Detecting body language via bone conduction

• US 10,108,984 B2
• Filed: 10/29/2013
• Issued: 10/23/2018
• Est. Priority Date: 10/29/2013
• Status: Active Grant

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Baldwin, Christopher, Amento, Brian S.
• Primary Examiner(s): Long, Fonya M
• Assistant Examiner(s): Shorter, Rashida
• Application Number:
  US14/065,663
• Publication Number:
  US 20150120465A1
• Time in Patent Office: 1,820 Days
• Field of Search: None
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06V 40/15 Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: H04L 2463/121 Timestamp cryptographic mechanisms or cryptographic arrangements involving time stamps H04L9/3297
• CPC Class Codes: H04L 63/0853 using an additional device, e.g. smartcard, SIM or a different communication terminal cryptographic mechanisms or cryptographic arrangements for entity authentication involving additional secure or trusted devices H04L9/3234
• CPC Class Codes: H04L 63/0861 using biometrical features, e.g. fingerprint, retina-scan cryptographic mechanisms or cryptographic arrangements for entity authentication using biological data H04L9/3231
• CPC Class Codes: H04L 63/1416 Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1466 Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
• CPC Class Codes: H04W 12/12 Detection or prevention of fraud
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Concepts and technologies are disclosed herein for detecting body language via bone conduction. According to one aspect, a device can detect body language of a user. The device can generate a signal and send the signal to a sensor network connected to a user. The device can receive a modified signal from the sensor network and compare the modified signal to a body language reference model. The device can determine the body language of the user based upon comparing the modified signal to the body language reference model.

Claims

1. A method comprising:

generating, by a device comprising a processor, a signal;

sending, by the device, the signal to a sensor network associated with a user, wherein the sensor network comprises a plurality of vibration sensors positioned at various locations on a body of the user;

receiving, at the device, a modified signal from the sensor network, wherein the modified signal comprises the signal received, from the device, by at least one vibration sensor of the plurality of vibration sensors and propagated by the at least one vibration sensor through at least one bone of the body of the user, thereby modifying the signal to create the modified signal;

selecting, by the device, a body language reference model to compare to the modified signal received from the sensor network, wherein the body language reference model identifies a plurality of body language features and associated signal features;

comparing, by the device, the modified signal to the body language reference model;

determining, by the device, based upon comparing the modified signal to the body language reference model, a signal feature of the modified signal that maps to a body language feature of the plurality of body language features identified in the body language reference model; and

outputting, by the device, the body language feature of the plurality of body language features identified in the body language reference model to an application.

2. The method of claim 1, wherein outputting, by the device, the body language feature to the application comprises outputting, by the device, the body language feature to a context-aware application.

3. The method of claim 2, further comprising:

receiving, at the context-aware application being executed by the processor, body language information associated with the body language feature;

analyzing, by the context-aware application, the body language information to determine a physical activity being performed by the user;

selecting, by the context-aware application, an advertisement appropriate for the physical activity being performed by the user; and

providing the advertisement to the user.

4. The method of claim 2, further comprising:

receiving, at the context-aware application being executed by the processor, body language information associated with the body language feature;

analyzing, by the context-aware application, the body language information to determine a mental state of the user;

selecting, by the context-aware application, an advertisement appropriate for the mental state of the user; and

providing the advertisement to the user.

5. The method of claim 2, further comprising:

receiving, at the context-aware application being executed by the processor, body language information associated with the body language feature;

analyzing, by the context-aware application, the body language information to determine an emotional state of the user;

selecting, by the context-aware application, an advertisement appropriate for the emotional state of the user; and

providing the advertisement to the user.

6. The method of claim 1, further comprising:

receiving, at the device, information associated with a physical attribute of the user;

creating, by the device, a body language profile based upon the information associated with the physical attribute of the user; and

causing, by the device, the body language profile to be stored;

and wherein selecting, by the device, the body language reference model to compare to the modified signal received from the sensor network comprises selecting, by the device, the body language reference model to compare to the modified signal received from the sensor network based, at least in part, upon the body language profile.

