JP2013097628A - Input device and calibration method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an input device capable of obtaining respective loads at a plurality of depressed points which are simultaneously depressed, and a method for detecting loads at a plurality of points.SOLUTION: An input device 1 includes: a capacitive touch panel sensor capable of detecting each of a plurality of depressed points A and B simultaneously depressed on an operation surface 4a; a load detection sensor capable of detecting a centroid position and a centroid load at each of the depressed points A and B; and a control unit capable of calculating respective loads (z1 and z2) at points A and B on the basis of position coordinates (x1,y1) and (x2,y2), centroid coordinates (X, Y), and centroid loads (Z) of respective depressed points.

Description

  The present invention relates to an input device that is mounted on a portable device or other electronic device and operates by bringing a finger or the like into contact with an operation panel.

  In an input device that calculates the position coordinates of a pressing point on an operation surface using a plurality of pressing sensors, a user or the like can easily set a correction value (coefficient) of a pressing point calculation formula along with a change over time. It is preferable that calibration can be performed.

  However, conventionally, a correction value is obtained by applying a predetermined load. For this reason, even if it is necessary to change the correction value due to changes over time, it is necessary to apply a predetermined load, which is a burden on the user, and an evaluation machine for load measurement is required, and the correction value cannot be changed easily. there were.

JP 2002-149348 A JP 2010-286742 A JP 2011-96271 A JP-A-61-120230 JP 2005-526333 A

  The present invention solves the above-described conventional problems, and it is possible to change the correction value (correction coefficient) by performing calibration more easily and freely than in the past, and also effectively improves the detection accuracy of the pressing point by calibration. An object of the present invention is to provide an input device and a calibration method that can be improved.

The input device in the present invention is
An operation panel;
A plurality of pressure sensors arranged on the back side of the operation surface of the operation panel;
A control unit capable of executing a calibration mode for obtaining a correction coefficient of a multiple regression equation used when calculating the position coordinates of the pressing point;
In the control unit, each of the pressure sensors obtained when the plurality of calibration pressing points on the operation surface having a number larger than the number of terms of the multiple regression equation are pressed and each calibration pressing point is pressed. To obtain the total output at each calibration pressure point, and divide the sensor output at each calibration pressure point by the total output at each calibration pressure point to standardize each output. An output is calculated, and the correction coefficient is calculated based on output data of the position coordinates of each calibration pressing point and each normalized output.

The calibration method in the present invention is
In a calibration mode for obtaining a correction coefficient of a multiple regression equation used when calculating a position coordinate of a pressing point of an input device having an operation panel in which a plurality of pressing sensors are arranged on the back side with respect to the operating surface.
Pressing each calibration pressing point on the operation surface having a number larger than the number of terms in the multiple regression equation; and
The total output at each calibration pressure point is calculated by adding the pressure sensors obtained when the calibration pressure points are pressed, and the sensor output at each calibration pressure point is calculated as the calibration value. Calculating each normalized output by dividing by the total output at the pressure point,
Calculating the correction coefficient based on the output data of the position coordinates of each calibration pressing point and each normalized output;
It is characterized by having.

  Conventionally, it was necessary to apply a predetermined load, and therefore load measurement was required. However, in the present invention, this is not necessary, and therefore, calibration can be performed easily and freely compared to the conventional method. The correction coefficient can be optimized by calibration under the environment. Therefore, the detection accuracy of the pressing point can be improved.

  In the present invention, it is preferable to use a quadratic or higher multiple regression equation. Thus, by performing calibration and optimizing the correction coefficient, it is possible to improve the detection accuracy of the pressed point more effectively.

  In the present invention, the pressure sensor is arranged opposite to the periphery of the operation surface in two different directions, and calculates a difference between the normalized outputs of the pressure sensors arranged opposite to each other. It is preferable to calculate the correction coefficient of the multiple regression equation using the third order or higher. When a calculation error occurs due to inferior linearity of each press sensor or the like, the calculation error can be reduced by using the difference between the standardized outputs of the pressure sensors arranged opposite to each other as described above. Therefore, by performing calibration and optimizing the correction coefficient, it is possible to improve the detection accuracy of the pressed point more effectively. However, the difference is used only when a multiple regression equation of third order or higher is used.

  Further, by using the difference, the number of terms in the multiple regression equation can be reduced, and the calculation burden on the control unit can be reduced.

  In the above, the pressure sensors are arranged at the four corners of the peripheral portion of the operation surface, or are arranged opposite to each other with the X direction and the Y direction orthogonal through the substantial center of the operation surface as the two directions. Can be.

  In the present invention, it is preferable that the control unit controls to switch between a normal mode in which a pressing point on the operation surface is calculated by polynomial approximation using the multiple regression equation and the calibration mode.

  In the present invention, the calculation of each normalized output is preferably executed when all outputs are within a predetermined range. As a result, the user can easily perform calibration.

  In the present invention, the calibration can be performed easily and freely as compared with the conventional case, and the correction coefficient can be optimized by the calibration under the use environment. Therefore, the detection accuracy of the pressing point can be improved.

