Activating applications based on accelerometer data

• US 8,438,373 B2
• Filed: 10/21/2011
• Issued: 05/07/2013
• 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.
• Primary Examiner(s): BUTLER, DENNIS
• Application Number:
  US13/278,445
• Publication Number:
  US 20120035881A1
• Time in Patent Office: 564 Days
• Field of Search: None
• US Class Current: 713/1
• 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
• 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:

accessing real-time acceleration data that is periodic in nature so as to represent periodic forces that were applied to a mobile device over a period of time;

identifying a dominant frequency of the real-time acceleration data;

selecting, from among multiple acceleration profiles that are accessible to the mobile device, an acceleration profile that stores acceleration data which matches the real-time acceleration data, wherein the selecting is based at least in part on the dominant frequency of the real-time acceleration data matching a dominant frequency of the acceleration data stored by the acceleration profile;

identifying one or more applications that are associated with the selected acceleration profile; and

invoking one or more of the identified applications.

2. The computer-implemented method of claim 1, further comprising identifying an average amplitude of the real-time acceleration data, wherein the selecting is further based at least in part on the average amplitude of the real-time acceleration data matching an average amplitude of the acceleration data stored by the acceleration profile.

3. The computer-implemented method of claim 1, wherein the periodic forces were applied by a vehicle to the mobile device and to a user carrying the mobile device.

4. The computer-implemented method of claim 1, further comprising analyzing raw acceleration data generated by one or more accelerometers on the mobile device to identify the real-time acceleration data as a portion of the raw acceleration data that is periodic in nature.

5. The computer-implemented method of claim 1, wherein:

the real-time acceleration data represents the periodic forces along a first set of multiple orthogonal axes; and

selecting the acceleration profile comprises normalizing the real-time acceleration data to represent the periodic forces along a different, second set of multiple orthogonal axes.

6. The computer-implemented method of claim 1, wherein,

the real-time acceleration data represents the periodic forces along more than one axis;

and

selecting the acceleration profile comprises selecting an acceleration profile that stores acceleration data that matches the real-time acceleration data along fewer than all of the more than one axis.

7. The computer-implemented method of claim 1, further comprising:

determining a current time of day; and

selecting one or more of the identified applications based on the current time of day,

wherein invoking one or more of the identified applications comprises invoking the selected applications.

8. The computer-implemented method of claim 1, further comprising:

determining a current location of the mobile device; and

selecting one or more of the identified applications based on the current location of the mobile device,

wherein invoking one or more of the identified applications comprises invoking the selected applications.

9. A non-transitory computer-readable device encoded with a computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising:

applying, while a mobile device remains in a training mode for a time period of multiple days, an algorithm that analyzes acceleration data generated by an accelerometer on the mobile device and activation of applications across the multiple days in order to correlate portions of the acceleration data to the applications;

storing the portions of acceleration data that are correlated to the applications;

accessing, while the mobile device is in an operating mode, real-time acceleration data, generated by the accelerometer, that is indicative of motions experienced by the mobile device;

determining that the real-time acceleration data is similar to a stored portion of the acceleration data; and

in response to determining that the real-time acceleration data is similar to the stored portion of the acceleration data, launching the application that is correlated to the stored portion of the acceleration data.

10. The computer-readable device of claim 9, wherein the real-time acceleration data that is generated by the accelerometer while the mobile device is in the operating mode is generated after the training mode time period of multiple days has ended.

11. The computer-readable device of claim 9, wherein:

the stored portion of the acceleration data represents forces applied to the mobile device when carried by a user that is walking, running, or traveling in a vehicle, and

the application that is correlated to the stored portion of the acceleration data comprises a music player application.

12. The computer-readable device of claim 9, further comprising:

accessing different, real-time acceleration data, generated by the accelerometer, that is indicative of different subsequent motions experienced by the mobile device; and

determining that the different, real-time acceleration data is similar to a different stored portion of the acceleration data,

wherein launching the application comprises launching an application that is correlated to both the stored portion of the acceleration data and the different stored portion of the acceleration data.

13. The computer-readable device of claim 12, further comprising:

determining a period of time between the motions and the different subsequent motions; and

determining that the period of time between the motions and the different subsequent motions satisfies a predetermined threshold,

wherein launching the application comprises launching the application only after determining that the period of time between the motions and the different subsequent motions satisfies the predetermined threshold.

14. A mobile device comprising:

a computer-readable device configured to;

store multiple applications, and

store one or more accelerometer signatures, wherein each accelerometer signature stores accelerometer data, and wherein each accelerometer signature is associated with a different end user activity and a different of the multiple applications;

an accelerometer configured to generate accelerometer data that measures one or more recent motions experienced by the mobile device; and

an application activation module configured to;

compare a dominant frequency of the generated accelerometer data to a dominant frequency of the accelerometer data stored in at least one of the one or more accelerometer signatures,

determine that a user of the mobile device is engaged in one or more of the end user activities based on comparing the dominant frequency of the generated accelerometer data to the dominant frequency of the accelerometer data that is stored in the at least one of the one or more accelerometer signatures,

select one or more of the multiple applications that are associated with the one or more of the end user activities, and

activate the selected one or more applications.

15. The device of claim 14, further comprising a display screen, wherein activating the one or more selected applications comprises giving the one or more selected applications active focus in a top layer of the display screen.

16. The device of claim 14, wherein:

the application activation module is configured to provide to the user a notification before activating the selected one or more applications,

wherein activating the selected one or more applications comprises activating the selected one or more applications after receiving a user input that is provided in response to the notification.

