Technology to Automatically Detect Falls and Assess Fall Risk in Senior Housing

自动检测跌倒并评估老年住宅跌倒风险的技术

基本信息

  • 批准号:
    8281330
  • 负责人:
  • 金额:
    $ 49.87万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-09-30 至 2014-07-31
  • 项目状态:
    已结题

项目摘要

Title: Automatic Sensing System to Detect Falls and Fall-Risk in Elders. Project Summary and Abstract One in every three people age 65 or older falls each year, making falls the most common cause of injuries and hospitalizations for trauma in older adults and the leading cause of death due to injury (CDC, 2006). Researchers have studied falls, fall risk assessment, and interventions to prevent falls. However, to date, their methods require that research staff or clinicians complete multi-factorial assessments of fall risk and that research subjects maintain logs of falls, wear devices that measure changes in positions that could indicate a fall or activate an alarm that they need assistance. Building on our current work, we propose to validate and deploy an innovative technological approach that automatically detects when falls have occurred or when the risk of falls is increasing. Subjects will not have to press buttons, pull cords or wear any devices. This new "passive" approach using sensors in the home could revolutionize detecting and preventing falls as well as measuring fall risk. By detecting falls or increasing fall risk early, this new technology can act as a trigger for elders, family members, or health care providers to improve physical function or better manage illnesses that are precipitating falls. The products of this study can improve access to fall risk measures by deploying the new sensor system in any private house or apartment as well as senior centers, churches, or retail stores. In such settings, persons could go to an accessible area to perform the guided motions to be measured by the sensor network developed in this application. In just a few minutes, a person would have a reliable fall risk indicator to alert increasing fall risk. An automatic sensing system to detect falls has major potential in senior housing, long-term care settings, private community housing as well in acute care settings where falls are a major risk. After laboratory validation using 567 falls performed by trained theater stunt actors, we will deploy the prototype sensing system for two years of data collection in ten apartments of elders in an independent living setting to complete validation and field testing (again using 960 falls performed by stunt actors). This application integrates the specialized talents and perspectives of not only health care scientific disciplines (nursing, physical therapy, social work, medicine) but also electrical and computer engineering, computer science, and informatics. This application will be of interest to AHRQ and likely the Innovations and Emerging Areas Portfolio that "will foster and nurture ideas and projects that have potential to lead to highly innovative solutions that may lead to significant advances in healthcare practice..."
题目:侦测长者福尔斯及跌倒风险的自动感应系统。 项目概要和摘要 65岁或以上的人中,每年有三分之一的人福尔斯,这使得福尔斯成为最常见的受伤原因 老年人因创伤而住院治疗,也是损伤导致死亡的主要原因(CDC,2006)。 研究人员研究了福尔斯、跌倒风险评估和预防福尔斯的干预措施。然而,到目前为止,他们 方法要求研究人员或临床医生完成跌倒风险的多因素评估, 研究对象保持福尔斯的日志,佩戴测量位置变化的设备, 跌倒或启动警报,他们需要帮助。在我们目前工作的基础上,我们建议验证和 部署创新的技术方法,自动检测何时发生福尔斯或何时 福尔斯的风险正在增加。受试者无需按下按钮、拉绳或佩戴任何器械。这个新 在家中使用传感器的“被动”方法可以彻底改变检测和防止福尔斯以及 测量跌倒风险。通过早期检测福尔斯或增加跌倒风险,这项新技术可以作为触发因素, 老年人,家庭成员或医疗保健提供者,以改善身体功能或更好地管理疾病, 正在加速福尔斯的坠落。本研究的产品可以通过部署 新的传感器系统在任何私人住宅或公寓,以及老年中心,教堂,或零售商店。在 在这样的设置下,人们可以到可接近的区域来执行将被测量的引导运动。 传感器网络开发的应用。在短短几分钟内,一个人将有一个可靠的跌倒风险 警告跌倒风险增加的指标。一个自动传感系统,以检测福尔斯有很大的潜力,在高级 住房、长期护理机构、私人社区住房以及福尔斯是 重大风险。经过实验室验证使用567福尔斯由训练有素的戏剧特技演员表演,我们将部署 该原型传感系统在一个独立的10个老年人公寓进行了两年的数据收集 现场设置完成验证和现场测试(再次使用特技演员表演的960次福尔斯)。这 应用整合了专业人才和观点,不仅卫生保健科学学科 (护理、物理治疗、社会工作、医学)还有电气和计算机工程、计算机 科学和信息学。这个应用程序将是感兴趣的AHRQ和可能的创新和新兴的 领域组合,“将促进和培育有潜力导致高度创新的想法和项目, 这些解决方案可能会导致医疗保健实践的重大进步...... "