FIG. 1 is a plan view of an input device according to the first embodiment of the present invention. FIG. 2 is a partial longitudinal sectional view of the input device according to the embodiment of the present invention. FIG. 2 is a partial longitudinal sectional view taken along a diagonal line between the pressure sensor 5 and the pressure sensor 8 shown in FIG. FIG. 3 is a block diagram of the input device of this embodiment. 4A and 4B are explanatory diagrams of the pressure sensor, in which FIG. 4A is a partial longitudinal sectional view, and FIG. 4B is a rear perspective view of a sensor substrate constituting the pressure sensor. FIG. 5 is a flowchart for explaining a pressing point detection method and a calibration method using the input device of this embodiment. FIG. 6 shows a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a quadratic multiple regression equation. FIG. 7 shows a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a cubic multiple regression equation. FIG. 8 shows a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a fourth-order multiple regression equation. FIG. 9 shows a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a fifth-order multiple regression equation. FIG. 10 shows a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a first-order multiple regression equation. FIG. 11 is a simulation result shown as a comparative example in the case where the position coordinates of the pressing point are calculated by polynomial approximation using a quadratic multiple regression equation. FIG. 12 shows a simulation result shown as a comparative example in the case where the position coordinates of the pressing point are calculated by polynomial approximation using a cubic multiple regression equation. FIG. 13A is a graph showing the relationship between the total output obtained by adding each sensor output and each sensor output. FIG. 13B shows the total output obtained by adding each sensor output and the output difference (sensor It is a graph which shows the relationship with output S2-S3, S1-S4). FIG. 13 is a plan view of an input device according to the second embodiment of the present invention. It is a flowchart figure for calculating the correction value in a comparative example. It is a flowchart figure for calculating the position coordinate of the press point in a comparative example.

  FIG. 1 is a plan view of the input device according to the first embodiment of the present invention, FIG. 2 is a partial longitudinal sectional view of the input device according to the embodiment of the present invention, and FIG. 3 is an input device according to the present embodiment. 4 is an explanatory diagram of the pressure sensor, FIG. 4 (a) is a partial longitudinal sectional view, and FIG. 4 (b) is a rear perspective view of the sensor substrate constituting the pressure sensor, FIG. 5 is a flowchart for explaining a pressing point detection method and a calibration method using the input device of this embodiment.

  The input device 1 according to the present embodiment includes a transparent operation panel 4 and a plurality of press sensors 5 to 8 provided on the back surface 4c of the operation panel 4. The operation panel 4 is made of glass or plastic. The surface of the operation panel 4 is an operation surface 4a.

  As shown in FIGS. 1 and 2, by providing a decoration layer 9 on the back surface 4 c of the peripheral portion 4 b of the operation panel 4, the liquid crystal display (LCD) 3 is displayed through the operation panel 4 and the operation on the operation surface 4 a is performed. Can be divided into an operation area that enables the operation area and an opaque decoration area that borders the periphery of the operation area. In the decoration area, the pressure sensors 5 to 8 are not visible from the operation surface 4a side. Here, FIG. 2 is assumed to be a partial longitudinal section cut along a diagonal line between the pressure sensor 5 and the pressure sensor 8 shown in FIG.

  Each of the pressure sensors 5 to 8 includes a sensor substrate 12 and a base substrate 13 as shown in FIG. The sensor substrate 12 is provided with a displacement portion 14 and a protruding pressure receiving portion 17 protruding upward on the upper surface of the displacement portion 14. A predetermined space portion 15 is formed between the sensor substrate 12 and the base substrate 13 so that the displacement portion 14 can be displaced in the height direction when receiving a load. As shown in FIGS. 4A and 4B, a plurality of piezoresistive elements 16 are provided on the back surface of the sensor substrate 12 as strain detecting elements. When the displacement portion 14 is displaced in the height direction by the load received by the pressure receiving portion 17, the electric resistance of each piezoresistive element 16 changes according to the amount of displacement, and the bridge circuit configured by each piezoresistive element 16 The sensor output can be obtained by changing the point potential. As shown in FIG. 4B, the wiring portion 18 routed from each piezoresistive element 16 is electrically connected to a pad portion (not shown).

  The pressure sensors 5 to 8 in the present embodiment may be other than the configuration shown in FIG. For example, the capacitance can be changed based on a change in the distance between the two electrodes when the operation surface 4a is pressed, and the load can be detected by the change in the capacitance.

  As shown in FIGS. 1 and 2, the pressure sensors 5 to 8 are arranged on the back surface 4 c side of the operation panel 4. For example, as shown in FIG. 5A, the pressure sensors 5 to 8 are arranged at the four corners of the peripheral portion 4b of the operation surface 4a. Further, as shown in FIG. 2, a support portion 10 that supports the pressure sensors 5 to 8 is provided, and the support portion 10 and the operation panel 4 are connected by a connection portion 11 that can be deformed in the height direction. Thereby, when the operation surface 4a is pressed, the operation panel 4 moves downward, and a load can be applied to the press sensors 5-8. The connection part 11 is a double-sided tape, for example.

