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.

项目概要/摘要每年跌倒的 65 岁以上成年人中，有三分之一是跌倒的主要原因。 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，医疗总量。目前，65 岁及以上人群因跌倒受伤造成的损失超过 500 亿美元。运动传感器（加速度计）用于检测即将发生的跌倒并对位于衣服中的微型安全气囊进行充气文献表明，基于运动传感的可穿戴解决方案。检测准确率低，容易出现误报（安全气囊在打开过程中可能会错误展开） 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跌倒保护检测装置我们的初步发现以及支持文献，提供了可靠的跌倒预测和伤害保护的信号。表明在意外失去平衡之前可靠的脑电图信号可能是开发用于跌倒检测和伤害预防的完整、可靠、符合人体工程学的解决方案，并将产生重大影响保持老年人的流动性和生活质量加速度计反映了身体运动及其变化。很难区分失去稳定性和其他非跌倒相关活动的关键区别。在有意的行为和无意的失去平衡之间会出现“惊吓”反应，这种反应可以是使用脑电图传感器捕获大多数脑电图通道将使我们能够识别跌倒和跌倒之间的差异。我们的目标是创建一种主要使用这些可靠的大脑反应的设备，与运动传感器相结合，准确检测平衡和稳定性的丧失，从而防止因伤害拟议的第一阶段项目的目标是为未来产品的使用提供概念证明。现有的跌倒防护产品依靠运动感应来检测即将发生的跌倒，我们将识别场景其中这些产品存在误报（安全气囊在日常活动中错误展开）将突然的运动解释为跌倒）或假阴性（安全气囊在真实跌倒场景中未展开）我们将在人类受试者身上模拟这些相同的场景（目标 1），并描述这些事件的特征。老年人群惊吓反应的生理参数（目标 2）以完善传感器融合该提案的目的是证明添加传感器融合算法的概念。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。