A touch information determination method and device, and a storage medium

Abstract

A touch information determination method and device, and a storage medium, the touch information determination method is applied to a touch device including a plurality of sensors, the touch information determination method comprises: dotting a plurality of preset calibration positions under a preset calibration force, acquiring a dotting signal of the sensor, generating a characteristic value curve of the sensor according to a characteristic value of the dotting signal, calibrating the characteristic value curves of the plurality of sensors so that the sensitivities of the plurality of sensors are consistent, and taking the calibrated characteristic value curve as a standard force template; when touch is detected, acquiring signals of the plurality of sensors, and determining a touch force according to the signals of the plurality of sensors, a touch position, and the standard force template. The scheme provided in the embodiment can reduce the amount of calculation and improve the calculation accuracy by pre-generating a standard force template and calculating the touch force based on the standard force template.

Description

Technical Field

[0001] This article relates to touch technology, and more particularly to a method and device for determining touch information and a storage medium. Background Technology

[0002] Touchscreen devices offer numerous advantages, including durability, fast response, space-saving design, and ease of communication, leading to their widespread application in industry, medicine, and communications. Touchscreen devices typically incorporate multiple sensors, calculating touch position and pressure by detecting sensor signals to enable interaction. However, calculating touch pressure in related technologies is relatively complex and inefficient. Summary of the Invention

[0003] This application provides a method, apparatus, and storage medium for determining touch information, which can reduce the computational complexity of touch information and improve efficiency.

[0004] This application provides a method for determining touch information, applied to a touch device including multiple sensors. The method for determining touch information includes:

[0005] Under a preset calibration force, multiple preset calibration positions are marked to obtain the marking signal of the sensor. The feature value curve of the sensor is generated based on the feature value of the marking signal. The feature value curves of the multiple sensors are calibrated to make the sensitivity of the multiple sensors consistent. The calibrated feature value curve is used as a standard force template.

[0006] When a touch is detected, the signals from the multiple sensors are acquired, and the touch force is determined based on the signals from the multiple sensors, the touch position, and the standard force template.

[0007] In an exemplary embodiment, the step of marking multiple preset calibration positions under a preset calibration force to obtain the marking signal of the sensor includes: marking multiple preset calibration positions multiple times under a preset calibration force to obtain multiple marking signals of the sensor at the same preset calibration position;

[0008] The step of generating the feature value curve of the sensor based on the feature value of the dotted signal includes:

[0009] The mean value of the feature values ​​of multiple dot signals at the same preset calibration position of the same sensor is obtained, and the feature value curve of the sensor is generated based on the mean value of the feature values.

[0010] In one exemplary embodiment, calibrating the feature curves of the plurality of sensors to make the sensitivity of the plurality of sensors consistent includes:

[0011] Select one feature value curve from the feature value curves of the plurality of sensors as a reference feature value curve, and select a reference point on the reference feature value curve;

[0012] Based on the difference between the values ​​of corresponding points on the feature curve outside the reference feature curve and the reference point, the corresponding sensor calibration coefficient is determined. The calibration coefficient makes the value of the corresponding point equal to the value of the reference point after calibration.

[0013] The eigenvalue curves outside the reference eigenvalue curves are calibrated using the corresponding calibration coefficients.

[0014] In an exemplary embodiment, determining the touch force based on the signals from the plurality of sensors, the touch position, and the standard force template includes:

[0015] A force vector y is determined based on the touch position and the standard force template. The force vector y includes the values ​​of the multiple sensors corresponding to the touch position in the standard force template, according to k = (y...). T y) -1 y T x determines the value of k, and the touch force is determined to be k * the preset calibration force, where x is the vector formed by the signals of the multiple sensors after being calibrated by the corresponding calibration coefficients.

[0016] In an exemplary embodiment, the method further includes: normalizing the values ​​of the calibrated feature curves of the plurality of sensors at the same preset calibration position to generate a standard position template. The normalization process includes: normalizing the values ​​of the calibrated feature curves of the plurality of sensors corresponding to the same preset calibration position using the maximum value among the values ​​of the calibrated feature curves of the plurality of sensors corresponding to the preset calibration position.

