Systems, articles, and methods for wearable human-electronics interface devices

• US 10,216,274 B2
• Filed: 06/18/2015
• Issued: 02/26/2019
• Est. Priority Date: 06/23/2014
• Status: Active Grant

• Current Assignee: META Platforms Technologies LLC (Meta Platforms, Inc. (f/k/a Facebook, Inc.))
• Original Assignee: North Inc.
• Inventors: Chapeskie, Jake, Lake, Stephen
• Primary Examiner(s): Lu, William
• Application Number:
  US14/743,536
• Publication Number:
  US 20150370326A1
• Time in Patent Office: 1,349 Days
• CPC Class Codes: G06F 1/163 Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 2200/1636 Sensing arrangement for detection of a tap gesture on the housing
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/014 Hand-worn input/output arrangements, e.g. data gloves
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883

Abstract

Systems, articles, and methods for wearable human-electronics interfaces are described. A wearable human-electronics interface device includes a band that in use is worn on an appendage (e.g., a wrist, arm, finger, or thumb) of a user. The band carries multiple sensors that are responsive to vibrations. The sensors are physically spaced apart from one another on or within the band. The band also carries an on-board processor. The sensors detect vibrations at the appendage of the user when the user performs different finger tapping gestures (i.e., tapping gestures involving different individual fingers or different combinations of fingers) and provide corresponding vibration signals to the processor. The processor classifies the finger tapping gesture(s) based on the vibration signals and an on-board transmitter sends a corresponding signal to control, operate, or interact with a receiving electronic device. The sensors include inertial sensors, digital MEMS microphones, or a combination thereof.

Claims

1. A method of operating a wearable human-electronics interface device, the device comprising a band that in use is worn around an appendage of a user, a set of microelectromechanical systems (“

MEMS”

) microphones carried by the band and physically spaced apart from one another, a processor carried by the band and communicatively coupled to each MEMS microphone in the set of MEMS microphones, a wireless transmitter carried by the band and communicatively coupled to the processor, and a vibration source carried by the band, wherein the method comprises;

detecting, by at least one MEMS microphone in the set of MEMS microphones, a vibration at the appendage of the user in response to a tapping gesture made by the user, the tapping gesture involving at least one finger of the user;

at least partially cancelling a background vibration at the appendage of the user, the background vibration caused by at least one ambient source other than the tapping gesture, by generating an interference vibration by the vibration source that destructively interferes with at least a portion of the background vibration at the appendage of the user;

providing at least one vibration signal from the at least one MEMS microphone that detects the vibration to the processor, wherein a composition of the at least one vibration signal depends at least in part on the at least one finger involved in the tapping gesture;

classifying, by the processor, the tapping gesture based at least in part on the at least one vibration signal; and

wirelessly transmitting at least one interfacial signal by the wireless transmitter based at least in part on classifying the tapping gesture by the processor.

2. The method of claim 1 wherein the human-electronics interface device further comprises at least one inertial sensor carried by the band and communicatively coupled to the processor, the method further comprising:

detecting, by the at least one inertial sensor, motion of the appendage of the user in response to the tapping gesture made by the user; and

providing at least one displacement signal from the at least one inertial sensor to the processor, wherein classifying, by the processor, the tapping gesture based at least in part on the at least one vibration signal includes classifying, by the processor, the tapping gesture based at least in part on both the at least one vibration signal and the at least one displacement signal.

3. The method of claim 2 wherein detecting, by the at least one inertial sensor, motion of the appendage of the user in response to the tapping gesture made by the user includes detecting at least one vibrational impulse at the appendage of the user caused by the tapping gesture.

4. The method of claim 1 wherein the wearable human-electronics interface device includes a non-transitory processor-readable storage medium carried by the band and communicatively coupled to the processor, and wherein the non-transitory processor-readable storage medium stores processor-executable tapping gesture classification instructions, the method further comprising:

executing the processor-executable tapping gesture classification instructions by the processor to cause the processor to classify the tapping gesture based at least in part on the at least one vibration signal.

5. The method of claim 1 wherein the set of MEMS microphones includes a first MEMS microphone and at least a second MEMS microphone, and wherein:

detecting, by at least one MEMS microphone in the set of sensors, a vibration at the appendage of the user in response to a tapping gesture made by the user includes detecting, by the first MEMS microphone and the at least a second MEMS microphone, the vibration at the appendage of the user;

providing at least one vibration signal from the at least one MEMS microphone that detects the vibration to the processor includes providing a first vibration signal from the first MEMS microphone to the processor and providing at least a second vibration signal from the at least a second MEMS microphone to the processor; and

classifying, by the processor, the tapping gesture based at least in part on the at least one vibration signal includes classifying, by the processor, the tapping gesture based at least in part on the first vibration signal and the at least a second vibration signal.

6. The method of claim 5 wherein classifying, by the processor, the tapping gesture based at least in part on the first vibration signal and the at least a second vibration signal includes at least one of:

determining, by the processor, relative amplitudes of the first vibration signal and the second vibration signal;

determining, by the processor, a temporal difference between at least one feature of the first vibration signal and at least one feature of the second vibration signal;

determining, by the processor, a phase difference between at least one feature of the first vibration signal and at least one feature of the second vibration signal;

comparing, by the processor, the first vibration signal and the at least a second vibration signal to at least one template that is representative of a defined tapping gesture; and

/or

executing, by the processor, a pattern recognition algorithm that classifies the tapping gesture based at least in part on the first vibration signal and the at least a second vibration signal.

