Using capacitance to detect touch pressure

• US 10,459,561 B2
• Filed: 07/09/2015
• Issued: 10/29/2019
• Est. Priority Date: 07/09/2015
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Bosch Ruiz, Marc, Hofree, Galit, Tartz, Robert Scott, Oliver, Robyn Teresa, Kies, Jonathan Karl, Keating, Virginia Walker
• Primary Examiner(s): Edwards, Carolyn R
• Application Number:
  US14/795,699
• Publication Number:
  US 20170010732A1
• Time in Patent Office: 1,573 Days
• Field of Search: 345174
• 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, the features comprising an average capacitance value of the touch event, a touch area of the touch event, and a touch duration of the touch event, wherein the average capacitance value is based on a plurality of capacitance values of the touch event; and

determining a touch pressure level for the touch event based on one or more of the features, wherein for each of the one or more features, a respective feature classifier and weight is established for a particular user based on training data associated with the particular user,

wherein determining the touch pressure level comprises;

classifying each of the one or more of the features using the respective feature classifier for the particular user to provide a corresponding confidence level for each of a plurality of different touch pressure levels; and

estimating the touch pressure level using a majority vote rule classifier based on the confidence levels provided by the feature classifiers and the weights associated with the one or more features for the particular user.

2. The method of claim 1, further comprising normalizing the touch area by adjusting a height of the touch area relative to a width of the touch area.

3. The method of claim 1, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

4. The method of claim 3, wherein the weight for at least one feature is zero.

5. The method of claim 1, wherein a confidence level associated with a risky touch pressure level is penalized in the feature classifier.

6. The method of claim 1, wherein establishing feature classifiers for a particular user 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.

7. The method of claim 1, further comprising adjusting the respective feature classifiers, based on an application specification of an application that is currently in use, to obtain respective adjusted feature classifiers,

wherein the classifying each feature of the one or more of the features is performed using the respective adjusted feature classifiers,

wherein the application specification includes at least one value that specifies a configuration setting.

8. The method of claim 7, wherein the application specification specifies a touch pressure level detection granularity.

9. The method of claim 7, wherein the application specification specifies a risk associated with a particular touch pressure level.

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

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

a memory; and

a processor coupled to the memory, the processor configured to;

detect a touch event by the touchscreen,

obtain data relating to features associated with the touch event, the features comprising an average capacitance value of the touch event, a touch area of the touch event, and a touch duration of the touch event, wherein the average capacitance value is based on a plurality of capacitance values of the touch event;

and

determine a touch pressure level for the touch event based on one or more of the features, wherein for each of the one or more features, a respective feature classifier and weight is established for a particular user based on training data associated with the particular user,

wherein the processor configured to determine the touch pressure level is configured to;

classify each feature of the one or more of the features using the respective feature classifier for the particular user to provide a corresponding confidence level for each of a plurality of different touch pressure levels, and

estimate the touch pressure level using a majority vote rule classifier based on the confidence levels provided by the feature classifiers and the weights associated with the one or more features for the particular user.

12. The apparatus of claim 11, wherein the processor is further configured to normalize the touch area by adjusting a height of the touch area relative to a width of the touch area.

13. The apparatus of claim 11, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

14. The apparatus of claim 13, wherein the weight for at least one feature is zero.

15. The apparatus of claim 11, wherein a confidence level associated with a risky touch pressure level is penalized in the feature classifier.

16. The apparatus of claim 11, wherein the processor configured to establish feature classifiers for a particular user is configured 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.

17. The apparatus of claim 11, wherein the processor is further configured to adjust the respective feature classifiers, based on an application specification of an application that is currently in use, to obtain respective adjusted feature classifiers,

wherein the processor configured to classify each of the one or more of the features is configured to classify each of the one or more features using the respective adjusted features classifiers,

wherein the application specification includes at least one value that specifies a configuration setting.

18. The apparatus of claim 17, wherein the application specification specifies a touch pressure level detection granularity.

19. The apparatus of claim 17, wherein the application specification specifies a risk associated with a particular touch pressure level.

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

21. 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, the features comprising an average capacitance value of the touch event, a touch area of the touch event, and a touch duration of the touch event, wherein the average capacitance value is based on a plurality of capacitance values of the touch event,

and

means for determining a touch pressure level for the touch event based on one or more of the features, wherein for each of the one or more features, a respective feature classifier and weight is established for a particular user based on training data associated with the particular user,

wherein the means for determining the touch pressure level comprises;

means for classifying each of the one or more of the features using the respective feature classifier for the particular user to provide a corresponding confidence level for each of a plurality of different touch pressure levels; and

means for estimating the touch pressure level using a majority vote rule classifier based on the confidence levels provided by the feature classifiers and the weights associated with the one or more features for the particular user.

22. The apparatus of claim 21, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

23. The apparatus of claim 21, wherein the means for establishing feature classifiers for a particular user 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.

24. 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, the features comprising an average capacitance value of the touch event, a touch area of the touch event, and a touch duration of the touch event, wherein the average capacitance value is based on a plurality of capacitance values of the touch event;

and

determining a touch pressure level for the touch event based on one or more of the features, wherein for each of the one or more features, a respective feature classifier and weight is established for a particular user based on training data associated with the particular user,

wherein the code for determining the touch pressure level comprises;

code for classifying each of the one or more of the features using the respective feature classifier for the particular user to provide a corresponding confidence level for each of a plurality of different touch pressure levels; and

code for estimating the touch pressure level using a majority vote rule classifier based on the confidence levels provided by the feature classifiers and the weights associated with the one or more features for the particular user.

25. The non-transitory computer-readable medium of claim 24, wherein the feature classifier is a Bayes Risk Classifier (BRC), and the weights are established using a boosting mechanism.

26. The non-transitory computer-readable medium of claim 24, wherein the code for establishing feature classifiers for a particular user 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.