项目成果

期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Radar walk detection in the apartments of elderly.
老年人公寓的雷达步行检测。
Monitoring patients in hospital beds using unobtrusive depth sensors.
使用不显眼的深度传感器监测医院病床上的患者。
Capturing habitual, in-home gait parameter trends using an inexpensive depth camera.
使用廉价的深度相机捕捉习惯性的家庭步态参数趋势。
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MARILYN J RANTZ其他文献

MARILYN J RANTZ的其他文献

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{{ truncateString('MARILYN J RANTZ', 18)}}的其他基金

Intelligent Sensor System for Early Illness Alerts in Senior Housing
用于老年住宅早期疾病警报的智能传感器系统
  • 批准号:
    8662807
  • 财政年份:
    2013
  • 资助金额:
    $ 49.87万
  • 项目类别:
Intelligent Sensor System for Early Illness Alerts in Senior Housing
用于老年住宅早期疾病警报的智能传感器系统
  • 批准号:
    8478491
  • 财政年份:
    2013
  • 资助金额:
    $ 49.87万
  • 项目类别:
Technology to Automatically Detect Falls and Assess Fall Risk in Senior Housing
自动检测跌倒并评估老年住宅跌倒风险的技术
  • 批准号:
    7933742
  • 财政年份:
    2009
  • 资助金额:
    $ 49.87万
  • 项目类别:
Technology to Automatically Detect Early Signs of Illness in Senior Housing
自动检测老年住宅早期疾病迹象的技术
  • 批准号:
    7914329
  • 财政年份:
    2009
  • 资助金额:
    $ 49.87万
  • 项目类别:
Technology to Automatically Detect Falls and Assess Fall Risk in Senior Housing
自动检测跌倒并评估老年住宅跌倒风险的技术
  • 批准号:
    7785679
  • 财政年份:
    2009
  • 资助金额:
    $ 49.87万
  • 项目类别:
Technology to Automatically Detect Falls and Assess Fall Risk in Senior Housing
自动检测跌倒并评估老年住宅跌倒风险的技术
  • 批准号:
    8111672
  • 财政年份:
    2009
  • 资助金额:
    $ 49.87万
  • 项目类别:
Multilevel Intervention to Improve Nursing Home Outcomes
多层次干预改善疗养院的结果
  • 批准号:
    7193528
  • 财政年份:
    2005
  • 资助金额:
    $ 49.87万
  • 项目类别:
Multilevel Intervention to Improve Nursing Home Outcomes
多层次干预改善疗养院的结果
  • 批准号:
    6916924
  • 财政年份:
    2005
  • 资助金额:
    $ 49.87万
  • 项目类别:
Multilevel Intervention to Improve Nursing Home Outcomes
多层次干预改善疗养院的结果
  • 批准号:
    7056802
  • 财政年份:
    2005
  • 资助金额:
    $ 49.87万
  • 项目类别:
Multilevel Intervention to Improve Nursing Home Outcomes
多层次干预改善疗养院的结果
  • 批准号:
    7572957
  • 财政年份:
    2005
  • 资助金额:
    $ 49.87万
  • 项目类别:

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开发商业上可行的机器学习产品来自动检测肩袖肌肉病理
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使用自然语言处理和机器学习技术自动检测网络欺凌。
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