improved; non-compliance; novel; prevent; research study; sensor; telehealth; therapy design; uptake

 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.

 描述（由适用提供）：跌倒是老年人致命和非致命伤害的主要原因（> 65岁），导致300亿美元的年医疗费用。医疗警报设备（通常是吊坠）可用于发出信号，以寻求帮助。最近，已经开发了具有自动秋季检测功能的医疗警报设备。这些设备使用加速度测定法检测跌落，如果佩戴者忘记或无能力按下警报按钮，则可以发出信号。这些设备的宽度采用受到未检测到的跌落和虚假警报的流行的限制，并且缺乏公开可用的研究，这些研究记录了在现实世界中的敏感性和虚假警报率的敏感性和错误警报率。生物密度与阿里桑那大学高级运动表现的跨学科联盟和亚利桑那州衰老中心合作开发了一个医疗警报吊坠（ActivePertM），其自动跌落检测，活动监测和非符合性警报通过IN National Ander of Aganing的I阶段的I＆II STTR。使用来自模拟瀑布的数据和在实验室环境中的日常生活的模拟活动开发了活跃者。在这种情况下，ActivePers具有100％的敏感性和特异性。但是，这些跌落检测算法在现实情况下尚未得到充分的表征。该提案的主要目标是在现实世界中测试活动物，并根据来自现实世界中跌倒的加速度数据，改善Activepers跌落检测算法，以实现灵敏度和虚假警报率之间的最佳权衡。此外，我们打算将活性器的使用扩展到近跌落的检测。对近跌落的检测可以实现新的结果指标，旨在评估旨在减少跌倒和接近老年人的干预措施的有效性。为了实现这些目标，有200名居住在社区的老年人将佩戴秋季检测传感器12个月。传感器将被配置为基于现有算法检测跌落，并记录原始三轴加速度计信号，以进行算法改进和开发新型算法以检测近跌落的新算法。将发现的跌倒和接近瀑布的瀑布与自我报告的瀑布和瀑布进行比较。这个Ambitiou项目通常不会得到II期SBIR的预算限制。但是，本提案代表了生物密度与合作伙伴医疗保健（新英格兰最大的医疗保健提供商）之间的独特伙伴关系。 Partners Healthcare是由以患者为中心的结果研究所（PCORI）资助的正在进行的，多站点的3,000万美元研究赠款中的试验地，以寻找有效的基于证据的预防策略。通过利用广泛的患者招聘并依靠正在进行的研究来收集自我报告的秋季日志，我们将能够在SBIR预算限制内实现既定目标。拟议的研究将为现实世界瀑布和独特位置生物密度提供最大的数据集，以使可靠的秋季检测技术商业化。