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    Wireless sensor based quantitative falls risk assessment
    2.
    发明授权
    Wireless sensor based quantitative falls risk assessment 有权
    基于无线传感器的定量下降风险评估

    公开(公告)号：US08805641B2

    公开(公告)日：2014-08-12

    申请号：US12782110

    申请日：2010-05-18

    申请人： Barry R. Greene

    发明人： Barry R. Greene

    IPC分类号： G01P3/00 ,  G01P15/00 ,  A61B5/11 ,  G06F19/00

    CPC分类号： A61B5/1117 ,  A61B5/1038 ,  A61B5/112 ,  A61B5/1123 ,  A61B5/6828 ,  A61B5/7275 ,  A61B2562/0219 ,  G01P3/00 ,  G01P3/64 ,  G01P15/00 ,  G06F19/00 ,  G16H50/30

    摘要： Methods and systems may provide for a plurality of kinematic sensors to be coupled to a corresponding plurality of shanks of an individual, a processor, and a memory to store a set of instructions. 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 a derived parameter based on the angular velocity data, and generate a falls risk assessment based on at least one of the TUG time segment and the derived parameter.

    摘要翻译： 方法和系统可以提供多个运动学传感器以耦合到个体，处理器和存储器的相应多个柄以存储一组指令。 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。 指令还可以使得系统基于角速度数据计算导出参数，并且基于TUG时间段和导出参数中的至少一个生成跌倒风险评估。

    SYSTEM AND METHOD FOR QUANTATIVE ASSESSMENT OF FRAILITY
    3.
    发明申请
    SYSTEM AND METHOD FOR QUANTATIVE ASSESSMENT OF FRAILITY 有权
    用于定量评估的系统和方法

    公开(公告)号：US20130110475A1

    公开(公告)日：2013-05-02

    申请号：US13283337

    申请日：2011-10-27

    申请人： Barry R. GREENE ,  Alan D. O'Donovan

    发明人： Barry R. GREENE ,  Alan D. O'Donovan

    IPC分类号： G06F17/10

    CPC分类号： G06F19/3431 ,  A61B5/1117 ,  A61B2562/0219 ,  G06F19/00 ,  G16H50/30

    摘要： Methods, systems, and apparatus for quantifying an individual's frailty level based on inertial sensor data collected from the individual. The quantified frailty level may correspond to and approximate clinical metrics of frailty, such as the Fried frailty index. A linear regression model may be used to output the quantitative frailty value based on input parameters from the inertial sensor data. The linear regression model may be initially generated from the clinically-measured frailty index values of individuals and inertial sensor data collected from them. The inertial sensor data may be collected during, for example, a timed up and go (TUG) test. Two logistic regression models may be used to output a frailty class based on input parameters from the inertial sensor data. A first logistic regression model may distinguish between robust and frail individuals. A second logistic regression model may distinguish between robust and pre-frail individuals.

    摘要翻译： 基于从个人收集的惯性传感器数据量化个体虚弱水平的方法，系统和装置。 量化的脆弱水平可能对应于近似疲劳的临床指标，如油脂脆弱指数。 可以使用线性回归模型基于来自惯性传感器数据的输入参数来输出定量脆弱值。 线性回归模型可以从临床测量的个体虚弱指数值和从其收集的惯性传感器数据最初产生。 惯性传感器数据可以在例如定时（TUG）测试期间被收集。 可以使用两个逻辑回归模型根据惯性传感器数据的输入参数输出虚弱类。 第一逻辑回归模型可以区分健壮和虚弱的个体。 第二个逻辑回归模型可以区分强健和虚弱的个体。

    MONITORING VELOCITY AND DWELL TRENDS FROM WIRELESS SENSOR NETWORK DATA
    4.
    发明申请
    MONITORING VELOCITY AND DWELL TRENDS FROM WIRELESS SENSOR NETWORK DATA 有权
    从无线传感器网络数据监测速度和速度趋势

    公开(公告)号：US20110153545A1

    公开(公告)日：2011-06-23

    申请号：US12642964

    申请日：2009-12-21

    申请人： Barry R. Greene ,  Adrian Burns

    发明人： Barry R. Greene ,  Adrian Burns

    IPC分类号： G06N5/02

    CPC分类号： G01P3/64 ,  A61B5/1113 ,  A61B5/6889 ,  A61B2503/08 ,  A61B2505/07 ,  A61B2562/043

    摘要： A system and method of processing one or more sensor logs includes receiving a sensor log and identifying a set of entries in the sensor log having a predefined sequence of sensor identifiers. The set of entries may define a velocity event. The method can also provide for calculating an in-home gait velocity for the velocity event. In one example, the method also provides for identifying another set of entries in the sensor log having a sensor identifier that corresponds to a dwell sensor mounted in a doorway, wherein the other set of entries define a dwell event. The method may also provide for calculating an in-home dwell time for the dwell event.

