SYSTEM AND METHOD FOR ESTIMATING LOCATION OF A TOUCH OBJECT IN A CAPACITIVE TOUCH PANEL

• US 20180032170A1
• Filed: 07/27/2016
• Published: 02/01/2018
• Est. Priority Date: 07/27/2016
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: SHAIK, Karimulla, VANGA, Sandeep, VADREVU, Prathyusha, KIM, Jongseon, LEE, Seungjae
• Application Number:
  US15/220,621
• Publication Number:
  US 20180032170A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 2203/04101 2.5D-digitiser, i.e. digitiser detecting the X/Y position of the input means, finger or stylus, also when it does not touch, but is proximate to the digitiser's interaction surface and also measures the distance of the input means within a short range in the Z direction, possibly with a separate measurement setup
• CPC Class Codes: G06F 2203/04107 Shielding in digitiser, i.e. guard or shielding arrangements, mostly for capacitive touchscreens, e.g. driven shields, driven grounds
• CPC Class Codes: G06F 2203/04806 Zoom, i.e. interaction techniques or interactors for controlling the zooming operation
• 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
• CPC Class Codes: G06F 3/0412 Digitisers structurally integrated in a display
• CPC Class Codes: G06F 3/041662 using alternate mutual and self-capacitive scanning
• CPC Class Codes: G06F 3/0418 for error correction or compensation, e.g. based on parallax, calibration or alignment
• CPC Class Codes: G06F 3/0446 using a grid-like structure of electrodes in at least two directions, e.g. using row and column electrodes
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method and capacitive touch panel are provided. The method includes receiving, by a sensing circuit, raw data for detecting a touch object in a proximity of a capacitive touch panel, where the raw data includes a difference of a mutual capacitance value and a self-capacitance value at each of touch nodes of the capacitive touch panel; processing, by a touch sensing controller, the received raw data to derive digitized capacitance data; classifying, by the touch sensing controller, the digitized capacitance data; and estimating, by the touch sensing controller, at least one of a location of the touch object on the capacitive touch panel and a distance of the touch object from the capacitive touch panel within the proximity using the classified capacitance data.

Claims

1. A method for estimating location of a touch object in a capacitive touch panel, the method comprising:

receiving, by a sensing circuit, raw data for detecting a touch object in a proximity of the capacitive touch panel, the raw data comprising a difference of a mutual capacitance value and a self-capacitance value at each of a plurality of touch nodes of the capacitive touch panel;

processing, by a touch sensing controller, the received raw data to derive digitized capacitance data;

classifying, by the touch sensing controller, the digitized capacitance data; and

estimating, by the touch sensing controller, at least one of a location of the touch object on the capacitive touch panel and a distance of the touch object from the capacitive touch panel within the proximity using the classified capacitance data.

2. The method as claimed in claim 1, wherein the processing comprises:

filtering noise data from the raw data to obtain threshold digitized capacitance data; and

extracting one or more features from the threshold digitized capacitance data, the one or more features including an energy, a gradient, a peak and a flatness aspect associated with the threshold digitized capacitance data.

3. The method as claimed in claim 1, wherein the location of the touch object is estimated by determining an X coordinate and a Y coordinate of the location of the touch object on the capacitive touch panel.

4. The method as claimed in claim 1, wherein the distance of the touch object from the capacitive touch panel is estimated based on at least one of an offline mode and an online mode.

5. The method as claimed in claim 4, wherein the offline mode comprises a linear discriminant analysis (LDA) and a Gaussian mixture model (GMM).

6. The method as claimed in claim 4, wherein the online mode comprises estimating the distance of the touch object based on extracted features.

7. The method as claimed in claim 5, further comprising learning discriminant functions using extracted features, and storing cluster centers for the linear discriminant analysis (LDA) during the offline mode.

8. The method as claimed in claim 5, further comprising learning covariance matrices and mixture weights of the Gaussian Mixture Model (GMM) using extracted features obtained in the offline mode.

9. The method as claimed in claim 1, further comprising:

inputting features extracted during an offline mode to a classifier;

projecting the extracted features onto a new coordinate system using vectors obtained during an online mode;

determining distances from each of a plurality of cluster centers to the projected features in the new coordinate system; and

assigning a vector with a class label having a minimum distance from the capacitive touch panel.

10. A capacitive touch panel for estimating location of a touch object relative to the capacitive touch panel, the capacitive touch panel comprising:

a sensor circuit that receives raw capacitance data for detecting a touch object in a proximity of the capacitive touch panel, the raw data comprising a difference of a mutual capacitance value and a self-capacitance value at each of a plurality of touch nodes of the capacitive touch panel; and

at least one microprocessor configured to;

process the received raw data to derive digitized capacitance data;

extract a plurality of features from the digitized capacitance data;

the plurality of features comprising an energy, a gradient and class labels;

project the extracted features on to a new coordinate system using vectors obtained during an online phase;

classify the digitized capacitance data;

determine distances from each of a plurality of cluster centers to the projected features in the new coordinate system;

assign a vector with a class label having a minimum distance from the capacitive touch panel; and

estimate at least one of a location of the touch object on the capacitive touch panel and a distance of the touch object from the capacitive touch panel within the proximity using the classified capacitance data.

11. A capacitive touch panel comprising:

a plurality of sensor electrodes configured to detect a touch object in proximity to the sensor electrodes using capacitance, and to generate raw capacitance data; and

at least one microprocessor configured to;

in a training phase, digitize training capacitance data from the sensor electrodes to generate training capacitance data, extract one or more features from the training capacitance data, classify the extracted one or more features to generate first classified data, and estimate a height of the touch object from the capacitive touch panel using the first classified data; and

in a testing phase, digitize test capacitance data from the sensor electrodes to generate test capacitance data, extract one or more features from the test capacitance data, classify the extracted one or more features based on the first classified data to generate second classified data, and determine the height of the touch object from the capacitive touch panel using the second classified data, the one or more extracted features from the test capacitance data, and the estimated height.

12. The capacitive touch panel as claimed in claim 11, further comprising an analog front end that removes noise from the raw capacitance data and digitizes the raw capacitance data.

13. The capacitive touch panel as claimed in claim 11, wherein the features comprise an energy, a gradient, a peak, and a flatness.

14. The capacitive touch panel as claimed in claim 11, wherein the extracted one or more features are classified to generate the first classified data in the training phase using a linear discriminant analysis (LDA) and/or a Gaussian mixture model (GMM), and the extracted one or more features are classified to generate the second classified data in the testing phase using a linear discriminant analysis (LDA) and/or a Gaussian mixture model (GMM).

15. The capacitive touch panel as claimed in claim 11, wherein the first classified data comprises one or more basis vectors and one or more cluster centers in a new coordinate system that is different from a coordinate system of the raw capacitance data.

16. The capacitive touch panel as claimed in claim 15, wherein in the testing phase, the one or more extracted features are projected onto a new coordinate system using the basis vectors.

17. The capacitive touch panel as claimed in claim 11, wherein the at least one microprocessor determines an X coordinate and a Y coordinate of the touch object on the capacitive touch panel.

18. The capacitive touch panel as claimed in claim 17, wherein the height is determined as a Z coordinate.