[0017] The touch position is determined based on the signals from the multiple sensors calibrated with corresponding calibration coefficients and the standard position template.

[0018] In an exemplary embodiment, determining the touch position based on the signals from the plurality of sensors and the standard position template includes:

[0019] The vector x formed by calibrating the signals from the multiple sensors with corresponding calibration coefficients is normalized to obtain x';

[0020] Calculate the distance between x' and the vector formed by the normalized feature value curves of multiple sensors corresponding to the preset calibration position in the standard position template, and take the preset calibration position corresponding to the vector with the smallest distance as the touch position.

[0021] In one exemplary embodiment, the method further includes: determining a downsampling level;

[0022] The process of acquiring the signals from the plurality of sensors includes: acquiring data from the plurality of sensors at a preset sampling frequency, downsampling the acquired data at the downsampling level, and using the downsampled data as the signals from the plurality of sensors.

[0023] In one exemplary embodiment, determining the downsampling level includes:

[0024] Data from multiple sensors is collected at a preset sampling frequency, and the touch position and touch force are calculated based on the collected data;

[0025] The collected data is downsampled at multiple downsampling levels. Touch position and touch force are calculated based on the downsampled data at different downsampling levels to obtain the touch position and touch force corresponding to each downsampling level. A position linearity parameter is determined based on the touch position corresponding to different downsampling levels, and a force continuity parameter is determined based on the touch force corresponding to different downsampling levels. The downsampling level is determined based on the position linearity parameter and the force continuity parameter. The position linearity parameter is determined based on the average touch position change between adjacent times and the number of minimum resolvable position units equal to the distance between adjacent touch positions. The force continuity parameter indicates the continuity of touch force changes across multiple times.

[0026] In an exemplary embodiment, the positional linearity parameter is determined according to the following formula:

[0027]

[0028]

[0029] Where L is the position linearity parameter, N is a natural number and N is greater than or equal to 2, S k ΔS represents the touch position at time k, and ΔS represents the smallest unit of position resolution.

[0030] In an exemplary embodiment, the force continuity parameter is determined according to the following formula:

[0031]

[0032] Where C is the force continuity parameter, F k Let N represent the touch force at time k, where N is a natural number and N is greater than or equal to 3.

[0033] In an exemplary embodiment, determining the downsampling level based on the position linearity parameter and the force continuity parameter includes:

[0034] Select the downsampling level corresponding to the touch force and touch position that maximizes the product of the position linearity parameter and the force continuity parameter;

[0035] Alternatively, choose one that makes y i The maximum touch pressure and the corresponding downsampling level for the touch location:

[0036]

[0037] Among them, w L For the preset weights of the position linearity parameter, w C w is the preset weight for the force continuity parameter. L The value of w ranges from 0 to 1. C The value of is between 0 and 2, and L i C is the position linearity parameter determined based on the touch position corresponding to the i-th downsampling level. i The force continuity parameter is determined based on the touch force corresponding to the i-th downsampling level, where i is from 1 to M and M is the number of downsampling levels.

[0038] This disclosure provides a touch information determination device, including a memory and a processor. The memory stores a program, and when the program is read and executed by the processor, it implements the touch information determination method described in any of the above embodiments.

[0039] This disclosure provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the touch information determination method described in any of the above embodiments.

[0040] Compared with related technologies, this application embodiment includes a touch information determination method and apparatus, and a storage medium. The touch information determination method is applied to a touch device including multiple sensors. The touch information determination method includes: marking multiple preset calibration positions under a preset calibration force to obtain the marking signals of the sensors; generating feature value curves of the sensors based on the feature values ​​of the marking signals; calibrating the feature value curves of the multiple sensors to make the sensitivity of the multiple sensors consistent; and using the calibrated feature value curves as a standard force template; when a touch is detected, acquiring the signals of the multiple sensors; and determining the touch force based on the signals of the multiple sensors, the touch position, and the standard force template. The solution provided in this embodiment reduces the amount of calculation and achieves higher calculation accuracy by pre-generating a standard force template and calculating the touch force based on the standard force template.

