Position determination techniques in resistive touch screen applications

Abstract

Systems and methods to determine locations for dual touch operations performed on a four-wire resistive touch screen. The systems and methods may include measuring signals from pairs of electrodes on each of a first and second resistive sheet of the resistive touch screen in two phases of operation. The systems and methods may further include determining touch screen segment resistances from the signal measurements. The systems and methods may determine locations corresponding to the dual touch operations from the resistances. The systems and methods may also determine locations from the signal measurements.

1 . A method for determining locations for a dual touch operation performed on a four- wire resistive touch screen, comprising:
 driving a voltage across a first resistive sheet of the touch screen and measuring a first set of signals from pairs of electrodes on the first resistive sheet and a second resistive sheet;   driving the voltage across the second resistive sheet of the touch screen and measuring a second set of signals from pairs of electrodes on the first resistive sheet and the second resistive sheet;   estimating segment resistances generated across the first and second resistive sheets by the dual touch operation from the first and second sets of measured signals; and   estimating X-Y coordinates representing locations of the dual touch operation from the estimated segment resistances.   
     
     
         2 . The method of  claim 1 , wherein the sets of measured signals are voltages at the pairs of electrodes. 
     
     
         3 . The method of  claim 1 , wherein the sets of measured signals are currents for each resistive sheet. 
     
     
         4 . The method of  claim 1 , the estimating coordinates further comprising:
 calculating a centroid location of the dual touch operation, and   estimating the distance between each touch of the dual touch operation.   
     
     
         5 . The method of  claim 1 , the estimating segment resistances further comprising calculating the segment resistances according to Eqs. 1 and 2. 
     
     
         6 . The method of  claim 1 , the estimating segment resistances further comprising inputting the first and second sets of measured signals to an artificial neural network. 
     
     
         7 . A method for determining locations for a dual touch operation performed on a four- wire resistive touch screen, comprising:
 capturing signals from pairs of electrodes of resistive sheets of the resistive touch screen during the dual touch operation;   calculating segment resistances for a first and second resistive sheet of the resistive touch screen according to Eqs. 1 and 2; and   calculating X-Y coordinates representing locations of the dual touch operation from the calculated segment resistances.   
     
     
         8 . The method of  claim 7 , further comprising outputting the X-Y coordinates from an integrated circuit. 
     
     
         9 . The method of  claim 7 , further comprising outputting the segment resistances from an integrated circuit. 
     
     
         10 . A method for determining estimated locations for a dual touch operation performed on a four-wire resistive touch screen, comprising:
 capturing signals from pairs of electrodes of resistive sheets of the resistive touch screen during the dual touch operation;   on a trial-and-error basis:
 estimating segment resistances for a first and second resistive sheet of the resistive touch screen; 
 calculating estimated signals for the first and second resistive sheets according to a multi-segment resistor model populated by the segment resistances; 
 comparing the estimated signals to the captured signals and determining an error value therefrom; and 
 if the error value is within a predetermined range, estimating locations of the dual touch operation from the estimated segment resistances. 
   
     
     
         11 . The method of  claim 10 , further comprising: if the error value is outside the predetermined range, adjusting the estimated segment resistances. 
     
     
         12 . The method of  claim 10 , wherein the estimated signals are computed according to Eqs. 1 and 2. 
     
     
         13 . The method of  claim 10 , further comprising:
 calculating a centroid location of the dual touch operation, and   estimating the distance between each touch of the dual touch operation.   
     
     
         14 . A system for determining estimated locations for a dual touch operation performed on a four-wire resistive touch screen, comprising:
 inputs for pairs of signals generated from respective passive layers of the resistive touch screen during the dual touch operation;   input nodes for multiplying the signals by respective scaling factors;   intermediate nodes for weighting and summing signals received from the scaling nodes and applying respective non-linear calculations to the weighted sum of the signals; and   output nodes for weighting and summing signals received from the intermediate nodes and applying respective non-linear calculations to the weighted sum of the signals, outputs of the output nodes indicating the estimated locations of the dual touch operation.   
     
     
         15 . The system of  claim 14 , wherein the non-linear calculation is a sigmoid calculation. 
     
     
         16 . The system of  claim 14 , wherein the non-linear calculation is piece-wise linear calculation. 
     