    摘要翻译： 处理一个或多个传感器记录的系统和方法包括接收传感器日志并识别传感器日志中具有预定义的传感器标识符序列的一组条目。 条目集可以定义速度事件。 该方法还可以提供用于计算速度事件的家庭步态速度。 在一个示例中，该方法还提供用于识别传感器日志中具有对应于安装在门口中的停留传感器的传感器标识符的另一组条目，其中另一组条目定义驻留事件。 该方法还可以提供用于计算停留事件的家庭停留时间。

    Calculation of minimum ground clearance using body worn sensors
    6.
    发明授权
    Calculation of minimum ground clearance using body worn sensors 有权
    使用身体磨损传感器计算最小离地间隙

    公开(公告)号：US09524424B2

    公开(公告)日：2016-12-20

    申请号：US13223759

    申请日：2011-09-01

    申请人： Barry R. Greene

    发明人： Barry R. Greene

    IPC分类号： G06K9/00 ,  A61B5/11 ,  G06F19/00 ,  A61B5/00 ,  A61B5/103

    CPC分类号： G06K9/00342 ,  A61B5/1036 ,  A61B5/112 ,  A61B5/1121 ,  A61B5/1127 ,  A61B5/7285 ,  G06K9/00563 ,  G16H50/50

    摘要： Methods, systems, and apparatus for deriving a relationship between minimum ground clearance (MGC) and inertial sensor data. A regression model may be generated by collecting tri-axial angular velocity and acceleration data from inertial sensors and MGC data from optical motion capture systems during a walking trial. A linear, quadratic, interaction, stepwise interaction, or another regression model may be generated. The regression model may estimate the MGC as a function of one or more parameters measured by or derived from the inertial sensor data. The regression model may be used to calculate an estimate of the MGC based on inertial sensor data collected from one or more individuals.

    摘要翻译： 用于推导出最小离地间隙（MGC）与惯性传感器数据之间的关系的方法，系统和装置。 可以通过在步行试验期间从惯性传感器和来自光学运动捕获系统的MGC数据收集三轴角速度和加速度数据来生成回归模型。 可以生成线性，二次，相互作用，逐步相互作用或另一回归模型。 回归模型可以将MGC估计为由惯性传感器数据测量或由惯性传感器数据导出的一个或多个参数的函数。 回归模型可以用于基于从一个或多个个体收集的惯性传感器数据来计算MGC的估计。

    Monitoring velocity and dwell trends from wireless sensor network data
    7.
    发明授权
    Monitoring velocity and dwell trends from wireless sensor network data 有权
    监测来自无线传感器网络数据的速度和驻留趋势

    公开(公告)号：US08775363B2

    公开(公告)日：2014-07-08

    申请号：US12642964

    申请日：2009-12-21

    申请人： Barry R. Greene ,  Adrian Burns

    发明人： Barry R. Greene ,  Adrian Burns

    IPC分类号： G06N5/00

    CPC分类号： G01P3/64 ,  A61B5/1113 ,  A61B5/6889 ,  A61B2503/08 ,  A61B2505/07 ,  A61B2562/043

    摘要： A system and method of processing one or more sensor logs includes receiving a sensor log and identifying a set of entries in the sensor log having a predefined sequence of sensor identifiers. The set of entries may define a velocity event. The method can also provide for calculating an in-home gait velocity for the velocity event. In one example, the method also provides for identifying another set of entries in the sensor log having a sensor identifier that corresponds to a dwell sensor mounted in a doorway, wherein the other set of entries define a dwell event. The method may also provide for calculating an in-home dwell time for the dwell event.