[0041] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the solutions described in the description and the accompanying drawings. Attached Figure Description

[0042] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0043] Figure 1 A flowchart of a touch information determination method provided in this embodiment of the disclosure;

[0044] Figure 2 Flowchart of the method for determining the standard position template and standard force template provided in the embodiments of this disclosure;

[0045] Figure 3 A schematic diagram of a dot signal provided for an exemplary embodiment;

[0046] Figure 4 A schematic diagram of the peak value of the dot signal provided for an exemplary embodiment;

[0047] Figure 5 A schematic diagram of the feature value curve of the dot signal provided for an exemplary embodiment;

[0048] Figure 6 A schematic diagram of a standard force template provided for an exemplary embodiment;

[0049] Figure 7 A schematic diagram of a standard location template provided for an exemplary embodiment;

[0050] Figure 8 A flowchart of a touch information determination method provided as an exemplary embodiment;

[0051] Figure 9 A flowchart of a downsampling level determination method provided as an exemplary embodiment;

[0052] Figure 10 A schematic diagram of a touch information determination device provided for an exemplary embodiment;

[0053] Figure 11 A schematic diagram of a computer-readable storage medium provided for an exemplary embodiment. Detailed Implementation

[0054] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0055] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0056] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0057] This disclosure provides a touch control device, which may include a touch panel, a force module, and a touch module. The touch module may include multiple touch electrodes. The touch module locates the touch points based on the electrical signals from the touch electrodes to obtain touch coordinates. The touch module can also identify whether the touch is made by a palm or a finger, and send the identified information and touch coordinates to the force module. The force module may include multiple elastic wave sensors, which can receive elastic wave signals and convert them into voltage signals. The force module calculates the force value based on the fluctuations in the voltage signals. The force module can calculate the force value at different locations on the touch panel. The force module can report the force value to the main controller of the touch device, and the main controller performs touch processing based on the touch coordinates and force value. The elastic wave sensor may be any one or more of a piezoelectric ceramic sensor, a resistive strain gauge, a fiber Bragg grating sensor, or a thin-film piezoelectric sensor.

[0058] Figure 1 This is a flowchart of a touch information determination method provided in an embodiment of this disclosure. Figure 1 As shown, this disclosure provides a method for determining touch information, applied to a touch device including multiple sensors. The method for determining touch information includes:

[0059] Step 101: Mark multiple preset calibration positions under a preset calibration force to obtain the marking signal of the sensor, generate the feature value curve of the sensor based on the feature value of the marking signal, calibrate the feature value curves of the multiple sensors to make the sensitivity of the multiple sensors consistent, and use the calibrated feature value curve as a standard force template.

[0060] Step 102: When a touch is detected, acquire the signals from the multiple sensors, and determine the touch force based on the signals from the multiple sensors, the touch position, and the standard force template.

[0061] In conventional solutions, touch force calculations are mostly performed by directly adding or weighting the amplitudes of signals from multiple sensors, which suffers from insufficient force resolution. The solution provided in this embodiment calibrates the sensors by pre-generating a standard force template, resulting in more accurate signals and improved force calculation accuracy. Calculating touch force based on the standard force template reduces computational load and achieves higher accuracy.

[0062] In one exemplary embodiment, the multiple preset calibration positions are multiple positions on the touch panel, which can be evenly distributed on the touch panel.

[0063] In one exemplary embodiment, the feature value may be a peak value.

[0064] In an exemplary embodiment, the step of marking multiple preset calibration positions under a preset calibration force to obtain the marking signal of the sensor includes: marking multiple preset calibration positions multiple times under a preset calibration force to obtain multiple marking signals of the sensor at the same preset calibration position;

[0065] The step of generating the feature value curve of the sensor based on the feature value of the dotted signal includes:

[0066] The mean value of the feature values ​​of multiple dot signals at the same preset calibration position of the same sensor is obtained, and the feature value curve of the sensor is generated based on the mean value of the feature values.

