Method and apparatus for a virtual accelerometer system

• US 7,970,586 B1
• Filed: 01/11/2010
• Issued: 06/28/2011
• Est. Priority Date: 07/11/2006
• Status: Active Grant

• Current Assignee: DP Technologies, Inc.
• Original Assignee: DP Technologies, Inc.
• Inventors: Kinsolving, Arthur, Kahn, Philippe
• Primary Examiner(s): Cosimano; Edward R
• Application Number:
  US12/685,648
• Time in Patent Office: 533 Days
• Field of Search: 734/88, 734/89, 734/90, 734/91, 735/041.8, 735/10, 702/1, 702/127, 702/141, 702/155, 702/158, 702/160, 702/187, 702/189, 709/230, 709/231, 709/236, 718/1, 718/100, 719/310, 719/319, 719/328, 719/329, 719/330
• US Class Current: 702/189
• CPC Class Codes: A63F 13/10 Control of the course of the game, e.g. start, progess, end
• CPC Class Codes: A63F 13/211 using inertial sensors, e.g. accelerometers or gyroscopes
• CPC Class Codes: A63F 13/45 Controlling the progress of the video game
• CPC Class Codes: A63F 13/818 Fishing
• CPC Class Codes: A63F 2300/105 using inertial sensors, e.g. accelerometers, gyroscopes
• CPC Class Codes: A63F 2300/204 the platform being a handheld device
• CPC Class Codes: A63F 2300/209 characterized by low level software layer, relating to hardware management, e.g. Operating System, Application Programming Interface
• CPC Class Codes: A63F 2300/6045 for mapping control signals received from the input arrangement into game commands
• CPC Class Codes: A63F 2300/8035 Virtual fishing
• CPC Class Codes: G01C 22/006 Pedometers

Abstract

A method and apparatus for a virtual accelerometer is described. The virtual accelerometer includes an active process identifier engine to identify any active application that may use accelerometer data. The virtual accelerometer also includes an intelligent signal interpretation and separation (ISIS) engine to separate raw accelerometer data for each of the active applications that use the accelerometer data. Additionally, the virtual accelerometer includes a universal application program interface (API) to provide separated accelerometer data to an application.

Claims

1. A method comprising:

receiving accelerometer data from a real accelerometer by a processor executing a virtual accelerometer;

identifying, by the processor, a first active application that uses a first subset of the accelerometer data;

separating out the first subset from the received accelerometer data; and

providing the first subset to the first active application.

2. The method of claim 1, further comprising:

identifying a second active application that uses a second subset of the accelerometer data;

separating out the second subset from the received accelerometer data; and

providing the second subset to the second active application.

3. The method of claim 1, further comprising:

calibrating the first active application based on generating a gesture set from the accelerometer data while there are no other active applications that use any subsets of the accelerometer data.

4. The method of claim 1, further comprising:

selecting a sample rate for the real accelerometer based on the first active application and any additional active applications.

5. The method of claim 1, further comprising:

powering down the real accelerometer if there are no active applications that use any subsets of the accelerometer data.

6. The method of claim 1, wherein the first active application includes a first gesture set that is used to identify the first subset.

7. The method of claim 6, further comprising:

detecting a user activity level from the accelerometer data; and

adjusting the first gesture set based on the user activity level.

8. The method of claim 6, further comprising:

detecting a user activity level from the accelerometer data;

determining whether the user activity level exceeds an activity level threshold; and

when the user activity level exceeds the activity level threshold, disabling the first gesture set.

9. The method of claim 6, wherein the first gesture set includes a motion sequence, the motion sequence indicating an expected motion based on previously received motions.

10. The method of claim 6, further comprising:

detecting a level of ambient noise from the accelerometer data; and

adjusting the first gesture set based on the detected level of ambient noise.

11. A handheld device comprising:

a real accelerometer to obtain motion data;

a memory including instructions for a virtual accelerometer; and

a processor, to execute the instructions, wherein the instructions cause the processor to;

receive the motion data from the real accelerometer;

identify a first active application that uses a first subset of the accelerometer data;

separate out the first subset from the received accelerometer data; and

provide the first subset to the first active application.

12. The handheld device of claim 11, further comprising the instructions to cause the processor to:

identify a second active application that uses a second subset of the accelerometer data;

separate out the second subset from the received accelerometer data; and

provide the second subset to the second active application.

13. The handheld device of claim 11, further comprising the instructions to cause the processor to:

calibrate the first active application based on generating a gesture set from the accelerometer data while there are no other active applications that use any subsets of the accelerometer data.

14. The handheld device of claim 11, further comprising the instructions to cause the processor to:

select a sample rate for the real accelerometer based on the first active application and any additional active applications.

15. The handheld device of claim 11, further comprising the instructions to cause the processor to:

power down the real accelerometer if there are no active applications that use any subsets of the accelerometer data.

16. The handheld device of claim 11, wherein the first active application includes a first gesture set that is used to identify the first subset.

17. The handheld device of claim 16, further comprising the instructions to cause the processor to:

detect a user activity level from the accelerometer data; and

adjust the first gesture set based on the user activity level.

18. The handheld device of claim 16, further comprising the instructions to cause the processor to:

detect a user activity level from the accelerometer data;

determine whether the user activity level exceeds an activity level threshold; and

disable the first gesture set when the user activity level exceeds the activity level threshold.

19. The handheld device of claim 16, wherein the first gesture set includes a motion sequence, the motion sequence indicating an expected motion based on previously received motions.

20. The handheld device of claim 16, further comprising the instructions to cause the processor to:

detect a level of ambient noise from the accelerometer data; and

adjust the first gesture set based on the detected level of ambient noise.