  In addition, the support structure of the pressure sensors 5-8 in the touch panel 1 is not limited to what is shown in FIG. The positions of the pressure sensors 5 to 8 on the touch panel 1 are not limited to those shown in FIG. 5A. For example, as shown in FIG. 14, the X direction and the Y direction orthogonal to each other through the center O of the operation surface 4a. The press sensors 5 to 8 can also be arranged at positions of the peripheral portions 4b facing each other in the direction (cross arrangement). In this case, the pressure sensors 5 to 8 can be arranged at arbitrary positions on each side of the peripheral portion 4b. However, it is preferable that the pressure sensors 5 to 8 be arranged closer to the center of each side of the peripheral portion 4b, and more accurate. In order to detect the position of a high pressing point, it is better to arrange the pressing sensors 5 to 8 at the center of each side of the peripheral portion 4b.

  As shown in FIG. 3, the input device 1 according to this embodiment includes an operation panel 4, press sensors 5 to 8, and a control unit (IC) 2 connected to the press sensors 5 to 8. Further, data from the control unit 2 can be transmitted to the image display device 20 such as a liquid crystal display (LCD) 3 of the device main body. The control unit 2 can calculate the pressing point p00 when the input device 1 is used and the operation surface 4a is pressed.

The control unit 2 calculates the position coordinates (X, Y) of the pressing point p00 by polynomial approximation using a multiple regression equation of second order or higher.
A quadratic multiple regression equation is shown below.

(Formula 1)
X = a1 + a2, S1 + a3, S2 + a4, S3 + a5, S4 + a6, S1 ² + a7, S1, S2 + a8, S1, S3 + a9, S1, S4 + a10, S2 ² + a11, S2, S3 + a12, S2, S4 + a13, S3 ² + a14, S3, S4 + a15, S4 ²
Y = b1 + b2, S1 + b3, S2 + b4, S3 + b5, S4 + b6, S1 ² + b7, S1, S2 + b8, S1, S3 + b9, S1, S4 + b10, S2 ² + b11, S2, S3 + b12, S2, S4 + b13, S3 ² + b14, S3, S4 + b15, S4 ²

  X and Y in Formula 1 indicate the pressing points calculated by Formula 1 (see FIG. 1). a1 to a15 and b1 to b15 are correction coefficients. S1 to S4 are standardized outputs.

“Standardized output” will be described.
When the operation surface 4a is pressed, sensor outputs can be obtained from the press sensors 5-8. Here, the sensor output of the press sensor 5 is s1, the sensor output of the press sensor 6 is s2, the sensor output of the press sensor 7 is s3, and the sensor output of the press sensor 8 is s4. The total output (s1 + s2 + s3 + s4) is obtained by adding the sensor outputs of the pressure sensors 5-8. The “normalized output” is obtained by dividing the sensor outputs s1 to s4 by the total output.

  The normalized output S1 in Equation 1 is s1 / (s1 + s2 + s3 + s4), the normalized output S2 is s2 / (s1 + s2 + s3 + s4), the normalized output S3 is s3 / (s1 + s2 + s3 + s4), and the normalized output S4 is S4 / (s1 + s2 + s3 + s4).

  First, as initial settings, correction coefficients a1 to a15 and b1 to b15 are calculated. The correction coefficient is calculated in advance before shipment, and in the present embodiment, the user can change the correction coefficient himself. The input device 1 according to the present embodiment includes a calibration mode. When the sensitivity of the press sensors 5 to 8 changes due to a change with time and the detection accuracy of the press points is reduced, the correction coefficients a1 to a15 are corrected. , B1 to b15 can be easily reset.

  Hereinafter, the calculation steps of the correction coefficients a1 to a15 and b1 to b15 in the calibration mode will be described with reference to the flowchart of FIG.

  First, as shown in FIG. 5, the control unit 2 shown in FIG. 3 determines whether or not the normal mode is set (step ST1). The normal mode is a mode for calculating the pressing point p00 (see FIG. 1) pressed by the user using the above-described quadratic regression equation including the calculated correction coefficients a1 to a15 and b1 to b15. Indicates the usage state. In this normal mode, the position coordinate of the pressing point p00 is detected, and the screen displayed on the operation surface 4a is updated / changed based on the detection result.

When not in the normal mode, the mode shifts to the calibration mode.
First, in the calibration mode, as shown in FIG. 1, the operation surface 4a is partitioned into, for example, a grid, and each horizontal line (virtual line parallel to the X1-X2 direction) and each vertical line ( The intersections with the imaginary line parallel to the Y1-Y2 direction are displayed as calibration pressing points p01 to p35 (step ST2). The X and Y coordinates of the calibration pressing points p01 to p35 are determined in advance. For example, in the configuration including the capacitive touch panel 21 shown in FIG. 3, the calibration press points p01 to p35 can be displayed on the operation surface 4a using the plane coordinates by the touch panel 21. The number of calibration pressing points p01 to p35 is larger than the number of terms in the multiple regression equation.

  The user presses the calibration pressing points p01 to p35 in order (step ST3). In addition, the structure which presses each calibration press point p01-p35 sequentially using the apparatus which can apply a load instead of a user may be sufficient. In the control part 2, each above-mentioned normalization output S1-S4 is calculated | required from each sensor output s1-s4 of each press sensor 5-8 when each press point p01-p35 is pressed (step ST4).

