Using Capacitive Images for Touch Type Classification

• US 20150242009A1
• Filed: 02/26/2014
• Published: 08/27/2015
• Est. Priority Date: 02/26/2014
• Status: Abandoned Application

• Current Assignee: Carnegie Mellon University, Qeexo Co. (TDK Corporation)
• Original Assignee: Qeexo Co. (TDK Corporation)
• Inventors: Xiao, Robert Bo, Schwarz, Julia, Harrison, Christopher, Lew, Greg
• Application Number:
  US14/191,329
• Publication Number:
  US 20150242009A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06F 18/23213 with fixed number of clusters, e.g. K-means clustering
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 2203/04106 Multi-sensing digitiser, i.e. digitiser using at least two different sensing technologies simultaneously or alternatively, e.g. for detecting pen and finger, for saving power or for improving position detection
• 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 3/043 using propagating acoustic waves
• CPC Class Codes: G06F 3/044 by capacitive means
• CPC Class Codes: G06V 10/763 Non-hierarchical techniques, e.g. based on statistics of modelling distributions
• CPC Class Codes: G06V 30/19147 Obtaining sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 30/19173 Classification techniques
• CPC Class Codes: G06V 30/194 References adjustable by an adaptive method, e.g. learning
• CPC Class Codes: G06V 40/107 Static hand or arm
• CPC Class Codes: G06V 40/11 Hand-related biometrics; Hand pose recognition
• CPC Class Codes: G06V 40/28 Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113

Abstract

An electronic device includes a touch-sensitive surface, for example a touch pad or touch screen. The user physically interacts with the touch-sensitive surface, producing touch events. The resulting interface actions taken depend at least in part on the touch type. The type of touch is determined in part based on capacitive image data produced by the touch event.

Claims

1. A method of interaction between a user and an electronic device having a touch-sensitive surface, the method comprising:

accessing a capacitive image comprising capacitive image data corresponding to capacitances at a plurality of locations on the touch-sensitive surface, the capacitances varying in response to a physical touch on the touch-sensitive surface;

processing the capacitive image data; and

determining a touch type for the physical touch based on the processed capacitive image data.

2. The method of claim 1 wherein the step of processing the capacitive image data comprises transforming the capacitive image data to obtain one or more derivative images for the capacitive image.

3. The method of claim 2 wherein the step of transforming the capacitive image data comprises using at least one of a log transform, adaptive thresholding, binarization, image morphological operation, or convolution with Gaussian kernel to transform the capacitive image data.

4. The method of claim 1 wherein the step of processing the capacitive image data comprises extracting a region of the capacitive image representing the physical touch, the region including capacitive image data for locations where the physical touch occurs.

5. The method of claim 4 wherein the step of extracting the region of the capacitive image comprises isolating the region from other regions representing other physical touches.

6. The method of claim 1 wherein the step of processing the capacitive image data comprises removing noise from the capacitive image.

7. The method of claim 1 wherein the step of determining a touch type comprises:

extracting features from the capacitive image data; and

classifying the features to determine a touch type for the physical touch.

8. The method of claim 7 wherein the extracted features include at least one of a covariance matrix computed based on the capacitive image data, a ratio of elements of the covariance matrix to the total variance of the capacitive image data and one or more eigenvalues of the covariance matrix.

9. The method of claim 7 wherein the step of extracting features comprises:

extracting, in the capacitive image, a plurality of various-sized neighborhoods around a touch contact origin of the physical touch; and

computing at least one statistical feature based on the plurality of neighborhoods.

10. The method of claim 9 wherein the statistical feature includes at least one of a mean, range, standard deviation, dispersion, skewness and root mean square (RMS).

11. The method of claim 7 wherein the extracted features include at least one of a vector of individual capacitive image data and the total sum of the individual capacitive image data.

12. The method of claim 7 wherein the step of extracting features comprises:

fitting a multivariate Gaussian function over distribution for the capacitive image data; and

computing features associated with the Gaussian function.

13. The method of claim 7 wherein the step of extracting features comprises estimating a touch contact area based on the number of nonzero values in the capacitive image data.

14. The method of claim 13 further comprising computing at least one statistical feature over the nonzero values.

15. The method of claim 7 wherein the step of extracting features comprises:

extracting a boundary corresponding to the physical touch in the capacitive image based on a contour analysis; and

computing features associated with the boundary.

16. The method of claim 7 wherein the extracted features include at least one feature based on a location, shape, orientation, and/or size of the touch.

17. The method of claim 7 wherein the step of classifying the features comprises using at least one of a support vector machine, neural network, decision tree, naï

ve Bayes, random forest, elastic matching, template matching, k-means clustering, or logistic boosting to classify the features.

18. The method of claim 7 wherein the step of classifying the features comprises:

applying multiple classifiers to the features to obtain multiple classification results; and

combining the classification results through a voting scheme.

19. The method of claim 1 further comprising:

accessing sensor data for a sensor signal produced by the physical touch, the sensor data different than the capacitive image data;

extracting sensor features from the sensor data; and

determining a touch type for the physical touch based also on the extracted sensor features.

20. The method of claim 19 wherein the sensor data includes vibro-acoustic data caused by the physical touch.

21. The method of claim 1 wherein accessing the capacitive image is responsive to the occurrence of the physical touch on the touch-sensitive surface.

22. The method of claim 1 further comprising:

executing an action on the electronic device in response to the touch and touch type, wherein the same touch results in execution of a first action for a first touch type and results in execution of a second action for a second touch type.

23. The method of claim 1 wherein the touch-sensitive surface is a touch screen.

24. A machine-readable tangible storage medium having stored thereon data representing sequences of instructions, which when executed by an electronic device having a touch-sensitive surface, cause the electronic device to perform a method comprising the steps of:

accessing a capacitive image comprising capacitive image data corresponding to capacitances at a plurality of locations on the touch-sensitive surface, the capacitances varying in response to a physical touch on the touch-sensitive surface;

processing the capacitive image data; and

determining a touch type for the physical touch based on the processed capacitive image data.

25. An electronic device comprising:

a touch-sensitive surface;

means for accessing a capacitive image comprising capacitive image data corresponding to capacitances at a plurality of locations on the touch-sensitive surface, the capacitances varying in response to a physical touch on the touch-sensitive surface;

means for processing the capacitive image data; and

means for determining a touch type for the physical touch based on the processed capacitive image data.