权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Sensor Fusion System For Early And Accurate Fall Detection and Injury Protection

传感器融合系统可实现早期、准确的跌倒检测和伤害保护

基本信息

批准号：
10256574
负责人：
Emily A Keshner
金额：
$ 29.89万
依托单位：
GRACEFALL, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10256574
关键词：
Acceleration Accelerometer Adult Air Bags Algorithms Anterior Appearance Body measure procedure Brain Cessation of life Coupled Data Detection Devices Elderly Electroencephalography Epilepsy Equilibrium Event Future Goals Hip region structure Human Image Injury Lead Legal patent Link Literature Medical Care Costs Methods Motion Motor Movement Output Parkinson Disease Participant Patients Pattern Pediatric Hospitals Performance Peripheral Nervous System Phase Philadelphia Physiological Physiology Population Populations at Risk Posture Prevention Protective Devices Public Health Quality of life Reaction Reflex action Research Risk Sampling Sensory Signal Transduction Startle Reaction System Technology Translating aging population base experimental study fall injury falls human subject improved injury prevention mortality motion sensor neuromuscular prevent response sensor severe injury wearable device young adult

项目摘要

Project Summary/Abstract Fall is the leading cause of injury among elderly. One in three adults over 65 falls each year. Of those who fall, 20% to 30% suffer moderate to severe injuries and increase their risk of early death. In 2015, the total medical costs related to fall injuries for people 65 and older was over $50 billion. Currently, there are devices that use motion sensors (accelerometers) to detect imminent falls and inflate micro-airbags located in garments worn by the users to protect from injury. The literature has shown that wearable solutions based on motion sensing have low detection accuracy and suffer from false-positive events (the airbag may erroneously deploy during daily activities after interpreting abrupt movements as falls). GraceFall, Inc. (GFI) will develop a patent protected fall detection device based on a sensor-fusion algorithm that combines brain (EEG) and body motion signals to allow reliable fall prediction and injury protection. Our initial findings, along with supporting literature, show that a reliable EEG signal preceding an unexpected loss of balance could be the key to developing a complete, reliable, ergonomic solution for fall detection and injury prevention, and would have a major impact on maintaining mobility and quality of life in our aging population. Accelerometers reflect body movement and it is difficult to distinguish between loss of stability and other non-fall related activities. The key difference between intended actions and unintended loss of balance is the appearance of a “startle” response that can be captured on most EEG channels. Using EEG sensors will allow us to identify the difference between a fall and other acceleration scenarios. Our goal is to create a device that primarily uses these reliable brain responses, coupled with motion sensors, to accurately detect loss of balance and stability, thereby preventing injuries due to falling. The goal of the proposed Phase I project is to provide a proof of concept for a future product. Using existing fall protection products that rely on motion sensing to detect an imminent fall, we will identify scenarios in which these products have either false-positive (the airbag erroneously deploying in daily activities after interpreting an abrupt movement as a fall) or false-negative (the airbag not deploying in a real fall scenario) events. We will simulate these same scenarios on human subjects (Aim 1) and we will characterize the physiological parameters of the startle response in an elderly population (Aim 2) to refine the sensor fusion algorithm. The purpose of this proposal is proof of concept that adding a sensor fusion algorithm that combines the EEG information with the acceleration data, improves the performance and reliability of the protection system.

项目总结/摘要跌倒是老年人受伤的主要原因。65岁以上的成年人每年有三分之一福尔斯。在那些倒下的人中， 20%至30%的人遭受中度至重度伤害，并增加了他们过早死亡的风险。2015年，医疗总 65岁及以上的人因跌倒受伤而造成的损失超过500亿美元。目前，有一些设备使用运动传感器（加速度计），用于检测即将发生的福尔斯跌倒并对位于服装中的微型气囊充气，保护用户免受伤害。文献表明，基于运动传感的可穿戴解决方案具有低的检测精度并且遭受假阳性事件（在使用过程中气囊可能错误地展开在将突然运动解释为福尔斯之后的日常活动）。GraceFall，Inc. (GFI)将开发一项专利基于结合大脑（EEG）和身体运动的传感器融合算法的受保护跌倒检测设备信号，以实现可靠的跌倒预测和伤害保护。我们的初步发现，沿着支持文献，表明，在意外失去平衡之前的可靠EEG信号可能是开发一种完整、可靠、符合人体工程学的跌倒检测和伤害预防解决方案，并将产生重大影响维持人口老化的流动性和生活质素。加速度计反映身体运动，很难区分失去稳定性和其他非坠落相关活动。的关键区别在有意的行动和无意的失去平衡之间，是一种“惊吓”反应的出现，大多数脑电图频道都能捕捉到使用脑电图传感器将使我们能够识别跌倒和其他加速场景。我们的目标是创造一种设备，主要利用这些可靠的大脑反应，再加上运动传感器，以准确地检测失去平衡和稳定，从而防止受伤，到坠落拟议的第一阶段项目的目标是为未来的产品提供概念验证。使用现有的坠落保护产品依赖于运动传感来检测即将发生的坠落，我们将识别其中这些产品具有假阳性（安全气囊在日常活动中错误地展开，将突然运动解释为跌倒）或假阴性（安全气囊在真实的跌倒场景中未展开）事件我们将在人类受试者身上模拟这些相同的场景（目标1），我们将描述老年人群惊吓反应的生理参数（目标2），以完善传感器融合算法该提案的目的是概念证明，增加传感器融合算法，将脑电信息与加速度数据相结合，提高了保护的性能和可靠性系统