Then, an output table in which the X and Y coordinates of each calibration pressing point are associated with each normalized output is created (step ST5).
An example is shown below.

  In addition, each sensor output shown in Table 1 is measured by pressing each of the press points p01 to p35 with a certain load (for example, 1N). However, as shown below, the data necessary for obtaining the correction coefficient is the position coordinate of the pressing point and each normalized output, and the load value is not taken into consideration. For example, in this embodiment, it is determined on the basis of all outputs, and when all outputs are within a certain range, it is determined that the measurement is normal and load measurement is not required.

  Table 1 lists not only the X and Y coordinates of each calibration pressing point and each normalized output, but also each sensor output. The output table in Table 1 is stored in the storage unit of the control unit 2.

After confirming whether or not the measurement at all calibration pressing points p01 to p35 is completed (step ST6), the correction coefficients a1 to a15 and b1 to b15 of Formula 1 are calculated based on the output table shown in Table 1 ( Step ST7).
For the calculation method of the correction coefficient, see Equation 2 below.

(Formula 2)

  The correction coefficients a1 to a15 and b1 to b15 calculated as described above are stored in the correction value table.

The normal mode will be described.
When the user presses on the operation surface 4a in the normal mode in step ST1 shown in FIG. 5 (for example, the press point p00 in FIG. 1), the sensor outputs s1 to s4 are obtained from the press sensors 5 to 8 shown in FIG. (Step ST8). And in the control part 8, each above-mentioned normalization output S1-S4 is calculated from each sensor output.

  As shown in FIG. 5, the correction coefficients a1 to a15 and b1 to b15 of Equation 1 stored in the correction value table are referred to (step ST9), and the pressing point p00 is obtained by polynomial approximation using the second-order multiple regression equation shown in Equation 1. Are calculated (step ST10).

  Then, the image display process of the liquid crystal display 3 is performed based on the coordinate data of the pressing point calculated in step ST10 (step ST11 in FIG. 5).

  FIG. 6 is a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation by the quadratic multiple regression equation shown in Equation 1.

  As shown in FIG. 6, it was found that the actual pressing point and the calculated pressing point almost coincided with each other near the center of the operation surface. However, it has been found that there is a slight deviation between the actual pressing point in the vicinity of the periphery of the operation surface and the calculated pressing point.

  In the present embodiment, the amount of deviation between the actual pressing point and the calculated pressing point can be reduced more effectively by using a third or higher order multiple regression equation. However, as the degree increases, the number of terms in the multiple regression equation increases accordingly, that is, the correction coefficient increases. In this case, in order to obtain the correction coefficient, it is necessary that the number of calibration pressing points shown in the output table of Table 1 is larger than the number of correction coefficients (number of terms). By the way, the number of terms is 35 if the third-order multiple regression equation using each of the above normalized outputs as a variable, and the number of terms is 70 if the fourth-order multiple regression equation using each of the above-mentioned normalized outputs as a variable. If the 5th order multiple regression equation with each normalized output is a variable, the number of terms is 126. Therefore, when using a multiple regression equation of the third or higher order, it is necessary to further increase the calibration pressing points p01 to p35 shown in FIG.

  If the number of terms increases in this way, there are too many calibration pressing points when performing calibration for resetting the correction coefficient, and the calculation burden on the control unit 2 increases.

  FIG. 13A is a graph showing the relationship between the total output obtained by adding each sensor output and each sensor output. FIG. 13B shows the total output obtained by adding each sensor output and the difference (standard). It is a graph which shows the relationship with the control output S2-S3, S1-S4).

  FIG. 13 shows the total output, sensor outputs, and differences (standardized outputs S2-S3, S1-S4) when a certain pressing point in FIG. 1 is pressed and the load is gradually increased. .

  As shown in FIG. 13A, when the linearity of the sensor outputs s1 to s4 decreases, the normalized outputs of the press sensors 5 to 8 vary. That is, for example, when the pressing point p01 in FIG. 1 is pressed, the normalized outputs of the pressing sensors 5 to 8 vary depending on the pressing force (load), and therefore the pressing point calculated by polynomial approximation by multiple regression equations is used as the pressing point. Fluctuates easily depending on pressure (load). In order to stabilize the detection accuracy, as shown in FIG. 13 (b), the standardized output S1 of the pressure sensor 5 located on the diagonal line and the standard of the pressure sensor 8 are obtained when a multiple regression equation of third order or higher is used. It is preferable to use the difference (S1-S4) from the normalized output S4 and the difference (S2-S3) between the normalized output S2 of the pressure sensor 6 and the normalized output S3 of the pressure sensor 7 as variables. As a result, a calculation error accompanying a decrease in linearity can be reduced. Here, the third-order or higher-order multiple regression equation is limited to the case where the polynomial approximation by the second-order multiple regression equation is performed using the above-described difference, compared to the case where each normalized output is used (FIG. 6), This is because the calculated pressing point is greatly deviated from the actual pressing point, and the detection accuracy of the pressing point is greatly lowered. In addition, the point that detection accuracy falls when a difference is used for the quadratic multiple regression equation will be described later with reference to FIG.