[0067] The solution provided in this embodiment reduces errors and improves accuracy by marking the same preset calibration position multiple times.

[0068] In one exemplary embodiment, calibrating the feature curves of the plurality of sensors to make the sensitivity of the plurality of sensors consistent includes:

[0069] Select one feature value curve from the feature value curves of the plurality of sensors as a reference feature value curve, and select a reference point on the reference feature value curve;

[0070] Based on the difference between the values ​​of corresponding points on the feature curve outside the reference feature curve and the reference point, the corresponding sensor calibration coefficient is determined, and the calibration coefficient makes the value of the corresponding point equal to the value of the reference point after calibration.

[0071] The eigenvalue curves outside the reference eigenvalue curves are calibrated using the corresponding calibration coefficients.

[0072] The corresponding point refers to a point selected on an eigenvalue curve outside the baseline eigenvalue curve according to the same selection principle used for selecting reference points. For example, the reference point could be the point where the peak value of the baseline eigenvalue curve is located. For any eigenvalue curve outside the baseline eigenvalue curve, the corresponding point is the point where the peak value of that eigenvalue curve is located.

[0073] In an exemplary embodiment, any eigenvalue curve can be selected as the reference eigenvalue curve.

[0074] In an exemplary embodiment, the calibration coefficient makes the value of the corresponding point equal to the value of the reference point after calibration, that is, the product of the value of the corresponding point and the calibration coefficient is equal to the value of the reference point.

[0075] In an exemplary embodiment, determining the touch force based on the signals from the plurality of sensors, the touch position, and the standard force template includes:

[0076] A force vector y is determined based on the touch position and the standard force template. The force vector y includes the values ​​of the multiple sensors corresponding to the touch position in the standard force template, according to k = (y...). T y) -1 y T x determines the value of k, and the touch force is determined to be k * the preset calibration force, where x is a vector formed by the signals of the multiple sensors after being calibrated by the corresponding calibration coefficients.

[0077] In an exemplary embodiment, the method further includes: normalizing the values ​​of the calibrated feature curves of the plurality of sensors at the same preset calibration position to generate a standard position template. The normalization process includes: normalizing the values ​​of the calibrated feature curves of the plurality of sensors corresponding to the same preset calibration position using the maximum value among the values ​​of the calibrated feature curves of the plurality of sensors corresponding to the preset calibration position.

[0078] The touch position is determined based on the signals from the multiple sensors calibrated with corresponding calibration coefficients and the standard position template.

[0079] In general solutions, the touch position is obtained by calculating it using the centroid method based on multi-sensor data, which has the disadvantages of poor accuracy and poor anti-interference ability. The solution provided in this embodiment determines the touch position by using a standard position template, which reduces the amount of calculation, increases the calculation accuracy, and strengthens the anti-interference ability.

[0080] In an exemplary embodiment, determining the touch position based on the signals from the plurality of sensors and the standard position template includes:

[0081] The vector x formed by calibrating the signals from the multiple sensors with corresponding calibration coefficients is normalized to obtain x';

[0082] Calculate the distance between x' and the vector formed by the normalized feature value curves of multiple sensors corresponding to the preset calibration position in the standard position template, and take the preset calibration position corresponding to the vector with the smallest distance as the touch position.

[0083] In one exemplary embodiment, the method further includes: determining a downsampling level;

[0084] The process of acquiring the signals from the plurality of sensors includes: acquiring data from the plurality of sensors at a preset sampling frequency, downsampling the acquired data at the downsampling level, and using the downsampled data as the signals from the plurality of sensors.