17. The device of claim 14, wherein the one or more of the end user activities that the user is determined to have engaged in includes riding in a vehicle.

18. A non-transitory computer-readable device encoded with a computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising:

accessing real-time acceleration data that is periodic in nature so as to represent periodic forces that were applied to a mobile device over a period of time;

identifying a dominant frequency of the real-time acceleration data;

selecting, from among multiple acceleration profiles that are accessible to the mobile device, an acceleration profile that stores acceleration data which matches the real-time acceleration data, wherein the selecting is based at least in part on the dominant frequency of the real-time acceleration data matching a dominant frequency of the acceleration data stored by the acceleration profile;

identifying one or more applications that are associated with the selected acceleration profile; and

invoking one or more of the identified applications.

19. The computer-readable device of claim 18, wherein the operations further comprise identifying an average amplitude of the real-time acceleration data, wherein the selecting is further based at least in part on the average amplitude of the real-time acceleration data matching an average amplitude of the acceleration data stored by the acceleration profile.

20. The computer-readable device of claim 18, wherein the periodic forces were applied by a vehicle to the mobile device and to a user carrying the mobile device.

21. The computer-readable device of claim 18, wherein the operations further comprise analyzing raw acceleration data generated by one or more accelerometers on the mobile device to identify the real-time acceleration data as a portion of the raw acceleration data that is periodic in nature.

22. The computer-readable device of claim 18, wherein:

the real-time acceleration data represents the periodic forces along a first set of multiple orthogonal axes; and

selecting the acceleration profile comprises normalizing the real-time acceleration data to represent the periodic forces along a different, second set of multiple orthogonal axes.

23. The computer-readable device of claim 18, wherein,

the real-time acceleration data represents the periodic forces along more than one axis;

and

selecting the acceleration profile comprises selecting an acceleration profile that stores acceleration data that matches the real-time acceleration data along fewer than all of the more than one axis.

24. The computer-readable device of claim 18, wherein the operations further comprise:

determining a current time of day; and

selecting one or more of the identified applications based on the current time of day,

wherein invoking one or more of the identified applications comprises invoking the selected applications.

25. The method of claim 18, wherein the operations further comprise:

determining a current location of the mobile device; and

selecting one or more of the identified applications based on the current location of the mobile device,

wherein invoking one or more of the identified applications comprises invoking the selected applications.

26. A computer-implemented method, comprising:

applying, while a mobile device remains in a training mode for a time period of multiple days, an algorithm that analyzes acceleration data generated by an accelerometer on the mobile device and activation of applications across the multiple days in order to correlate portions of the acceleration data to the applications;

storing the portions of acceleration data that are correlated to the applications;

accessing, while the mobile device is in an operating mode, real-time acceleration data, generated by the accelerometer, that is indicative of motions experienced by the mobile device;

determining that the real-time acceleration data is similar to a stored portion of the acceleration data; and

in response to determining that the real-time acceleration data is similar to the stored portion of the acceleration data, launching the application that is correlated to the stored portion of the acceleration data.

27. The computer-implemented method of claim 26, wherein the real-time acceleration data that is generated by the accelerometer while the mobile device is in the operating mode is generated after the training mode time period of multiple days has ended.

28. The computer-implemented method of claim 26, wherein:

the stored portion of the acceleration data represents forces applied to the mobile device when carried by a user that is walking, running, or traveling in a vehicle, and

the application that is correlated to the stored portion of the acceleration data comprises a music player application.

29. The computer-implemented method of claim 26, further comprising:

accessing different, real-time acceleration data, generated by the accelerometer, that is indicative of different subsequent motions experienced by the mobile device; and

determining that the different, real-time acceleration data is similar to a different stored portion of the acceleration data,

wherein launching the application comprises launching an application that is correlated to both the stored portion of the acceleration data and the different stored portion of the acceleration data.

30. The computer-implemented method of claim 29, further comprising:

determining a period of time between the motions and the different subsequent motions; and

determining that the period of time between the motions and the different subsequent motions satisfies a predetermined threshold,

wherein launching the application comprises launching the application only after determining that the period of time between the motions and the different subsequent motions satisfies the predetermined threshold.

31. A computer-implemented method, comprising:

storing multiple applications;

storing one or more accelerometer signatures, wherein each accelerometer signature stores accelerometer data, and wherein each accelerometer signature is associated with a different end user activity and a different of the multiple applications;

generating accelerometer data that measures one or more recent motions experienced by a mobile device;

comparing a dominant frequency of the generated accelerometer data to a dominant frequency of the accelerometer data stored in at least one of the one or more accelerometer signatures;

determining that a user of the mobile device is engaged in one or more of the end user activities based on comparing the dominant frequency of the generated accelerometer data to the dominant frequency of the accelerometer data that is stored in the at least one of the one or more accelerometer signatures;

selecting one or more of the multiple applications that are associated with the one or more of the end user activities; and

activating the selected one or more applications.

32. The computer-implemented method of claim 31, wherein activating the one or more selected applications comprises giving the one or more selected applications active focus in a top layer of a display screen.

33. The computer-implemented method of claim 31, further comprising providing to the user a notification before activating the selected one or more applications, and

wherein activating the selected one or more applications comprises activating the selected one or more applications after receiving a user input that is provided in response to the notification.

34. The computer-implemented method of claim 31, wherein the one or more of the end user activities that the user is determined to have engaged in includes riding in a vehicle.