  As described above, by using the difference (S1-S4, S2-S3) of each normalized output, the number of terms of the multiple regression equation of the third or higher order can be reduced.

  The following Equation 3 is a third-order multiple regression equation that uses the difference (S1-S4, S2-S3) of the normalized outputs of the pressure sensors 5-8 that are arranged to face each other.

(Formula 3)
X = a1 + a2 ・ (S1-S4) + a3 ・ (S2-S3) + a4 ・ (S1-S4) ² + a5 ・ (S1-S4) (S2-S3) + a6 ・ (S2-S3) ² + a7 ・ (S1-S4) ³ + a8 ・ (S1-S4) ² (S2-S3) + a9 ・ (S1-S4) (S2-S3) ² + a10 ・ (S2-S3) ³
Y = b1 + b2 ・ (S1-S4) + b3 ・ (S2-S3) + b4 ・ (S1-S4) ² + b5 ・ (S1-S4) (S2-S4) + b6 ・ (S2-S3) ² + b7 ・ (S1-S4) ³ + b8 ・ (S1-S4) ² (S2-S3) + b9 ・ (S1-S4) (S2-S3) ² + b10 ・ (S2-S3) ³
a1 to a10 and b1 to b10 are correction coefficients. S1 to S4 are standardized outputs.

  As shown in Equation 3, the number of terms in the multiple regression equation for obtaining the X coordinate and the Y coordinate is 10. The correction coefficient can be obtained by Equation 2 using the steps in FIG.

  FIG. 7 is a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a cubic multiple regression equation shown in Equation 3.

  As shown in FIG. 7, it has been found that the deviation between the actual pressing point and the calculated pressing point can be made smaller than when the quadratic multiple regression equation shown in FIG. 6 is used. In addition, it turned out that the shift | offset | difference of an actual press point and the calculated press point becomes small gradually as it goes inside rather than the outer side of Y1-Y2.

  Subsequently, the following Equation 4 is a fourth-order multiple regression equation using the difference (S1-S4, S2-S3) of each normalized output of each of the pressure sensors 5 to 8 arranged facing each other.

(Formula 4)
X = a1 + a2 ・ (S1-S4) + a3 ・ (S2-S3) + a4 ・ (S1-S4) ² + a5 ・ (S1-S4) (S2-S3) + a6 ・ (S2-S3) ² + a7 ・ (S1-S4) ³ + a8 ・ (S1-S4) ² (S2-S3) + a9 ・ (S1-S4) (S2-S3) ² + a10 ・ (S2-S3) ³ + a11 ・ ( (S1-S4) ⁴ + a12 ・ (S2-S3) ³ (S1-S4) + a13 ・ (S1-S4) ² (S2-S3) ² + a14 ・ (S1-S4) (S2-S3) ³ + a15・ (S2-S3) ⁴
Y = b1 + b2 ・ (S1-S4) + b3 ・ (S2-S3) + b4 ・ (S1-S4) ² + b5 ・ (S1-S4) (S2-S4) + b6 ・ (S2-S3) ² + b7 ・ (S1-S4) ³ + b8 ・ (S1-S4) ² (S2-S3) + b9 ・ (S1-S4) (S2-S3) ² + b10 ・ (S2-S3) ³ + b11 ・ ( (S1-S4) ⁴ + b12 ・ (S1-S4) ³ (S2-S3) + b13 ・ (S1-S4) ² (S2-S3) ² + b14 ・ (S1-S4) (S2-S3) ³ + b15・ (S2-S3) ⁴

a1 to a15 and b1 to b15 are correction coefficients. S1 to S4 are standardized outputs. The correction coefficient can be obtained by Equation 2 using the steps in FIG.
As shown in Equation 4, the number of terms in the multiple regression equation for obtaining the X coordinate and the Y coordinate is 15.

  FIG. 8 is a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using the fourth-order multiple regression equation shown in Equation 4.

  As shown in FIG. 8, when the fourth-order multiple regression equation was used, almost the same result was obtained as when the third-order multiple regression equation in FIG. 7 was used.

  Subsequently, Equation 5 below is a fifth-order multiple regression equation that uses the difference (S1-S4, S2-S3) of the normalized outputs of the pressure sensors 5 to 8 arranged facing each other.

