USING CAPACITANCE TO DETECT TOUCH PRESSURE

• US 20170010732A1
• Filed: 07/09/2015
• Published: 01/12/2017
• Est. Priority Date: 07/09/2015
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Bosch Ruiz, Marc, Hofree, Galit, Oliver, Robyn Teresa, Tartz, Robert Scott, Kies, Jonathan Karl, Keating, Virginia Walker
• Granted Patent: US 10,459,561 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 2203/04111 Cross over in capacitive digitiser, i.e. details of structures for connecting electrodes of the sensing pattern where the connections cross each other, e.g. bridge structures comprising an insulating layer, or vias through substrate
• CPC Class Codes: G06F 2203/04808 Several contacts: gestures triggering a specific function, e.g. scrolling, zooming, right-click, when the user establishes several contacts with the surface simultaneously; e.g. using several fingers or a combination of fingers and pen
• CPC Class Codes: G06F 3/0412 Digitisers structurally integrated in a display
• CPC Class Codes: G06F 3/0416 Control or interface arrangements specially adapted for digitisers
• CPC Class Codes: G06F 3/04186 Touch location disambiguation
• CPC Class Codes: G06F 3/044 by capacitive means
• CPC Class Codes: G06F 3/0488 using a touch-screen or digitiser, e.g. input of commands through traced gestures
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text

Abstract

Aspects of the disclosure are related to a method for determining a touch pressure level on a touchscreen, comprising: detecting a touch event by the touchscreen; obtaining data relating to features associated with the touch event comprising a capacitance value, a touch area, and/or a touch duration; and determining a touch pressure level based on one or more of the features.

Claims

1. A method for determining a touch pressure level on a touchscreen, comprising:

detecting a touch event by the touchscreen;

obtaining data relating to features associated with the touch event comprising a capacitance value, a touch area, and/or a touch duration; and

determining a touch pressure level for the touch event based on one or more of the features.

2. The method of claim 1, further comprising normalizing the touch area.

3. The method of claim 1, wherein determining the touch pressure level further comprises:

classifying each feature using a feature classifier; and

classifying the touch pressure level using a majority vote rule classifier based on the feature classifications and weights associated with the features,

wherein for a particular user, a feature classifier and a weight is established for each feature based on training data associated with the particular user.

4. The method of claim 3, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

5. The method of claim 4, wherein the weight for at least one feature is zero.

6. The method of claim 3, wherein a confidence level associated with a risky touch pressure level is penalized in the feature classifier.

7. The method of claim 3, wherein establishing feature classifiers for a particular user further comprises:

receiving training data relating to a plurality of touch events associated with the user;

extracting feature data from the training data; and

establishing the feature classifiers based on the feature data.

8. The method of claim 3, further comprising:

adjusting the feature classifiers based on an application specification; and

classifying each feature using a respective adjusted feature classifier.

9. The method of claim 8, wherein the application specification comprises a touch pressure level detection granularity.

10. The method of claim 8, wherein the application specification comprises a risk associated with a particular touch pressure level.

11. The method of claim 1, wherein the determined touch pressure level is further used to determine a touch gesture.

12. An apparatus for determining a touch pressure level on a touchscreen, comprising:

a memory; and

a processor coupled to the memory, the processor to;

detect a touch event by the touchscreen,

obtain data relating to features associated with the touch event comprising a capacitance value, a touch area, and/or a touch duration, and

determine a touch pressure level for the touch event based on one or more of the features.

13. The apparatus of claim 12, wherein the processor is further to normalize the touch area.

14. The apparatus of claim 12, wherein the processor to determine the touch pressure level is further to:

classify each feature using a feature classifier, and

classify the touch pressure level using a majority vote rule classifier based on the feature classifications and weights associated with the features,

wherein for a particular user, a feature classifier and a weight is established for each feature based on training data associated with the particular user.

15. The apparatus of claim 14, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

16. The apparatus of claim 15, wherein the weight for at least one feature is zero.

17. The apparatus of claim 14, wherein a confidence level associated with a risky touch pressure level is penalized in the feature classifier.

18. The apparatus of claim 14, wherein the processor to establish feature classifiers for a particular user is further to:

receive training data relating to a plurality of touch events associated with the user,

extract feature data from the training data, and

establish the feature classifiers based on the feature data.

19. The apparatus of claim 14, wherein the processor is further to:

adjust the feature classifiers based on an application specification, and

classify each feature using a respective adjusted feature classifier.

20. The apparatus of claim 19, wherein the application specification comprises a touch pressure level detection granularity.

21. The apparatus of claim 19, wherein the application specification comprises a risk associated with a particular touch pressure level.

22. The apparatus of claim 12, wherein the determined touch pressure level is further used to determine a touch gesture.

23. An apparatus for determining a touch pressure level on a touchscreen, comprising:

means for detecting a touch event by the touchscreen;

means for obtaining data relating to features associated with the touch event comprising a capacitance value, a touch area, and/or a touch duration; and

means for determining a touch pressure level for the touch event based on one or more of the features.

24. The apparatus of claim 23, wherein the means for determining the touch pressure level further comprises:

means for classifying each feature using a feature classifier; and

means for classifying the touch pressure level using a majority vote rule classifier based on the feature classifications and weights associated with the features,

wherein for a particular user, a feature classifier and a weight is established for each feature based on training data associated with the particular user.

25. The apparatus of claim 24, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

26. The apparatus of claim 24, wherein the means for establishing feature classifiers for a particular user further comprises:

means for receiving training data relating to a plurality of touch events associated with the user;

means for extracting feature data from the training data; and

means for establishing the feature classifiers based on the feature data.

27. A non-transitory computer-readable medium comprising code which, when executed by a processor, causes the processor to perform a method comprising:

detecting a touch event by the touchscreen;

obtaining data relating to features associated with the touch event comprising a capacitance value, a touch area, and/or a touch duration; and

determining a touch pressure level for the touch event based on one or more of the features.

28. The non-transitory computer-readable medium of claim 27, wherein the code for determining the touch pressure level further comprises:

code for classifying each feature using a feature classifier; and

code for classifying the touch pressure level using a majority vote rule classifier based on the feature classifications and weights associated with the features,

wherein for a particular user, a feature classifier and a weight is established for each feature based on training data associated with the particular user.

29. The non-transitory computer-readable medium of claim 28, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

30. The non-transitory computer-readable medium of claim 28, wherein the code for establishing feature classifiers for a particular user further comprises:

code for receiving training data relating to a plurality of touch events associated with the user;

code for extracting feature data from the training data; and

code for establishing the feature classifiers based on the feature data.