[0085] In one exemplary embodiment, determining the downsampling level includes:

[0086] Data from multiple sensors is collected at a preset sampling frequency, and the touch position and touch force are calculated based on the collected data;

[0087] The collected data is downsampled at multiple downsampling levels. Touch position and touch force are calculated based on the downsampled data at different downsampling levels to obtain the touch position and touch force corresponding to each downsampling level. A position linearity parameter is determined based on the touch position corresponding to different downsampling levels, and a force continuity parameter is determined based on the touch force corresponding to different downsampling levels. The downsampling level is determined based on the position linearity parameter and the force continuity parameter. The position linearity parameter is determined based on the average touch position change between adjacent times and the number of minimum resolvable position units equal to the distance between adjacent touch positions. The force continuity parameter indicates the continuity of touch force changes across multiple times.

[0088] In an exemplary embodiment, the position linearity parameter can be determined according to the following formula:

[0089]

[0090]

[0091] Where L is the position linearity parameter, N is a natural number and N is greater than or equal to 2, S k ΔS represents the touch position at time k, and ΔS represents the smallest unit of position resolution.

[0092] In an exemplary embodiment, the force continuity parameter can be determined according to the following formula:

[0093]

[0094] Where C is the force continuity parameter, F k Let N represent the touch force at time k, where N is a natural number and N is greater than or equal to 3.

[0095] In an exemplary embodiment, determining the downsampling level based on the position linearity parameter and the force continuity parameter may include:

[0096] Select the downsampling level corresponding to the touch force and touch position that maximizes the product of the position linearity parameter and the force continuity parameter;

[0097] Alternatively, choose one that makes y i The maximum touch pressure and the corresponding downsampling level for the touch location:

[0098]

[0099] Among them, wL For the preset weights of the position linearity parameter, w C w is the preset weight for the force continuity parameter. L The value of w ranges from 0 to 1. C The value of is between 0 and 2, and L i C is the position linearity parameter determined based on the touch position corresponding to the i-th downsampling level. i The force continuity parameter is determined based on the touch force corresponding to the i-th downsampling level, where i is from 1 to M and M is the number of downsampling levels.

[0100] The technical solution of the embodiments of this application will be further described below through a specific example.

[0101] Figure 2 A flowchart illustrating a method for determining a standard position template and a standard force template, provided for an exemplary embodiment. In this embodiment, the touch device includes three sensors. Figure 2 As shown, the method for determining the standard position template and standard force template provided in this embodiment includes:

[0102] Step 201: Apply multiple calibration points to multiple preset calibration devices under the same preset calibration force;

[0103] In this embodiment, 21 preset calibration positions are included. Each preset calibration position can be marked three times to avoid accidental errors caused by factors such as the marking equipment.

[0104] The collected point signals from the three sensors are as follows: Figure 3 As shown, the three sensors are the first sensor CH1, the second sensor CH2, and the third sensor CH3. The dot signal of the first sensor CH1 is represented by a dashed line, the dot signal of the second sensor CH2 is represented by a solid line, and the dot signal of the third sensor CH3 is represented by a dotted line.

[0105] Step 202: Determine the characteristic value of each dot signal.

[0106] In this embodiment, the characteristic value is the peak value. For example... Figure 4 As shown.

[0107] Step 203: Calculate the average value of the characteristic values ​​of the three point signals of each sensor at each preset calibration position (in this embodiment, calculate the average value of the peak values), and generate a characteristic value curve, such as... Figure 5 As shown in the figure. The characteristic curve of the first sensor CH1 is represented by a dashed line, the characteristic curve of the second sensor CH2 is represented by a solid line, and the characteristic curve of the third sensor CH3 is represented by a dotted line.

[0108] In one exemplary embodiment, if there are multiple dot signals, the peak values ​​of some of the dot signals can be selected and averaged to generate a feature curve. For example, obvious error values ​​in the peak values ​​can be removed before averaging, etc.

[0109] Step 204: Determine the calibration coefficient of each sensor, calibrate the sensitivity of the multiple sensors to be consistent, obtain the characteristic value curves of the three sensors after calibration, and save the waveform as a standard force template.