(Formula 5)
X = a1 + a2 ・ (S1-S4) + a3 ・ (S2-S3) + a4 ・ (S1-S4) ² + a5 ・ (S1-S4) (S2-S3) + a6 ・ (S2-S3) ² + a7 ・ (S1-S4) ³ + a8 ・ (S1-S4) ² (S2-S3) + a9 ・ (S1-S4) (S2-S3) ² + a10 ・ (S2-S3) ³ + a11 ・ ( (S1-S4) ⁴ + a12 ・ (S2-S3) ³ (S1-S4) + a13 ・ (S1-S4) ² (S2-S3) ² + a14 ・ (S1-S4) (S2-S3) ³ + a15・ (S2-S3) ⁴ + a16 ・ (S1-S4) ⁵ + a17 ・ (S2-S3) ⁴ (S1-S4) + a18 ・ (S1-S4) ³ (S2-S3) ² + a19 ・ (S1 -S4) ² (S2-S3) ³ + a20 ・ (S1-S4) (S2-S3) ⁴ + a21 ・ (S2-S3) ⁵
Y = b1 + b2 ・ (S1-S4) + b3 ・ (S2-S3) + b4 ・ (S1-S4) ² + b5 ・ (S1-S4) (S2-S4) + b6 ・ (S2-S3) ² + b7 ・ (S1-S4) ³ + b8 ・ (S1-S4) ² (S2-S3) + b9 ・ (S1-S4) (S2-S3) ² + b10 ・ (S2-S3) ³ + b11 ・ ( (S1-S4) ⁴ + b12 ・ (S1-S4) ³ (S2-S3) + b13 ・ (S1-S4) ² (S2-S3) ² + b14 ・ (S1-S4) (S2-S3) ³ + b15・ (S2-S3) ⁴ + b16 ・ (S1-S4) ⁵ + b17 ・ (S2-S3) ⁴ (S1-S4) + b18 ・ (S1-S4) ³ (S2-S3) ² + b19 ・ (S1 -S4) ² (S2-S3) ³ + b20 ・ (S1-S4) (S2-S3) ⁴ + b21 ・ (S2-S3) ⁵

a1 to a21 and b1 to b21 are correction coefficients. S1 to S4 are standardized outputs. The correction coefficient can be obtained by Equation 2 using the steps in FIG.
As shown in Equation 5, the number of terms in the multiple regression equation for obtaining the X coordinate and the Y coordinate is 21.

  FIG. 9 is a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using the fifth order multiple regression equation shown in Equation 5.

  As shown in FIG. 9, when a fifth order multiple regression equation is used, the calculated pressing point can be made to substantially coincide with the actual pressing point, and the second to fourth order multiple regression equations shown in FIGS. Compared with the case of using, it was possible to improve the detection accuracy of the pressing point most effectively.

FIG. 10 shows a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a first-order multiple regression equation. FIG. 10 is a comparative example.
The linear regression equation is expressed by the following Equation 6.

(Formula 6)
X = a + b ・ S1 + c ・ S2 + d ・ S3 + e ・ S4
Y = f + g ・ S1 + h ・ S2 + i ・ S3 + j ・ S4

  Here, a to j are correction coefficients. S1 to S4 are standardized outputs. The correction coefficient can be obtained by Equation 2 using the steps in FIG.

  As shown in FIG. 10, an area where the positional coordinate deviation amount between the calculated pressing point and the actual pressing point can be reduced is near the center of the operation surface, but is a very limited range. It was found that the position coordinate deviation amount between the calculated pressing point and the actual pressing point becomes very large near the periphery.

  FIG. 11 shows a simulation result when the position coordinates of the pressing point are calculated by polynomial approximation using a quadratic multiple regression equation using the difference (S1-S4, S2-S3) of each normalized output. FIG. 11 is a comparative example.

  In Comparative Example 1 shown in FIG. 11, the difference between the normalized output S1 of the pressure sensor 5 and the normalized output S2 of the pressure sensor 6 (S1−S2) and the normalized output S3 of the pressure sensor 7 is standardized. This is a case where the position coordinates of the pressing point are calculated by polynomial approximation using a quadratic multiple regression equation (Formula 7 below) using the difference (S3−S4) of the output S4.

(Formula 7)
X = a1 + a2 ・ (S1-S2) + a3 ・ (S3-S4) + a4 ・ (S1-S2) ² + a5 ・ (S1-S2) (S3-S4) + a6 ・ (S3-S4) ²
Y = b1 + b2 ・ (S1-S2) + b3 ・ (S3-S4) + b4 ・ (S1-S2) ² + b5 ・ (S1-S2) (S3-S4) + b6 ・ (S3-S4) ²

  Moreover, the comparative example 2 shown in FIG. 11 is the difference (S1-S3) of the normalized output S1 of the press sensor 5, the normalized output S3 of the press sensor 7, and the standardized output S2 of the press sensor 6 This is a case where the position coordinates of the pressing point are calculated by polynomial approximation using a quadratic multiple regression equation (the following equation 8) using the difference (S2-S4) of the digitized output S4.

(Formula 8)
X = a1 + a2 ・ (S1-S3) + a3 ・ (S2-S4) + a4 ・ (S1-S3) ² + a5 ・ (S1-S3) (S2-S4) + a6 ・ (S2-S4) ²
Y = b1 + b2 ・ (S1-S3) + b3 ・ (S2-S4) + b4 ・ (S1-S3) ² + b5 ・ (S1-S3) (S2-S4) + b6 ・ (S2-S4) ²

  Moreover, the comparative example 3 shown in FIG. 11 is the difference (S1-S4) of the normalized output S1 of the press sensor 5, the normalized output S4 of the press sensor 8, and the standardized output S2 of the press sensor 6 This is a case where the position coordinates of the pressing point are calculated by polynomial approximation using a quadratic multiple regression equation (Formula 9 below) using the difference (S2−S3) of the digitized output S3.

