Non-parametric modeling apparatus and method for classification, especially of activity state

• US 8,478,542 B2
• Filed: 10/06/2010
• Issued: 07/02/2013
• Est. Priority Date: 06/17/2005
• Status: Active Grant

• Current Assignee: Prolaio, Inc.
• Original Assignee: Venture Gain LLC
• Inventors: Mott, Jack E.
• Primary Examiner(s): Hindenburg, Max
• Assistant Examiner(s): DANEGA, RENEE A
• Application Number:
  US12/898,883
• Publication Number:
  US 20110029250A1
• Time in Patent Office: 1,000 Days
• Field of Search: 702/19, 600/300
• US Class Current: 702/19
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The activity state classification method of the present invention employs a kernel-based modeling technique, and more specifically a set of similarity-based models, which have been created using example data, to process an input observation or set of input observations, each comprising a set of sensor readings or “features” derived there from or other data, to predict the activity state of a person from whom the sensor data was obtained. A model is created for each class of activity. The input data is processed by each model and the resulting predictions are combined to yield a final prediction of which state of activity is represented by the input data.

Claims

1. A method for determining the class of an activity of a person, selected from a plurality of activities of interest, based on multivariate sensor data comprising values or features derived from values of sensors measuring physiological parameters from the person, comprising the steps of:

providing a plurality of kernel-based models, each corresponding to one of said activities of interest, and each model comprising a plurality of reference observations of said multivariate sensor data, at least some said reference observations having been acquired from sensors on a person during a modeled activity to which said model corresponds, and at least some said reference observations having been acquired from sensors on a person during activity different from that to which said model corresponds, and all said reference observations further having a class membership value corresponding to whether said reference observation is of said modeled activity of not;

obtaining a new observation of readings of said multivariate sensor data;

generating in a computer processor an inferential estimate of said class membership value for each of at least some of said kernel-based models using said new observation as input to the at least some of said kernel-based models, where said inferential estimate for a given kernel-based model is generated from a linear combination of at least some of said reference observations of said given kernel-based model; and

determining the class of activity corresponding to said new observation based on a comparison of the class membership estimates.

2. A method according to claim 1, wherein at least one of said kernel-based models is a similarity-based model.

3. A method according to claim 2, further comprising the step of determining a class of activity of a window of successive observations in time to be the class most often represented among the successive observations.

4. A method according to claim 3, wherein said sensor data includes a measure of heart rate.

5. A method according to claim 3, wherein said sensor data includes a measure of temperature of the person.

6. A method according to claim 3, wherein said sensor data includes a measure of respiration.

7. A method according to claim 3, further comprising the step of filtering the window of successive observations as an input to a medical diagnosis based on the determination of the class of the window.

8. A method according to claim 3, wherein the computer processor used to generate the inferential estimate of said class membership value is embedded in a wearable hardware and further comprising the step of selectively initiating an action by said hardware upon determination of the class of activity corresponding to said window of successive observations.

9. A method according to claim 3, wherein said new observation is used to localize each said kernel-based model so that each said inferential estimate is generated from a linear combination of just a subset of reference observations of each said kernel-based model.

10. A method according to claim 9, wherein said subset is determined by a kernel-based similarity comparison of said new observation with said reference observations.

11. A method according to claim 10, wherein said kernel-based similarity comparison of said new observation with said reference observations marks for inclusion in said subset a predetermined number of those reference observations that have a highest similarity value when compared with said new observation.

12. A method according to claim 10, wherein said kernel-based similarity comparison of said new observation with said reference observations marks for inclusion in said subset those reference observations that have a similarity value above a predetermined threshold, and that further either have the highest similarity value with the new observation among a set of all reference observations that have a selected sensor data value less than the corresponding sensor data value of said new observation, or have the highest similarity value with the new observation among a set of all reference observations that have said selected sensor data value greater than the corresponding sensor data value of said new observation.

13. A method according to claim 1, wherein at least one of said kernel-based models is a kernel regression model.

14. A method according to claim 13, further comprising the step of determining a class of activity of a window of successive observations in time to be the class most often represented among the successive observations.

15. A method according to claim 14, wherein said sensor data includes a measure of heart rate.

16. A method according to claim 14, wherein said sensor data includes a measure of temperature of the person.

17. A method according to claim 14, wherein said sensor data includes a measure of respiration.

18. A method according to claim 1, further comprising the step of filtering said new observation as an input to a medical diagnosis based on the determination the class of activity.

19. A method according to claim 1, wherein the computer processor used to generate the inferential estimate of said class membership value is embedded in a wearable hardware and further comprising the step of selectively initiating an action by said hardware upon determination of the class of activity corresponding to said new observation.