[0110] like Figure 6 As shown, the characteristic curve of the first sensor CH1 is taken as the reference characteristic curve. The peak value of the reference characteristic curve is used as a reference point. The characteristic curve of the second sensor CH2 is calibrated using the second calibration parameter so that the peak value of the second sensor CH2 is the same as the peak value of the reference characteristic curve (the values ​​of the remaining points of the second sensor CH2 are also calibrated using the second calibration parameter). The characteristic curve of the third sensor CH3 is calibrated using the third calibration parameter so that the peak value of the third sensor CH3 is the same as the peak value of the reference characteristic curve (the values ​​of the remaining points of the third sensor CH3 are also calibrated using the third calibration parameter). This yields... Figure 6 The waveform shown is saved as a standard force template.

[0111] Step 205: Normalize the amplitude of each position in the standard force template to obtain the standard position template.

[0112] like Figure 7 As shown, for any position, the maximum value among the three eigenvalue curves at that position is normalized to 1, and the remaining values ​​are divided by this maximum value to obtain new values, thus obtaining... Figure 7 The normalized eigenvalue curve shown is used as a standard location template.

[0113] Figure 8 This is a flowchart illustrating a touch information determination method provided for an exemplary embodiment. In this embodiment, the touch device includes three sensors. Figure 8 As shown, the touch information determination method provided in this embodiment includes:

[0114] Step 801: Real-time data x = [x1, x2, x3] from three sensors is collected and normalized to obtain normalized data x'. The normalization method is as follows:

[0115]

[0116] Right now

[0117] The values ​​x1, x2, and x3 are data calibrated using the corresponding sensor calibration coefficients.

[0118] Step 802: Calculate the distance between x' and each preset calibration position in the standard position template, and select the preset calibration position with the smallest distance as the current touch position;

[0119] That is, Position = min(distance(x',P1),..., distance(x',Pn)), where Pi represents... Figure 7 The values ​​of the three sensors corresponding to the i-th preset calibration position in the standard position template shown. For example, P10 = (X_10_1, X_10_2, X_10_3). distance(x',P1) is, for example, the Euclidean distance.

[0120] Step 803: Select the force vector y corresponding to the current touch position from the standard force template. The touch force value can be calculated by minimizing the residual using the least squares method.

[0121]

[0122] Solving using the normal equation, we get k = (y T y) -1 y T x

[0123] For example, such as Figure 6 As shown, if the touch position is the 10th preset calibration position, then the force vector is y = (y1, y2, y3).

[0124] When the preset calibration force of the standard force template is 300g, if k is obtained in a certain touch calculation, it means that the touch force value at this time is k*300g.

[0125] Figure 9 A flowchart illustrating a method for determining a downsampling level, provided as an exemplary embodiment. Figure 9 As shown, the method for determining the downsampling level provided in this embodiment includes:

[0126] Step 901: Collect data from the multiple sensors in real time at a preset sampling frequency;

[0127] Step 902: Determine whether the collected data is greater than the preset trigger threshold. If yes, proceed to step 902; otherwise, return to step 901.

[0128] Step 903: Calculate the touch position and touch force based on the collected data;

[0129] In this embodiment, the data that has not been downsampled is referred to as the raw data;

[0130] Step 904: Determine whether the change in touch position is less than a preset threshold; if yes, proceed to step 905; otherwise, proceed to step 906.

[0131] Step 905: Determine whether the preset release condition is met. If it is met, proceed to step 914; otherwise, proceed to step 901.

[0132] The preset release condition could be, for example, that the touch force is less than the release threshold, etc.

[0133] Step 906: Downsample the original data by the downsampling level;

[0134] Multiple downsampling levels can be preset, and downsampling can be performed at each preset downsampling level.

[0135] Step 907: Calculate the touch position and touch force based on the data obtained after downsampling at different downsampling levels; the calculation method is the same as in the previous embodiment and will not be repeated here.