    摘要翻译： 处理一个或多个传感器记录的系统和方法包括接收传感器日志并识别传感器日志中具有预定义的传感器标识符序列的一组条目。 条目集可以定义速度事件。 该方法还可以提供用于计算速度事件的家庭步态速度。 在一个示例中，该方法还提供用于识别传感器日志中具有对应于安装在门口中的停留传感器的传感器标识符的另一组条目，其中另一组条目定义驻留事件。 该方法还可以提供用于计算停留事件的家庭停留时间。

    CALCULATION OF MINIMUM GROUND CLEARANCE USING BODY WORN SENSORS
    8.
    发明申请
    CALCULATION OF MINIMUM GROUND CLEARANCE USING BODY WORN SENSORS 有权
    使用身体传感器计算最小地面间隙

    公开(公告)号：US20130060512A1

    公开(公告)日：2013-03-07

    申请号：US13223759

    申请日：2011-09-01

    申请人： Barry R. GREENE

    发明人： Barry R. GREENE

    IPC分类号： G06F15/00

    CPC分类号： G06K9/00342 ,  A61B5/1036 ,  A61B5/112 ,  A61B5/1121 ,  A61B5/1127 ,  A61B5/7285 ,  G06K9/00563 ,  G16H50/50

    摘要： Methods, systems, and apparatus for deriving a relationship between minimum ground clearance (MGC) and inertial sensor data. A regression model may be generated by collecting tri-axial angular velocity and acceleration data from inertial sensors and MGC data from optical motion capture systems during a walking trial. A linear, quadratic, interaction, stepwise interaction, or another regression model may be generated. The regression model may estimate the MGC as a function of one or more parameters measured by or derived from the inertial sensor data. The regression model may be used to calculate an estimate of the MGC based on inertial sensor data collected from one or more individuals.

    摘要翻译： 用于推导出最小离地间隙（MGC）与惯性传感器数据之间的关系的方法，系统和装置。 可以通过在步行试验期间从惯性传感器和来自光学运动捕获系统的MGC数据收集三轴角速度和加速度数据来生成回归模型。 可以生成线性，二次，相互作用，逐步相互作用或另一回归模型。 回归模型可以将MGC估计为由惯性传感器数据测量或由惯性传感器数据导出的一个或多个参数的函数。 回归模型可以用于基于从一个或多个个体收集的惯性传感器数据来计算MGC的估计。

    System and method for quantitative assessment of frailty
    9.
    发明授权
    System and method for quantitative assessment of frailty 有权
    虚弱的定量评估系统和方法

    公开(公告)号：US09165113B2

    公开(公告)日：2015-10-20

    申请号：US13283337

    申请日：2011-10-27

    申请人： Barry R. Greene ,  Alan D. O'Donovan

    发明人： Barry R. Greene ,  Alan D. O'Donovan

    IPC分类号： A61B5/11 ,  G06F19/00

    CPC分类号： G06F19/3431 ,  A61B5/1117 ,  A61B2562/0219 ,  G06F19/00 ,  G16H50/30

    摘要： Methods, systems, and apparatus for quantifying an individual's frailty level based on inertial sensor data collected from the individual. The quantified frailty level may correspond to and approximate clinical metrics of frailty, such as the Fried frailty index. A linear regression model may be used to output the quantitative frailty value based on input parameters from the inertial sensor data. The linear regression model may be initially generated from the clinically-measured frailty index values of individuals and inertial sensor data collected from them. The inertial sensor data may be collected during, for example, a timed up and go (TUG) test. Two logistic regression models may be used to output a frailty class based on input parameters from the inertial sensor data. A first logistic regression model may distinguish between robust and frail individuals. A second logistic regression model may distinguish between robust and pre-frail individuals.

    摘要翻译： 基于从个人收集的惯性传感器数据量化个体虚弱水平的方法，系统和装置。 量化的脆弱水平可能对应于近似疲劳的临床指标，如油脂脆弱指数。 可以使用线性回归模型基于来自惯性传感器数据的输入参数来输出定量脆弱值。 线性回归模型可以从临床测量的个体虚弱指数值和从其收集的惯性传感器数据最初产生。 惯性传感器数据可以在例如定时（TUG）测试期间被收集。 可以使用两个逻辑回归模型根据惯性传感器数据的输入参数输出虚弱类。 第一逻辑回归模型可以区分健壮和虚弱的个体。 第二个逻辑回归模型可以区分强健和虚弱的个体。

    METHOD FOR BODY-WORN SENSOR BASED PROSPECTIVE EVALUATION OF FALLS RISK IN COMMUNITY-DWELLING ELDERLY ADULTS
    10.
    发明申请
    METHOD FOR BODY-WORN SENSOR BASED PROSPECTIVE EVALUATION OF FALLS RISK IN COMMUNITY-DWELLING ELDERLY ADULTS 审中-公开
    基于身体传感器的社区衰落风险预测评估方法 - 老年成人

    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