公开(公告)号：US20130023798A1

公开(公告)日：2013-01-24

申请号：US13186709

申请日：2011-07-20

申请人： Barry R. GREENE ,  Emer P. Doheny

发明人： Barry R. GREENE ,  Emer P. Doheny

IPC分类号： A61B5/11

CPC分类号： A61B5/112 ,  A61B5/1117 ,  A61B5/6828 ,  A61B2562/0219

摘要： Methods and systems may provide for falls risk assessment using body-worn sensors. If executed by the processor, the instructions can cause the system to calculate a timed up and go (TUG) time segment based on angular velocity data from the plurality of kinematic sensors. The instructions may also cause the system to calculate one or more derived parameters based on the angular velocity data, including temporal gait parameters, spatial gait parameters, tri-axial angular velocity parameters, and turn parameters. Falls data may be collected retrospectively, based on whether the test participant has fallen in the past. Falls data may be collected prospectively, in which the individual is contacted in the future to determine if they have fallen. This outcome data may be used to train regularized discriminant classifier models based on relevant sub-sets of the feature set, selected using sequential forward feature selection. Regularized discriminant parameters and along with associated sequential forward feature selection obtained feature set are obtained via grid-search

摘要翻译： 方法和系统可以使用身体磨损的传感器来提供跌倒风险评估。 如果由处理器执行，指令可以使得系统基于来自多个运动传感器的角速度数据来计算定时和去（TUG）时间段。 指令还可以使系统基于包括时间步态参数，空间步态参数，三轴角速度参数和转弯参数的角速度数据来计算一个或多个派生参数。 根据测试参与者是否已经过去，可以追溯收集下降数据。 未来数据可能会被收集，未来将会联系到个人，以确定他们是否已经下降。 该结果数据可以用于基于使用顺序前向特征选择选择的特征集的相关子集来训练正则化判别分类器模型。 通过网格搜索获得正则化判别参数以及相关顺序前向特征选择获得的特征集

公开(公告)号：US09877667B2

公开(公告)日：2018-01-30

申请号：US13611012

申请日：2012-09-12

申请人： Emer P. Doheny

发明人： Emer P. Doheny

IPC分类号： A61B5/11 ,  G06F19/00 ,  A61B5/00 ,  G08B21/04

CPC分类号： A61B5/1116 ,  A61B5/1117 ,  A61B5/7264 ,  A61B5/7275 ,  A61B2562/0219 ,  G06F19/00 ,  G08B21/0446 ,  G08B21/0453 ,  G16H50/30

摘要： Methods and systems may provide for estimating falls risk based on inertial sensor data collected during a Five Times Sit-to-Stand (FTSS) test. In an embodiment, a classifier model may be trained with inertial sensor data collected from a sample of people performing the FTSS test and their self-reported falls history. In an embodiment, one or more features related to steadiness or smoothness of the person's movement may be calculated. In an embodiment, one or more features related to timing of the FTSS test, such as a total time taken to complete the FTSS test or to complete individual sit-stand-sit (SSS) phases of the test, may be calculated. In an embodiment, supervised pattern recognition techniques may train the classifier model to classify a person as being likely to fall or not being likely to fall based on FTSS-related feature values collected from that person.