Rantz Marilyn, Skubic Marjorie, Abbott Carmen, Galambos Colleen, Popescu Mihail, Keller James, Stone Erik, Back Jessie, Miller Steven J, Petroski Gregory F
Sinclair School of Nursing and Family and Community Medicine, University of Missouri, Columbia.
Electrical and Computer Engineering, University of Missouri, Columbia.
Gerontologist. 2015 Jun;55 Suppl 1(Suppl 1):S78-87. doi: 10.1093/geront/gnv044.
Falls are a major problem for the elderly people leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care.
A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar and Kinect generated variables.
All FRAs are highly correlated (p < .01) with the Kinect gait velocity and Kinect stride length. Radar velocity is correlated (p < .05) to all the FRAs and highly correlated (p < .01) to most. Real-time alerts of actual falls are being sent to clinicians providing faster responses to urgent situations.
The in-home FRA and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.
跌倒对老年人来说是一个重大问题，会导致受伤、残疾甚至死亡。一种不引人注意的家庭传感器系统，能够持续监测老年人的跌倒风险并检测跌倒情况，可能会彻底改变跌倒预防和护理方式。
开发了一种跌倒风险与检测系统，并安装在一家老年生活设施中19位老年人的公寓里。该系统包括脉冲多普勒雷达、一台微软Kinect和两台网络摄像头。为了收集数据以与传感器数据进行比较并用于算法开发，特技演员在参与者的公寓里每月进行跌倒表演，持续了2年，参与者使用临床有效的标准化工具完成跌倒风险评估（FRA）。FRA由临床医生评分，并通过传感方式记录。参与者在GAITRite垫子上行走时测量其步态参数。这些数据被用作基准事实、客观数据，用于算法开发并与雷达和Kinect生成的变量进行比较。
所有FRA与Kinect步态速度和Kinect步幅高度相关（p < .01）。雷达速度与所有FRA相关（p < .05），与大多数FRA高度相关（p < .01）。实际跌倒的实时警报正在发送给临床医生，以便对紧急情况做出更快响应。
家庭FRA和检测系统有潜力帮助老年人保持独立、维持功能能力并在家中生活更长时间。
