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.

项目摘要/摘要跌倒是老年人的主要原因。 20％至30％的人受到中度至重度伤害，并增加了早期死亡的风险。与65岁及以上的人跌倒有关的费用超过500亿美元。运动传感器（加速度计）以检测到即将跌落并充气的微型弹袋，位于服装中用户免受伤害。检测准确性较低，并遭受虚假阳性事件的痛苦将突然的动作解释为瀑布的日常活动。根据结合大脑（EEG）和身体运动的传感器融合算法的保护秋季检测装置允许可靠的跌倒预测和伤害保护的信号。证明在意外失去平衡之前，可靠的脑电图可能是开发一个的关键完整，可靠，符合人体工程学的解决方案，用于预防跌倒，并将产生重大影响维持我们老龄化的流动性和生活质量。很难区分稳定性的损失和其他非下降活动在预期的行动和意外失去平衡之间是“起动”响应的外观，可以是在大多数脑电图通道上捕获。其他加速场景。再加上运动传感器，以准确检测平衡和稳定性的丧失，从而防止跌倒。现有的依靠运动来检测即将跌落的秋季保护产品，我们将确定方案这些产品具有假阳性（安全气囊在将突然的运动解释为秋天或假阴性（安全气囊不在真正的秋季场景中部署）事件。老年人群中惊吓反应的生理参数（AIM 2），以完善传感器融合算法。带有加速数据的脑电图信息，提高保护的性能和宗教性系统。