[0136] Step 908: Calculate the position linearity parameter and the force continuity parameter based on the touch position and touch force;

[0137] The position linearity parameter consists of two parts: position discrimination and position subdivision. Position discrimination is obtained by calculating the average position change between adjacent time points; a higher value indicates that the downsampling level results in greater position discrimination across different time points, which is beneficial for real-time calculation and linear output of touch positions. Position subdivision is calculated by counting the number of adjacent time points where the distance equals the minimum resolvable position unit; a higher value indicates that the downsampling level results in better position subdivision across different time points, which is beneficial for calculating all touch positions without omission. Position discrimination can be used... This indicates that the level of location granularity can be expressed as... This means that, by combining the two, the positional linearity parameter can be determined according to the following formula:

[0138]

[0139]

[0140] Where L is the position linearity parameter, N is a natural number and N is greater than or equal to 2, S k This represents the touch position at time k, that is, the touch position calculated based on the data at time k. This indicates the number of statistically adjacent time intervals where the distance is equal to ΔS, where ΔS represents the smallest unit of resolution.

[0141] The force continuity parameter can be determined according to the following formula:

[0142]

[0143] Where C is the force continuity parameter, F kC represents the touch force at time k, where N is a natural number and N is greater than or equal to 3. The force continuity parameter describes the continuity of force changes over the first N time steps; a larger C value indicates more continuous force.

[0144] Step 909: Determine the downsampling level based on the position linearity parameter and the force continuity parameter;

[0145] For example, select the downsampling level that maximizes the product of the position linearity parameter and the force continuity parameter;

[0146] Alternatively, weights w can be assigned to the position linearity parameter and the force continuity parameter respectively. L and w C Constructing combined indicators Then, select the downsampling level that maximizes the combined index; where w L The value range of w is 0 to 1. C The value range of is 0 to 2, and i ranges from 1 to M, where M is the number of downsampling levels.

[0147] Step 910: Determine whether the preset release condition is met. If it is met, proceed to step 914; otherwise, proceed to step 911.

[0148] Step 911: Collect data from the multiple sensors in real time at a preset sampling frequency;

[0149] Step 912: Downsample the collected data based on the selected downsampling level;

[0150] Step 913: Calculate the touch location and touch force based on the downsampled data;

[0151] Step 914: Perform touch gesture recognition based on all touch locations and touch strengths during this interaction, then end.

[0152] For example, the path and force change waveforms formed at all positions in this interaction can be matched with preset gestures to achieve gesture recognition.

[0153] like Figure 10 As shown, this embodiment of the present disclosure provides a touch information determination device 100, including a memory 1010 and a processor 1020. The memory 1010 stores a program, which, when read and executed by the processor 1020, implements the touch information determination method described in any of the above embodiments.

[0154] like Figure 11As shown, this disclosure provides a computer-readable storage medium 110, which stores one or more programs 120. The one or more programs 120 can be executed by one or more processors to implement the touch information determination method described in any of the above embodiments.

[0155] This disclosure provides a touch device, including the touch information determination device described above.