(Formula 9)
X = a1 + a2 ・ (S1-S4) + a3 ・ (S2-S3) + a4 ・ (S1-S4) ² + a5 ・ (S1-S4) (S2-S3) + a6 ・ (S2-S3) ²
Y = b1 + b2 ・ (S1-S4) + b3 ・ (S2-S3) + b4 ・ (S1-S4) ² + b5 ・ (S1-S4) (S2-S3) + b6 ・ (S2-S3) ²

  As shown in FIG. 11, in each of Comparative Examples 1 to 3, the calculated pressing point is greatly deviated from the actual pressing point, and the calculated pressing point is particularly similar to the actual pressing point. They no longer line up in a grid. Thus, in Comparative Examples 1 to 3, the positional coordinate deviation between the calculated pressing point and the actual pressing point is approximated by a polynomial approximation using a quadratic multiple regression equation using the normalized output as shown in FIG. As a result, the position coordinates of the pressing point were larger than those calculated.

  Therefore, the use of the difference between the standardized outputs is limited to the case where the position coordinates of the pressing point are calculated by polynomial approximation using a third-order or higher multiple regression equation.

  12A shows the difference (S1−S2) between the normalized output S1 of the press sensor 5 and the normalized output S2 of the press sensor 6, and the normalized output S3 of the press sensor 7 and the normalized output of the press sensor 8. The simulation result of calculating the position coordinates of the pressing point by polynomial approximation using the third order multiple regression equation using the difference of S4 (S3-S4), FIG. 12B shows the normalized output S1-pressing sensor of the pressing sensor 5 7 using the difference of the normalized output S3 (S1-S3) and the normalized output S2 of the pressure sensor 6 -the difference of the normalized output S4 of the pressure sensor 8 (S2-S4). It is the simulation result which computed the position coordinate of the press point by approximation.

  As shown in FIG. 12, the calculated pressing points are greatly deviated from the actual pressing points, and the calculated pressing points are not arranged in a substantially lattice pattern like the actual pressing points. As described above, it is necessary to use the difference (S1−S4, S2−S3) of the normalized output of the pressure sensor arranged oppositely on the diagonal line as the variable used in the multiple regression equation of the third or higher order. (See FIGS. 7 to 9). In addition, when each press sensor 5-8 is arrange | positioned like FIG. 14, the difference (S1-S4) of the press sensor 5 and the press sensor 8 which opposes in Y1-Y2 direction, and the press which opposes in X1-X2 direction The difference (S2-S3) between the sensor 6 and the pressure sensor 7 is used.

  The characteristic part of the input device 1 in the present embodiment includes a calibration mode. In the calibration mode, the calibration pressing points p01 to p35 on the operation surface 4a having a number larger than the number of terms in the multiple regression equation are displayed. The total pressure at each of the pressure points p01 to p35 is calculated by adding the pressure sensors obtained when the calibration pressure points p01 to p35 are pressed, and the total output at each of the pressure points p01 to p35 is calculated. Each sensor output is divided by the total output at each of the pressing points p01 to p35 to calculate each normalized output, and the output data of the position coordinates of each of the calibration pressing points p01 to p35 and the respective normalized outputs is calculated. On the basis of this, there is a calibration method for calculating a correction coefficient, and a control unit 2 capable of executing the calibration mode.

  In this embodiment, as described above, each sensor output of each of the pressure sensors 5 to 8 is divided by the total output and normalized. As a result, the normalized output can be easily calculated.

  Then, the correction coefficients a1 to a15 and b1 to b15 are obtained using the position coordinates of the calibration pressing points p01 to p35 and the output data of each normalized output. Therefore, in the calibration mode, when calculating the correction coefficients a1 to a15 and b1 to b15, it does not matter how much load the sensor output is obtained. That is, in the present embodiment, when calculating the correction coefficient, the load parameter is not necessary and load measurement is not necessary. Therefore, the burden on the user at the time of calibration can be reduced, and a load measuring device is not necessary. The calibration is not performed by the user, but may be performed using a dedicated device or the like. However, in any case, since it is not necessary to measure how much load is pressed, the calibration can be simplified.

  In this embodiment, the determination standard for normal measurement when pressing each calibration pressing point p01 to p35 is, for example, pressing each calibration pressing point p01 to p35, and the total output obtained from each pressing sensor 5 to 8 Is within the data acquisition range shown in FIG. 13, it is determined that the measurement is normal, and the color of the calibration press point determined to be normal at this time changes or blinks, etc. Can be informed.

  In the case of the comparative example shown in FIG. 15, when a load is applied to the calibration pressing point in step ST15, it is determined that the measurement is normal only when a predetermined load is applied, and the measurement result obtained in step ST16. Is saved. In step ST17, the correction value is calculated. However, in actual use as shown in FIG. 16, calibration cannot be performed as in the present embodiment shown in FIG. That is, as shown in FIG. 16, the sensor output of each press sensor is measured at step ST18, and at step ST19, the position coordinates of the press point are calculated using the correction value obtained at step ST17 of FIG. be able to. However, in the configuration of the comparative example shown in FIG. 15 and FIG. 16, since the load parameter is included in the change of the correction value, it is impossible to perform calibration without a load measuring function.