7. The method of claim 1 wherein the set of MEMS microphones includes at least four MEMS microphones, at least one MEMS microphone positioned in each respective quadrant of a perimeter of the band when the band is worn around the appendage of the user, and wherein:

detecting, by at least one MEMS microphone in the set of MEMS microphones, a vibration at the appendage of the user in response to a tapping gesture made by the user includes detecting, by at least one respective MEMS microphone in each respective quadrant of the perimeter the band, the vibration at the appendage of the user in response to the tapping gesture made by the user.

8. The method of claim 1 wherein the set of MEMS microphones includes a set of non-contact, substantially omnidirectional MEMS microphones and wherein:

detecting, by at least one MEMS microphone in the set of MEMS microphones, a vibration at the appendage of the user in response to a tapping gesture made by the user includes detecting, by at least one non-contact, substantially omnidirectional MEMS microphone in the set of non-contact, substantially omnidirectional MEMS microphones, a vibration at the appendage of the user in response to a tapping gesture made by the user.

9. The method of claim 1 wherein detecting, by at least one MEMS microphone in the set of MEMS microphones, a vibration at the appendage of the user in response to a tapping gesture made by the user includes detecting at least one of:

a muscle vibration at the appendage of the user in response to the tapping gesture made by the user;

a tendon vibration at the appendage of the user in response to the tapping gesture made by the user;

a bioacoustic vibration at the appendage of the user in response to the tapping gesture made by the user; and

/or

a mechanical vibration at the appendage of the user in response to the tapping gesture made by the user.

10. The method of claim 1 wherein the appendage of the user around which the human-electronics interface device is, in use, worn is selected from a group consisting of:

an arm of the user, a wrist of the user, a finger of the user, and a thumb of the user.

11. The method of claim 1 wherein the human-electronics interface device further comprises a background vibration sensor carried by the band, the method further comprising:

detecting, by the background vibration sensor, the background vibration caused by the at least one ambient source.

12. A human-electronics interface device comprising:

a band that, in use, is worn around an appendage of a user;

a set of microelectromechanical systems (“

MEMS”

) microphones carried by the band, each MEMS microphone in the set of MEMS microphones physically spaced apart from the other MEMS microphones in the set of MEMS microphones, and each MEMS microphone in the set of MEMS microphones responsive to vibrations at the appendage of the user when the user performs a tapping gesture involving at least one finger;

a vibration source carried by the band;

a processor carried by the band and communicatively coupled to each MEMS microphone in the set of MEMS microphones;

a wireless transmitter carried by the band and communicatively coupled to the processor; and

a non-transitory processor-readable storage medium carried by the band and communicatively coupled to the processor, wherein the non-transitory processor-readable storage medium stores processor-executable tapping instructions that, when executed by the processor, cause the processor to cause the vibration source to at least partially cancel a background vibration at the appendage of the user, the background vibration caused by at least one ambient source other than the tapping gesture, by generating an interference vibration that destructively interferes with at least a portion of the background vibration at the appendage of the user.

13. The human-electronics interface device of claim 12, further comprising at least one inertial sensor carried by the band and communicatively coupled to the processor.

14. The human-electronics interface device of claim 12 wherein the non-transitory processor-readable storage medium stores processor-executable tapping gesture classification instructions that, when executed by the processor, cause the processor to classify the tapping gesture performed by the user based at least in part on at least one signal provided from at least one MEMS microphone in the set of MEMS microphones to the processor in response to the tapping gesture.

15. The human-electronics interface device of claim 12, further comprising:

a display unit carried by the band and communicatively coupled to the processor.

16. The human-electronics interface device of claim 12 wherein the set of MEMS microphones includes at least four MEMS microphones, at least one MEMS microphone positioned in each respective quadrant of a perimeter of the band when the band is worn around the appendage of the user.

17. The human-electronics interface device of claim 12 wherein the set of MEMS microphones includes a set of non-contact, substantially omnidirectional MEMS microphones.

18. The human-electronics interface device of claim 12 wherein each MEMS microphone in the set of MEMS microphones is responsive to at least one of:

a muscle vibration at the appendage of the user in response to the tapping gesture made by the user;

a tendon vibration at the appendage of the user in response to the tapping gesture made by the user;

a bioacoustic vibration at the appendage of the user in response to the tapping gesture made by the user; and

/or

a mechanical vibration at the appendage of the user in response to the tapping gesture made by the user.

19. The human-electronics interface device of claim 12 wherein the band is sized and dimensioned to, in use, be worn around an appendage of the user selected from a group consisting of:

an arm of the user, a wrist of the user, a finger of the user, and a thumb of the user.

20. The human-electronics interface device of claim 12 wherein the set of MEMS microphones includes at least eight MEMS microphones evenly spaced around a circumference of the band.

21. The human-electronics interface device of claim 12, further comprising a background vibration sensor carried by the band, the background vibration sensor responsive to the background vibration caused by the at least one ambient source.

22. The human-electronics interface device of claim 21, wherein the background vibration sensor is selected from the group consisting of:

a MEMS microphone in the set of MEMS microphones, or a dedicated outward-facing sensor carried by the band.