[0156] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A method for determining touch information, characterized in that, A touch device comprising multiple sensors, wherein the sensors are elastic wave sensors, and the touch information determination method includes: Determine the downsampling level; mark multiple preset calibration positions under a preset calibration force to obtain the mark signal of the sensor; generate the feature value curve of the sensor based on the feature value of the mark signal; calibrate the feature value curves of the multiple sensors to make the sensitivity of the multiple sensors consistent; and use the calibrated feature value curve as a standard force template. When a touch is detected, data from the multiple sensors are collected at a preset sampling frequency. The collected data is downsampled at the downsampling level, and the downsampled data is used as the signal of the multiple sensors. The touch force is determined based on the signals of the multiple sensors, the touch position, and the standard force template. The downsampling level is determined as follows: Data from multiple sensors is collected at a preset sampling frequency, and the touch position and touch force are calculated based on the collected data; The collected data is downsampled at multiple downsampling levels. The touch position and touch force are calculated based on the downsampled data at different downsampling levels to obtain the touch position and touch force corresponding to each downsampling level. The position linearity parameter is determined based on the touch position corresponding to different downsampling levels, and the force continuity parameter is determined based on the touch force corresponding to different downsampling levels. The downsampling level corresponding to the touch force and touch position that maximizes the product of the position linearity parameter and the force continuity parameter is selected. Or, choose to make The maximum touch pressure and the corresponding downsampling level for the touch location: in, Preset weights for the position linearity parameter. The preset weights for the force continuity parameter, The value ranges from 0 to 1. The value of is between 0 and 2, and , The position linearity parameter is determined based on the touch position corresponding to the i-th downsampling level. The force continuity parameter is determined based on the touch force corresponding to the i-th downsampling level, where i ranges from 1 to M, and M is the number of downsampling levels; wherein, the position linearity parameter is determined according to the following formula: Where L is the positional linearity parameter, and N is a natural number greater than or equal to 2. This represents the touch position at time k. Indicates the smallest unit of resolution; The force continuity parameter is determined according to the following formula: Wherein, C is the force continuity parameter. Let N represent the touch force at time k, where N is a natural number and N is greater than or equal to 3.

2. The touch information determination method according to claim 1, characterized in that, The step of marking multiple preset calibration positions under a preset calibration force to obtain the marking signal of the sensor includes: marking multiple preset calibration positions multiple times under a preset calibration force to obtain multiple marking signals of the sensor at the same preset calibration position. The step of generating the feature value curve of the sensor based on the feature value of the dotted signal includes: The mean value of the feature values ​​of multiple dot signals at the same preset calibration position of the same sensor is obtained, and the feature value curve of the sensor is generated based on the mean value of the feature values.

3. The touch information determination method according to claim 1, characterized in that, The eigenvalue curves of the multiple sensors are calibrated to ensure consistent sensitivity across the sensors, including: Select one feature value curve from the feature value curves of the plurality of sensors as a reference feature value curve, and select a reference point on the reference feature value curve; Based on the difference between the values ​​of corresponding points on the feature curve outside the reference feature curve and the reference point, the corresponding sensor calibration coefficient is determined. The calibration coefficient makes the value of the corresponding point equal to the value of the reference point after calibration. The eigenvalue curves outside the reference eigenvalue curves are calibrated using the corresponding calibration coefficients.

4. The touch information determination method according to claim 3, characterized in that, Determining the touch force based on the signals from the multiple sensors, the touch position, and the standard force template includes: A force vector y is determined based on the touch location and the standard force template. The force vector y includes the values ​​of the multiple sensors corresponding to the touch location in the standard force template, and the touch force is determined as follows: The preset calibration force It is a vector formed by calibrating the signals from the multiple sensors with corresponding calibration coefficients.

5. The touch information determination method according to claim 4, characterized in that, The method further includes: normalizing the values ​​of the calibrated feature curves of the multiple sensors at the same preset calibration position to generate a standard position template. The normalization process includes: normalizing the values ​​of the calibrated feature curves of the multiple sensors corresponding to the same preset calibration position using the maximum value among the values ​​of the calibrated feature curves of the multiple sensors corresponding to the preset calibration position. The touch position is determined based on the signals from the multiple sensors calibrated with corresponding calibration coefficients and the standard position template.

6. The touch information determination method according to claim 5, characterized in that, Determining the touch position based on the signals from the plurality of sensors and the standard position template includes: The vector x formed by calibrating the signals from the multiple sensors with corresponding calibration coefficients is normalized to obtain x'; Calculate the distance between x' and the vector formed by the normalized feature value curves of multiple sensors corresponding to the preset calibration position in the standard position template, and take the preset calibration position corresponding to the vector with the smallest distance as the touch position.

7. A touch information determining device, characterized in that, The device includes a memory and a processor, wherein the memory stores a program that, when read and executed by the processor, implements the touch information determination method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the touch information determination method as described in any one of claims 1 to 6.

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