     
         17 . A method for determining estimated locations for a dual touch operation performed on a four-wire resistive touch screen using an artificial neural network, comprising:
 capturing signals from pairs of electrodes of resistive sheets of the resistive touch screen during the dual touch operation;   multiplying each of the captured signals by predetermined scaling factor;   applying first weighted summing calculations to the scaled signals;   applying first non-linear calculations to the weighted sums of the scaled signals;   applying second weighted summing calculations to predetermined sets of the first non-linear calculations;   applying second non-linear calculations to the weighted sums of the predetermined sets; and   estimating X-Y coordinates representing locations of the dual touch operation from the second non-linear calculations.   
     
     
         18 . The method of  claim 17 , wherein the first non-linear calculation is a sigmoid calculation. 
     
     
         19 . The method of  claim 17 , wherein the first non-linear calculation is a piece-wise linear calculation. 
     
     
         20 . The method of  claim 17 , wherein the first non-linear calculation is a hyperbolic tangent calculation. 
     
     
         21 . The method of  claim 17 , wherein the first non-linear calculation is an arctangent calculation. 
     
     
         22 . The method of  claim 17 , wherein the second non-linear calculation is a sigmoid calculation. 
     
     
         23 . The method of  claim 17 , wherein the second non-linear calculation is a piece-wise linear calculation. 
     
     
         24 . The method of  claim 17 , wherein the second non-linear calculation is a hyperbolic tangent calculation. 
     
     
         25 . The method of  claim 17 , wherein the second non-linear calculation is an arctangent calculation. 
     
     
         26 . The method of  claim 17 , wherein the second non-linear calculation is a sigmoid calculation, a piece-wise linear calculation, a hyperbolic tangent calculation, or an arctangent calculation. 
     
     
         27 . The method of  claim 17 , further comprising calibrating the artificial neural network. 
     
     
         28 . The method of  claim 27 , the calibrating the artificial neural network further comprising:
 on an iterative basis:
 inputting training data sets to the artificial neural network; 
 comparing the artificial neural network outputs with expected outputs from the training data sets; 
 adjusting operational parameters of the artificial neural network from the comparison; 
 inputting validation data sets to the artificial neural network; 
 calculating the difference between the artificial neural network outputs and the expected outputs from the validation data sets; 
 if within a first iteration and the difference is above a predetermined threshold, repeating the inputting, the comparing, the adjusting, the inputting, and the calculating the difference; and 
   if the difference decreases between successive iterations, repeating the inputting, the comparing, the adjusting, the inputting, and the calculating the difference.   
     
     
         29 . The method of  claim 28 , further comprising:
 if the difference stops decreasing between successive iterations, stopping the calibrating, and   resetting the operational parameters to the settings of the previous iteration.   
     
     
         30 . The method of  claim 28 , further comprising:
 repeating the inputting training data sets, the comparing, and the adjusting for a predetermined number of iterations.   
     
     
         31 . A method for determining a touch type for a touch operation performed on a four- wire resistive touch screen, comprising:
 driving a voltage across a first resistive sheet of the touch screen and measuring a first set of signals from pairs of electrodes on the first resistive sheet and the second resistive sheet;   driving the voltage across the second resistive sheet of the touch screen and measuring a second set of signals from pairs of electrodes on the first resistive sheet and the second resistive sheet; and   classifying a touch operation as either a single touch type or a dual touch type based on the measured signals.   
     
     
         32 . The method of  claim 31 , further comprising outputting an indicator of the touch type from an integrated circuit. 
     
     
         33 . An apparatus for determining estimated locations for a touch operation performed on a four-wire resistive touch screen, comprising:
 a switching block for managing an interface to the resistive touch screen;   a touch screen driver for generating signals to drive the resistive touch screen;   a converter for digitizing and measuring signals output from the switching block to the converter;   a data storage for storing digital codes output from the converter; and   a processing unit configured to determine estimated X-Y coordinates representing locations for the touch operation from the digital codes output from the data storage.   
     
     
         34 . The apparatus of  claim 33 , further comprising an output connected to the converter for outputting the digital codes from the converter. 
     
     
         35 . The apparatus of  claim 33 , further comprising an output connected to the processing unit for outputting the estimated X-Y coordinates. 
     
     
         36 . The apparatus of  claim 33 , wherein the processing unit being further configured to determine a touch type for the touch operation. 
     
     
         37 . The apparatus of  claim 33 , wherein the processing unit comprises a neural network. 
     
     
         38 . A method for determining estimated locations for a dual touch operation performed on a four-wire resistive touch screen, comprising:
 capturing signals from pairs of electrodes of resistive sheets of the resistive touch screen during the dual touch operation;   calculating a centroid location of the dual touch operation;   estimating a distance between each touch of the dual touch operation; and   estimating X-Y coordinates representing locations of the dual touch operation from the centroid location and the estimated distance.