7. A device comprising:

a processor; and

a memory that stores computer-readable instructions that, when executed by the processor, cause the processor to perform operations comprising

generating a signal,

sending the signal to a sensor network associated with a user, wherein the sensor network comprises a plurality of vibration sensors positioned at various locations on a body of the user,

receiving a modified signal from the sensor network, wherein the modified signal comprises the signal received, from the device, by at least one vibration sensor of the plurality of vibration sensors and propagated by the at least one vibration sensor through at least one bone of the body of the user, thereby modifying the signal to create the modified signal,

selecting a body language reference model to compare to the modified signal received from the sensor network, wherein the body language reference model identifies a plurality of body language features and associated signal features,

comparing the modified signal to the body language reference model,

determining, based upon comparing the modified signal to the body language reference model, a signal feature of the modified signal that maps to a body language feature of the plurality of body language features identified in the body language reference model, and

outputting the body language feature of the plurality of body language features identified in the body language reference model to an application.

8. The device of claim 7, wherein outputting the body language feature to the application comprises outputting the body language feature to a context-aware application.

9. The device of claim 8, wherein the operations further comprise:

receiving body language information associated with the body language feature;

analyzing the body language information to determine a physical activity being performed by the user;

selecting an advertisement appropriate for the physical activity being performed by the user; and

providing the advertisement to the user.

10. The device of claim 8, wherein the operations further comprise:

receiving body language information associated with the body language feature;

analyzing the body language information to determine a mental state of the user;

selecting an advertisement appropriate for the mental state of the user; and

providing the advertisement to the user.

11. The device of claim 8, wherein the operations further comprise:

receiving body language information associated with the body language feature;

analyzing the body language information to determine an emotional state of the user;

selecting an advertisement appropriate for the emotional state of the user; and

providing the advertisement to the user.

12. The device of claim 7, wherein the operations further comprise:

receiving information associated with a physical attribute of the user;

creating a body language profile based upon the information associated with the physical attribute of the user; and

causing the body language profile to be stored;

and wherein selecting the body language reference model to compare to the modified signal received from the sensor network comprises selecting the body language reference model to compare to the modified signal based, at least in part, upon the body language profile.

13. A computer-readable storage medium having instructions stored thereon that, when executed by a processor, cause the processor to perform operations comprising:

generating a signal;

sending the signal to a sensor network associated with a user, wherein the sensor network comprises a plurality of vibration sensors positioned at various locations on a body of the user;

receiving a modified signal from the sensor network, wherein the modified signal comprises the signal received, from the device, by at least one vibration sensor of the plurality of vibration sensors and propagated by the at least one vibration sensor through at least a bone of the body of the user, thereby modifying the signal to create the modified signal;

selecting a body language reference model to compare to the modified signal received from the sensor network, wherein the body language reference model identifies a plurality of body language features and associated signal features;

comparing the modified signal to the body language reference model; and

determining, based upon comparing the modified signal to the body language reference model, a signal feature of the modified signal that maps to a body language feature of the plurality of body language features identified in the body language reference model; and

outputting the body language feature of the plurality of body language features identified in the body language reference model to an application.

14. The computer-readable storage medium of claim 13, wherein outputting the body language feature to the application comprises outputting the body language feature to a context-aware application.

15. The computer-readable storage medium of claim 14, wherein the operations further comprise:

receiving body language information associated with the body language feature;

analyzing the body language information to determine a physical activity being performed by the user;

selecting an advertisement appropriate for the physical activity being performed by the user; and

providing the advertisement to the user.

16. The computer-readable storage medium of claim 14, wherein the operations further comprise:

receiving body language information associated with the body language feature;

analyzing the body language information to determine a mental state of the user;

selecting an advertisement appropriate for the mental state of the user; and

providing the advertisement to the user.

17. The computer-readable storage medium of claim 14, wherein the operations further comprise:

receiving body language information associated with the body language feature;

analyzing the body language information to determine an emotional state of the user;

selecting an advertisement appropriate for the emotional state of the user; and

providing the advertisement to the user.