Abstract: 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.

Abstract: 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.

Abstract: In one embodiment, an apparatus can include a service broker configured to: (i) register a service classifier, and to provide context information to the service classifier; and (ii) register a plurality of service nodes. The service broker can also receive capability and service requests from the service classifier. Further, the context information can include a service header, a reachability indication, and an encapsulation, where the service header and the encapsulation may be attached or related to a packet in the service classifier. In addition, the service classifier can use this information to redirect the packet to a first service node.

Abstract: 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.

Abstract: A system and method of processing one or more sensor logs includes receiving a sensor log an 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.

Abstract: 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.

Abstract: A system and method of processing one or more sensor logs includes receiving a sensor log an 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems may include a single gyroscope and gait logic to receive angular velocity data from the single gyroscope. The gait logic can also determine a stride length of an individual based on the angular velocity data. In one example, the gait logic integrates the angular velocity data to obtain angular displacement data and determines a stride length based at least in part on the angular displacement data.

Abstract: 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.

Abstract: 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.

Abstract: In one embodiment, an apparatus can include a service broker configured to: (i) register a service classifier, and to provide context information to the service classifier; and (ii) register a plurality of service nodes. The service broker can also receive capability and service requests from the service classifier. Further, the context information can include a service header, a reachability indication, and an encapsulation, where the service header and the encapsulation may be attached or related to a packet in the service classifier. In addition, the service classifier can use this information to redirect the packet to a first service node.