Optimization for host based touch processing

Patent metadata

Field	Value
Publication number	US-9310933-B2
Application number	US-201414540894-A
Country	US
Kind code	B2
Filing date	Nov 13, 2014
Priority date	Feb 26, 2014
Publication date	Apr 12, 2016
Grant date	Apr 12, 2016

Abstract

Systems and methods are disclosed for determining a touch position from data received from a touch panel. In one implementation, an apparatus for processing a touch input signal includes a processor that may be configured for parallel processing, a touch device, a memory, operably connected to the processor, and configured to store processor instructions. The processor instructions can configure the processor to receive a plurality of data points corresponding to a plurality of touch events on the touch device, determine a center of mass estimate from the plurality of data points, determine a search radius of center of mass estimate, and determine an optimal touch point position based on the center of mass estimate and the search radius using the processor.

First claim

What is claimed is:

1. An apparatus for processing a touch input signal, comprising:
a processor;
a touch device configured to receive touch inputs;
a memory, operably connected to the processor, and configured to store processor instructions that configure the processor to
receive a plurality of data points corresponding to a plurality of touch events on the touch device,
determine a center of mass estimate from the plurality of data points;
determine a search radius of center of mass estimate;
for each of a plurality of maximum likelihood estimation models:
determine a touch location estimate based on the center of mass estimate and the search radius using a maximum likelihood estimation model of the plurality of maximum likelihood estimation models;
calculate an error of the touch location determined using the maximum likelihood estimation model;

select a touch location estimate from the plurality of maximum likelihood estimation models based on the calculated errors; and
determine an optimal touch point position based on a comparison of the error of the selected touch location estimate to a threshold.

2. The apparatus of claim 1 , wherein the processor is further configured to:
construct an adaptive maximum likelihood estimation model based upon a Gaussian model with an estimated covariance matrix formed using weighted sample estimates;
determine an error for the adaptive maximum likelihood estimation model;
compare the error for the adaptive maximum likelihood estimation model to the error of the selected touch location estimate; and
if the error for the adaptive maximum likelihood estimation model is less than the error of the selected touch location estimate, store the adaptive maximum likelihood estimation model in the plurality of maximum likelihood estimation models.

3. The apparatus of claim 1 , wherein determining an optimal touch point position based on a comparison of the error of the selected touch location estimate to a threshold comprises:
compare the error of the selected touch location estimate to a threshold;
if the error is below the threshold, use the selected touch location as the optimal touch point position; and
if the error is not below the threshold, determine the optimal touch point position based on a centroid estimate of a touch point.

4. The apparatus of claim 1 , wherein the processor instructions further configure the processor to determine the search radius based on determining an estimate of a search centroid error of the center of mass.

5. The apparatus of claim 4 , wherein the processor instructions further configure the processor to determine an estimate of a search centroid error of the center of mass using a signal-to-noise ratio of the touchscreen.

6. The apparatus of claim 4 , wherein the processor instructions further configure the processor to determine an estimate of a search centroid error of the center of mass using an aliasing estimate of the touchscreen.

7. The apparatus of claim 4 , wherein the processor instructions further configure the processor to determine an estimate of a search centroid error of the center of mass using distance to edge data of the touchscreen.

8. The apparatus of claim 4 , wherein the processor instructions further configure the processor to determine an estimate of a search centroid error of the center of mass based on a signal-to-noise ratio of the touchscreen, an aliasing estimate of the touchscreen and distance to edge data of the touchscreen.

9. The apparatus of claim 8 , wherein the processor instructions further configure the processor to downsample the received plurality of data points before determining the center of mass estimate.

10. A method of processing a touch input signal, the method comprising:
receiving a plurality of data points corresponding to a plurality of touch events on the touch device,
determining a center of mass estimate from the plurality of data points;
determining a search radius of center of mass estimate;
for each of a plurality of maximum likelihood estimation models:
determining a touch location estimate based on the center of mass estimate and the search radius using a maximum likelihood estimation model of the plurality of maximum likelihood estimation models;
calculating an error of the touch location determined using the maximum likelihood estimation model;

selecting a touch location estimate from the plurality of maximum likelihood estimation models based on the calculated errors; and
determining an optimal touch point position based on a comparison of the error of the selected touch location estimate to a threshold.

11. The method of claim 10 , further comprising:
constructing an adaptive maximum likelihood estimation model based upon a Gaussian model with an estimated covariance matrix formed using weighted sample estimates;
determining an error for the adaptive maximum likelihood estimation model;
comparing the error for the adaptive maximum likelihood estimation model to the error of the selected touch location estimate; and
if the error for the adaptive maximum likelihood estimation model is less than the error of the selected touch location estimate, storing the adaptive maximum likelihood estimation model in the plurality of maximum likelihood estimation models.

12. The method of claim 10 , wherein determining an optimal touch point position based on a comparison of the error of the selected touch location estimate to a threshold comprises:
comparing the error of the selected touch location estimate to a threshold;
if the error is below the threshold, using the selected touch location as the optimal touch point position; and
if the error is not below the threshold, determining the optimal touch point position based on a centroid estimate of a touch point.

13. The method of claim 10 , further comprising determining the search radius based on determining an estimate of a search centroid error of the center of mass.

14. The method of claim 13 , further comprising determining an estimate of a search centroid error of the center of mass using a signal-to-noise ratio of the touchscreen.

15. The method of claim 13 , further comprising determining an estimate of a search centroid error of the center of mass using an aliasing estimate of the touchscreen.

16. The method of claim 13 , further comprising determining an estimate of a search centroid error of the center of mass using distance to edge data of the touchscreen.

17. The method of claim 13 , further comprising determining an estimate of a search centroid error of the center of mass based on a signal-to-noise ratio of the touchscreen, an aliasing estimate of the touchscreen and distance to edge data of the touchscreen.

18. The method of claim 17 , further comprising downsampling the received plurality of data points before determining the center of mass estimate.

19. A non-transitory, computer readable storage medium having instructions stored thereon that cause a processing circuit to perform a method comprising:
receiving a plurality of data points corresponding to a plurality of touch events on the touch device,
determining a center of mass estimate from the plurality of data points;
determining a search radius of center of mass estimate;
for each of a plurality of maximum likelihood estimation models:
determining a touch location estimate based on the center of mass estimate and the search radius using a maximum likelihood estimation model of the plurality of maximum likelihood estimation models;
calculating an

Assignees

• Qualcomm Inc

Inventors

• Huang William Yee-Ming
• Ahmed Mohamed Imtiaz
• Wong Hsun Wei David
• Tilak Raghukul
• Gao Qiang
• Jalil Suhail
• Ng Teresa Ka Ki
• Law Carol King Mui
• Wang Rex

Classifications

• G06F3/044

  by capacitive means · CPC title

• G06F3/0418 Primary

  for error correction or compensation, e.g. based on parallax, calibration or alignment · CPC title

• G06F3/041661 Primary

  using detection at multiple resolutions, e.g. coarse and fine scanning; using detection within a limited area, e.g. object tracking window · CPC title

• G06F3/0412

  Digitisers structurally integrated in a display · CPC title

• G06F3/0416 Primary

  Control or interface arrangements specially adapted for digitisers · CPC title