a Department of Neuroscience & Movement Science, University of Fribourg , Fribourg , Switzerland.
b Head of "Frailty Care", Cantonal Hospital , Fribourg , Switzerland.
Inform Health Soc Care. 2019 Sep;44(3):237-245. doi: 10.1080/17538157.2018.1496089. Epub 2018 Aug 13.
Fall risk assessment is usually conducted in specialized centers using clinical tests. Most of the time, these tests are performed only after the occurrence of health problems potentially affecting gait and posture stability. Our aim is to define fall risk indicators that could routinely be used at home to automatically monitor the evolution of fall risk over time. We used the standard Timed Up and Go (T.U.G.) test to classify 43 individuals into two classes of fall risk, namely high- vs low- risk. Several parameters related to the gait pattern and the sitting position included in the T.U.G. test were automatically extracted using an ambient sensor (Microsoft Kinect sensor). We were able to correctly classify all individuals using machine learning on the combination of two parameters among gait speed, step length and speed to sit down. Coupled to an ambient sensor installed at home to monitor the relevant parameters in daily activities, these algorithms could therefore be used to assess the evolution of fall risk, thereby improving fall prevention.
跌倒风险评估通常在专门的中心使用临床测试进行。大多数情况下，只有在可能影响步态和姿势稳定性的健康问题发生后，才会进行这些测试。我们的目标是确定可以在家中常规使用的跌倒风险指标，以便自动监测跌倒风险随时间的演变。我们使用标准的计时起立行走（TUG）测试将 43 个人分为高风险和低风险两类。TUG 测试中与步态模式和坐姿相关的几个参数使用环境传感器（Microsoft Kinect 传感器）自动提取。我们通过对步态速度、步长和坐下速度这两个参数中的两个参数进行机器学习，成功地对所有个体进行了分类。与安装在家中以监测日常活动中相关参数的环境传感器相结合，这些算法可用于评估跌倒风险的演变，从而提高跌倒预防效果。
