Activating Applications Based on Accelerometer Data

• US 20090132197A1
• Filed: 11/10/2008
• Published: 05/21/2009
• Est. Priority Date: 11/09/2007
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Rubin, Andrew E., Conway, David P.
• Granted Patent: US 8,065,508 B2
• Time in Patent Office: Days
• US Class Current: 702/141
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: G01C 21/12 executed aboard the object being navigated; Dead reckoning
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G06F 1/1694 the I/O peripheral being a single or a set of motion sensors for pointer control or gesture input obtained by sensing movements of the portable computer
• CPC Class Codes: G06F 15/177 Initialisation or configuration control processor initialisation G06F9/4405
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• 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 1/72448 with means for adapting the functionality of the device according to specific conditions
• CPC Class Codes: H04M 1/72451 according to schedules, e.g. using calendar applications
• CPC Class Codes: H04M 1/72457 according to geographic location
• CPC Class Codes: H04M 2250/10 including a GPS signal receiver
• CPC Class Codes: H04M 2250/12 including a sensor for measuring a physical value, e.g. temperature or motion telephones including GPS signal receivers H04M2250/10
• CPC Class Codes: H04W 4/00 Services specially adapted for wireless communication networks; Facilities therefor
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information

Abstract

In some implementations, a computer-implemented method includes storing a plurality of acceleration profiles in a mobile device; receiving accelerometer data from an accelerometer in the mobile device; correlating the accelerometer data with one accelerometer profile in the plurality of accelerometer profiles; and activating a user application of the mobile device that is associated with the correlated accelerometer profile. Each acceleration profile can correspond to a sequence of acceleration forces a mobile device would be subjected to when carried with a user during an activity that corresponds to the correlated acceleration profile.

Claims

1. A computer-implemented method comprising:

storing a plurality of acceleration profiles, each acceleration profile corresponding to a sequence of acceleration forces a mobile device would be subjected to when carried with a user during one of a plurality of activities corresponding to the plurality of acceleration profiles;

receiving accelerometer data from an accelerometer in the mobile device;

correlating the accelerometer data with one accelerometer profile in the plurality of accelerometer profiles; and

activating a user application of the mobile device that is associated with the correlated one accelerometer profile.

2. The computer-implemented method of claim 1, wherein at least one sequence of acceleration forces is predicted based on analysis of a plurality of devices that are distinct from the mobile device.

3. The computer-implemented method of claim 2, wherein the at least one sequence is pre-stored in the mobile device before the mobile device is delivered to a corresponding user.

4. The computer-implemented method of claim 2, wherein the at least one sequence is wirelessly downloaded from a content provider to the mobile device

5. The computer-implemented method of claim 1, wherein at least one sequence of acceleration forces is stored by the mobile device during a training mode in which actual acceleration forces to which the mobile device is subjected are sampled in real-time and stored in the mobile device.

6. The computer-implemented method of claim 1, wherein correlating comprises sampling the received accelerometer data and comparing the sampled accelerometer data to one or more of the plurality of acceleration profiles using a comparison algorithm to determine if the sampled accelerometer data matches an acceleration profile in the plurality of acceleration profiles.

20. The computer-implemented method of claim 1, wherein storing the plurality of acceleration profiles comprises storing the plurality of acceleration profiles at a network-accessible location external to the mobile device;

wherein correlating the accelerometer data comprises accessing the network-accessible location after receiving the accelerometer data.

21. The computer-implemented method of claim 1, wherein correlating the accelerometer data comprises processing the accelerometer data to remove effects of positional orientation of the mobile device.

22. The computer-implemented method of claim 21, wherein processing the accelerometer data comprises translating multi-axis accelerometer data to a different coordinate system than a coordinate system in which the mutely-axis data was captured.

7. A method of automatically activating an application in a mobile device that runs a plurality of applications, the method comprising:

receiving accelerometer data from an accelerometer in a mobile device;

generating a signature based on the received accelerometer data;

correlating the generated signature to an acceleration profile that is stored in the mobile computing device; and

activating an application in the mobile computer device that is associated with the acceleration profile to which the generated signature is correlated.

8. The method of claim 7, wherein correlating comprises comparing the generated signature to one or more acceleration profiles that are stored in the mobile computing device and determining when the generated signature and one of the one or more acceleration profiles match within a threshold measure of similarity.

9. The method of claim 7, further comprising, in a training mode:

receiving training acceleration data for the mobile computing device;

receiving user input to launch an application in the plurality of applications;

generating one or more training signatures based on the received acceleration data and the user input; and

receiving additional acceleration data for the mobile computing device and additional user input to launch the application, and adaptively refining the one or more training signatures.

10. The method of claim 9, wherein receiving training acceleration data comprises receiving data from an accelerometer included in the mobile computing device, and receiving acceleration data also comprises receiving data from the accelerometer.

11. The method of claim 10, wherein the training acceleration data comprises acceleration data for the mobile computing device in at least two axes.

12. The method of claim 11, wherein receiving training acceleration data comprises periodically receiving the acceleration data for the mobile computing device at a rate of at least 25 Hz.

13. The method of claim 9, further comprising storing received training acceleration data in a first-in, first-out circular buffer such that a predetermined amount of recently received training acceleration data is stored at a given time.

14. The method of claim 9, wherein the training signature is further generated based on a time at which the user input is received.

15. The method of claim 14, wherein the training signature is further generated based on a day of the week on which the user input is received.

16. The method of claim 9, wherein the training signature is further generated based on a location of the mobile computing device when the user input is received.

17. The method of claim 9, further comprising generating a first training signature based on a training acceleration data and user input to the activate the application that are received at a first time, and generating a second, distinct training signature based on training acceleration data and user input to launch the specific application that are received at a second time that is different than the first time by at least a predetermined threshold.

18. The method of claim 9, wherein generating the training signature comprises generating the training signature based on training acceleration data that is received for a predetermined period of time prior to receiving the user input to launch the specific application.

19. The method of claim 18, wherein generating the training signature further comprises generating the training signature based on training acceleration data that is received for a second predetermined period of time following receiving the user input to launch the specific application.

23. A method of automatically activating an application in a mobile device that runs a plurality of applications, the method comprising:

receiving accelerometer data from an accelerometer in a mobile device;

generating a signature based on the received accelerometer data;

correlating the generated signature to an existing acceleration profile; and

activating an application in the mobile computer device that is associated with the acceleration profile to which the generated signature is correlated.

24. The method of claim 23, wherein correlating comprises comparing the generated signature to one or more existing acceleration profiles that are stored in a network-accessible device that is external to the mobile device.

25. The method of claim 23, further comprising receiving confirmation input from a user of mobile device prior to activating the application.