DETECTING USER ACTIVITY BASED ON LOCATION DATA

• US 20180349728A1
• Filed: 06/02/2017
• Published: 12/06/2018
• Est. Priority Date: 06/02/2017
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Wang, Lei, Miller, Isaac Thomas, Bae, Hyojoon, Selle, Christina, MacGougan, Glenn Donald
• Granted Patent: US 10,451,744 B2
• Time in Patent Office: Days
• CPC Class Codes: G01S 19/19 Sporting applications
• CPC Class Codes: G01S 19/485 whereby the further system is an optical system or imaging system
• CPC Class Codes: G01S 5/017 Detecting state or type of motion
• CPC Class Codes: G01S 5/0294 Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 7/248 involving reference images or patches
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters
• CPC Class Codes: G06V 10/44 Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
• CPC Class Codes: G06V 10/751 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training

Abstract

The disclosed embodiments are directed to detecting a user activity based on patterns in location data. In an embodiment, a method comprises: obtaining, by a processor of a computing device, location data; detecting, by the processor, a pattern in the location data; determining, by the processor and based on the detected pattern, an activity associated with a mobile device; and adjusting, by the processor, one or more parameters of a location data filter configured to process the location data.

Claims

1. A method comprising:

obtaining, by a processor of a computing device, location data;

detecting, by the processor, a pattern in the location data;

determining, by the processor and based on the detected pattern, an activity associated with a mobile device; and

adjusting, by the processor, one or more parameters of a location data filter configured to process the location data.

2. The method of claim 1, wherein detecting a pattern in the location data comprises:

detecting a loop in the location data.

3. The method of claim 2, wherein detecting a loop in the location data comprises:

traversing through a sequence of the location data at different rates; and

comparing, at each step in the sequence, location data that corresponds to respective locations in the sequence using an equivalence threshold.

4. The method of claim 3, wherein the equivalence threshold is determined at least by location speed and location uncertainty.

5. The method of claim 2, wherein detecting the loop in the location data further comprises:

determining a center or centroid of a subset of the location data included in the loop;

calculating a distance of each location position data in the subset of location position data from the center or centroid;

determining a percentage of the subset of location data that falls within a minimum and maximum boundary determined by dimensions of a reference running track; and

determining that the percentage of location data that falls within the minimum and maximum boundaries exceeds a specified threshold value.

6. The method of claim 1, wherein adjusting one or more parameters of the location data filter further comprises adjusting one or more parameters of the location filter that determine an amount of smoothing of location data by the location data filter.

7. The method of claim 6, wherein the location data filter is a Bayesian filter and the one or more parameters are process noise.

8. A method comprising:

generating, by one or more processors of a computing device, an image of location data;

determining, by the one or more processors, a contour in the image, the contour representing an activity path associated with a mobile device;

determining, by the one or more processors, a path descriptor for the activity path;

matching, by the one or more processors, the path descriptor with one of a plurality of reference path descriptors;

responsive to the matching, adjusting, by the one or more processors, one or more parameters of a location data filter; and

filtering, by the location data filter, the location data.

9. The method of claim 8, wherein the path descriptor and reference path descriptors are feature vectors that include a plurality of image moment invariants.

10. The method of claim 8, wherein the binary image is morphed before determining the contour in the image.

11. A system comprising:

one or more processors;

memory coupled to the one or more processors and configured to store instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising;

obtaining location data;

detecting a pattern in the location data;

determining an activity associated with a mobile device based on the detected pattern; and

adjusting one or more parameters of a location data filter configured to process the location data.

12. The system of claim 11, wherein detecting a pattern in the location data comprises:

detecting a loop in the location data.

13. The system of claim 12, wherein detecting a loop in the location data comprises:

traversing through a sequence of the location data at different rates; and

comparing, at each step in the sequence, location data that corresponds to respective locations in the sequence using an equivalence threshold.

14. The system of claim 13, wherein the equivalence threshold is determined at least by location speed and location uncertainty.

15. The system of claim 12, wherein detecting the loop in the location data further comprises:

determining a center or centroid of a subset of the location data included in the loop;

calculating a distance of each location data in the subset of location data from the center or centroid;

determining a percentage of the subset of location data that falls within a minimum and maximum boundary determined by dimensions of a reference running track; and

determining that the percentage of location data that falls within the minimum and maximum boundaries exceeds a specified threshold value.

16. The system of claim 11, wherein adjusting one or more parameters of the location data filter further comprises adjusting one or more parameters of the location filter that determine an amount of smoothing of location data by the location data filter.

17. The system of claim 16, wherein the location data filter is a Bayesian filter and the one or more parameters are process noise.

18. A system comprising:

one or more processors;

memory coupled to the one or more processors and configured to store instructions, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising;

generating an image of location data;

determining a contour in the image, the contour representing an activity path associated with a mobile device;

determining a path descriptor for the activity path;

matching the path descriptor with one of a plurality of reference path descriptors;

responsive to the matching, adjusting, by the one or more processors, one or more parameters of a location data filter; and

filtering, by the location data filter, the location data.

19. The system of claim 18, wherein the path descriptor and reference path descriptors are feature vectors that include a plurality of image moment invariants.

20. The system of claim 18, wherein the binary image is morphed before determining the contour in the image.