User activity tracking system

• US 9,531,824 B2
• Filed: 07/22/2015
• Issued: 12/27/2016
• Est. Priority Date: 06/28/2013
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Pennanen, Juho, Kyrl, Aapo, Partanen, Jukka
• Primary Examiner(s): Eisner, Ronald
• Application Number:
  US14/806,449
• Publication Number:
  US 20150326709A1
• Time in Patent Office: 524 Days
• Field of Search: 4554141-4143, 455418-420, 455/404.1, 455/404.2, 4554561-457, 340/539.13, 340988-996
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/103 Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes A61B5/08 takes precedence; measuring aids for tailors A41H1/00; measuring instruments specially adapted for dentistry A61C19/04
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: G01S 19/19 Sporting applications
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04M 1/72454 according to context-related or environment-related conditions
• CPC Class Codes: H04M 1/72457 according to geographic location
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/70 Services for machine-to-machine communication [M2M] or machine type communication [MTC]

Abstract

In one embodiment, sensor signals corresponding to motions of a computing device are determined. Activities of a user corresponding to the computing device are determined by selecting activity types which are based on the sensor signals, a set of user characteristics associated with the user, a classification of the set of user characteristics, and signal parameters. A reference to the selected activity types is displayed to the user. User input indicating whether the reference is correct is received. Based on the user input, the signal parameters may be adjusted.

Claims

1. A method comprising:

by a computing device, determining one or more sensor signals corresponding to one or more motions of the computing device;

by the computing device, determining one or more activities of a user, wherein;

the user corresponds to the computing device;

the user is associated with a set of user characteristics; and

the determining comprises selecting one or more activity types, wherein the one or more activity types correspond to a particular time period, and the selecting is based on;

the sensor signals;

one or more classifications corresponding to the user characteristics; and

one or more signal parameters;

by the computing device, displaying to the user a reference to the activity types;

by the computing device, receiving user input indicating whether the reference is correct; and

by the computing device, determining whether to adjust the signal parameters, wherein the determining is based on the user input.

2. The method of claim 1, wherein the one or more classifications of the set of user characteristics comprises:

a demographic classification of the user;

a device type of the computing device;

an activity level of the user;

or

one or more groups associated with the user.

3. The method of claim 2, wherein the demographic classification of the user comprises:

gender;

age;

or

place of living.

4. The method of claim 2, wherein the determining further comprises one or more activity types associated with sensor signals of a plurality of other users, wherein the other users each correspond to user characteristics sharing one or more classifications with the set of user characteristics.

5. The method of claim 1, wherein the determining further comprises weighing a portion of the sensor signals specific to the user compared to a portion of the sensor signals matching an average sensor signal from a plurality of users.

6. The method of claim 1, wherein the reference comprises a timeline of activity periods experienced by the computing device.

7. The method of claim 6, wherein the timeline comprises:

an indication of the activity types for each activity period; and

one or more locations associated with each activity period.

8. The method of claim 1, wherein a first processing module of the computing device determines the one or more activity types, and a second processing module of the computing device determines location and movement information corresponding to each of the one or more activity types.

9. The method of claim 1, wherein the computing device comprises a handheld mobile communication device.

10. The method of claim 1, wherein the sensor signals are provided by one or more sensors of the computing device, the sensors comprising:

a gyroscopic angular sensor;

an accelerometer;

a GPS position sensor;

a radio network based position sensor;

a magnetometer;

a microphone;

a camera;

or

a temperature sensor.

11. The method of claim 1, wherein selecting the activity types comprises:

a supervised or semisupervised classification analysis of the sensor signals;

or

a heuristic analysis of the sensor signals.

12. The method of claim 11, wherein the supervised or semisupervised classification analysis comprises a classification algorithm to measure one or more frequency components of the information included in the one or more sensor signals for determining the amplitudes of the one or more frequency components at different points in time, wherein the amplitudes are used as input parameters in the supervised classification analysis or heuristic analysis.

13. The method of claim 1, wherein the signal parameters are adjusted by a machine learning algorithm based on the indication.

14. The method of claim 1, wherein the indication comprises a correction of the reference by the user, and the signal parameters are adjusted based on the correction.

15. The method of claim 14, wherein the correction comprises a correction to the location associated with an activity period of the reference.

16. The method of claim 14, wherein the correction comprises a correction to the activity types corresponding to an activity period of the reference.

17. The method of claim 14, wherein the correction comprises a correction to the time period corresponding to an activity period of the reference.

18. The method of claim 1, wherein if the indication comprises data indicating that the user has viewed the reference, but no feedback has been received from the user, then the reference is determined to be correct.

19. A system comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to;

determine one or more sensor signals corresponding to one or more motions of the system;

determine one or more activities of a user, wherein;

the user corresponds to the system;

the user is associated with a set of user characteristics; and

the determining comprises selecting one or more activity types, wherein the one or more activity types correspond to a particular time period, and the selecting is based on;

the sensor signals;

one or more classifications corresponding to the user characteristics; and

one or more signal parameters;

display to the user a reference to the activity types;

receive user input indicating whether the reference is correct; and

determine whether to adjust the signal parameters, wherein the determining is based at least in part on the user input.

20. One or more computer readable non-transitory storage media embodying software, the software being operable when executing on a computing device to:

determine one or more sensor signals corresponding to one or more motions of the computing device;

determine one or more activities of a user, wherein;

the user corresponds to the computing device;

the user is associated with a set of user characteristics; and

the determining comprises selecting one or more activity types, wherein the one or more activity types correspond to a particular time period, and the selecting is based on;

the sensor signals;

one or more classifications corresponding to the user characteristics and

one or more signal parameters;

display to the user a reference to the activity types;

receive user input indicating whether the reference is correct; and

determine whether to adjust the signal parameters, wherein the determining is based at least in part on the user input.