Intel-Ge Care Innovations LLC

Care Innovations, LLC

Kinesis Health Technologies Limited

Barry R Greene

Adrian Burns

Alan D O'Donovan

A system, method, and apparatus is provided for estimating a risk of falls from pressure sensor data and inertial sensor data. A classifier function may be generated to relate the inertial sensor and the pressure sensor data with the falls risk (e.g., falls risk or prospective falls data) of the person who generated the pressure sensor data and inertial sensor data. The classifier function may be used to predict a person's risk of falls based on inputs of pressure sensor data and inertial sensor data. Separate classifier functions may be generated for men and women, or separate classifier functions may be generated for patients who closed their eyes while data was being collected and for patients who opened their eyes while data was being collected.

Quantitative falls risk assessment through inertial sensors and pressure sensitive platform

Methods and systems may provide for cognitive decline prediction or assessment. Baseline and follow-up inertial sensor data may be received from one or more inertial sensors attached to a person. Baseline and follow-up data indicative of cognitive decline may be received from the person. An indication of cognitive decline may be determined based on the baseline and follow-up cognitive decline data. A classifier function for predicting cognitive decline may be trained with the baseline inertial sensor data and the indication of cognitive decline. A classifier function for assessing cognitive decline may be trained with the baseline inertial sensor data, a difference between the baseline inertial sensor data and follow-up inertial sensor data, and the indication of cognitive decline.

Method for assessing cognitive function and predicting cognitive decline through quantitative assessment of the TUG test

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.

Calculation of minimum ground clearance using body worn sensors

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.

Wireless sensor based quantitative falls risk assessment

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.

System and method for quantitative assessment of frailty

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.

Monitoring velocity and dwell trends from wireless sensor

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.

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Chapelizod, Ireland

Barry R Greene