Patents by Inventor Alan D. O'Donovan

Alan D. O'Donovan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • System and method for quantitative assessment of frailty
    Patent number: 9165113
    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.
    Type: Grant
    Filed: October 27, 2011
    Date of Patent: October 20, 2015
    Assignee: INTEL-GE CARE INNOVATIONS LLC
    Inventors: Barry R. Greene, Alan D. O'Donovan
  • SYSTEM AND METHOD FOR QUANTATIVE ASSESSMENT OF FRAILITY
    Publication number: 20130110475
    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.
    Type: Application
    Filed: October 27, 2011
    Publication date: May 2, 2013
    Applicant: INTEL-GE CARE INNOVATIONS LLC
    Inventors: Barry R. GREENE, Alan D. O'Donovan