  On the other hand, in this embodiment, by dividing each sensor output by all outputs and normalizing, the load parameter is not included in the calculation of the correction value (correction coefficient), and therefore the user or the like calibrates as shown in FIG. It is possible to change the correction value (correction coefficient) simply and freely by selecting a mode. Therefore, when the sensitivity of each of the pressure sensors 5 to 8 changes due to changes over time and the detection accuracy of the pressed point is lowered, the correction coefficients a1 to a15 and b1 to b15 can be easily reset.

  In the calibration in the present embodiment, the multiple regression equation may be linear. In the first case, as shown in FIG. 10, the variation between the calculated pressing point and the actual pressing point becomes large. For example, the calculated pressing point is actually in the X1-X2 direction at the center of the Y1-Y2 direction. There is also a portion that almost coincides with the pressing point. The present embodiment can also be used for calibration for obtaining a correction coefficient of a first-order multiple regression equation because it depends on which part of the pressing point detection accuracy is required depending on the use application of the input device.

  However, it is preferable to use a quadratic or higher regression equation. Thus, by performing calibration and optimizing the correction coefficient, it is possible to improve the detection accuracy of the pressed point more effectively.

  Further, in the present embodiment, when a calculation error occurs due to the inferior linearity of each press sensor by using the difference between the standardized outputs of each press sensor arranged to face each other, as described above, the pressure sensors are arranged to face each other. The calculation error can be reduced by using the difference between the normalized outputs of the pressure sensors. Thus, by performing calibration and optimizing the correction coefficient, it is possible to improve the detection accuracy of the pressed point more effectively. However, when using a variable for the difference, it is limited to a multiple regression equation of third order or higher.

  Further, by using the difference, the number of terms in the multiple regression equation can be reduced, and the calculation burden on the control unit can be reduced.

  In this embodiment, in step ST19 in FIG. 16, calculation for obtaining the position of the center of gravity of the load may be performed.

p00 Press point (during use)
p01 to p35 Calibration pressing point 1 Input device 2 Control unit 4 Operation panel 4a Operation surface 5 to 8 Press sensor 20 Image display device

Claims (11)

An operation panel;
A plurality of pressure sensors arranged on the back side of the operation surface of the operation panel;
A control unit capable of executing a calibration mode for obtaining a correction coefficient of a multiple regression equation used when calculating the position coordinates of the pressing point;
In the control unit, each of the pressure sensors obtained when the plurality of calibration pressing points on the operation surface having a number larger than the number of terms of the multiple regression equation are pressed and each calibration pressing point is pressed. To obtain the total output at each calibration pressure point, and divide the sensor output at each calibration pressure point by the total output at each calibration pressure point to standardize each output. An input device that calculates an output and calculates the correction coefficient based on output data of a position coordinate of each calibration pressing point and each normalized output.   The input device according to claim 1, wherein a quadratic or higher multiple regression equation is used.   The pressure sensor is arranged opposite to the periphery of the operation surface in two different directions, calculates a difference between the normalized outputs of the pressure sensors arranged opposite to each other, and uses each difference to obtain a third or higher order. The input device according to claim 2, wherein the correction coefficient of the multiple regression equation is calculated.   The said press sensor is arrange | positioned at the four corners of the peripheral part of the said operation surface, or is opposingly arranged by making the X direction and Y direction orthogonal through the approximate center of the said operation surface into the said 2 directions. Input device.   5. The control unit according to claim 1, wherein the control unit controls to switch between a normal mode in which a pressing point on the operation surface is calculated by polynomial approximation based on the multiple regression equation and the calibration mode. 6. Input device.   6. The input device according to claim 1, wherein the calculation of each normalized output is executed when all the outputs are within a predetermined range. In a calibration mode for obtaining a correction coefficient of a multiple regression equation used when calculating a position coordinate of a pressing point of an input device having an operation panel in which a plurality of pressing sensors are arranged on the back side with respect to the operating surface.
Pressing each calibration pressing point on the operation surface having a number larger than the number of terms in the multiple regression equation; and
The total output at each calibration pressure point is calculated by adding the pressure sensors obtained when the calibration pressure points are pressed, and the sensor output at each calibration pressure point is calculated as the calibration value. Calculating each normalized output by dividing by the total output at the pressure point,
Calculating the correction coefficient based on the output data of the position coordinates of each calibration pressing point and each normalized output;
A calibration method for an input device, comprising:   8. The input device calibration method according to claim 7, wherein a quadratic or higher multiple regression equation is used.   The pressure sensor is arranged opposite to the periphery of the operation surface in two different directions, calculates a difference between the normalized outputs of the pressure sensors arranged opposite to each other, and uses each difference as the output data. The calibration method for an input device according to claim 8, wherein each correction coefficient of the multiple regression equation of the next order or more is calculated.   10. The control unit according to claim 7, wherein the control unit controls to switch between a normal mode in which the pressing point on the operation surface is calculated by polynomial approximation based on the multiple regression equation and the calibration mode. 10. Calibration method for input devices.   The input device calibration method according to claim 7, wherein the calculation of each normalized output is executed when all the outputs are within a predetermined range.

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