A high-precision finger touch recognition method for capacitive screens

Abstract

This invention discloses a high-precision finger touch recognition method for capacitive touchscreens, relating to the field of finger touch recognition technology. The method includes the following steps: constructing a finger touch experimental group and recording touch experimental data from the group using a capacitive touchscreen; extracting instantaneous change features, long-term change features, and touch shape features from the touch experimental data; performing independent and joint analysis on the instantaneous and long-term change features to obtain a time change standard; analyzing the touch shape features to obtain a touch shape standard; and performing high-precision recognition of finger touch actions based on the time change standard and the touch shape standard. This invention addresses the problem that existing finger touch recognition technologies still suffer from insufficient feature analysis during finger touch, leading to significant errors in finger touch recognition and suboptimal accuracy.

Description

Technical Field

[0001] This invention relates to the field of finger touch recognition technology, specifically a high-precision finger touch recognition method for capacitive screens. Background Technology

[0002] Finger touch recognition technology refers to a technology that uses sensors to detect and analyze the physical signals generated by the interaction between a human finger and a touch screen, accurately locates the touch point, and distinguishes between valid operations and interference.

[0003] Existing finger touch recognition technologies typically employ image comparison methods, comparing images of the touched area to determine if they match the characteristics of a finger touch, or using preset thresholds to judge whether the signal generated by the touch matches the characteristics of a finger touch. These methods lack comprehensive feature analysis of finger touch, resulting in significant errors in finger touch recognition and suboptimal accuracy. For example, patent application CN110162230A discloses a "method, device, and storage medium for identifying touch positions," which uses preset thresholds to determine the touch area and whether it is a finger touch. However, this approach still lacks sufficient accuracy. Therefore, existing finger touch recognition technologies suffer from insufficient feature analysis of finger touch, leading to significant errors in finger touch recognition and suboptimal accuracy. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It constructs a finger touch experimental group, records touch experimental data using a capacitive screen, and then extracts instantaneous and long-term change features from the touch experimental data. Simultaneously, it extracts touch shape features from the touch experimental data, and then independently analyzes the instantaneous and long-term change features to obtain time-independent change standards. Furthermore, it performs joint analysis of the instantaneous and long-term change features to obtain time-joint change standards. Next, it analyzes the touch shape features to obtain touch shape standards. Finally, based on the time change standards and touch shape standards, it performs high-precision recognition of finger touch actions. This addresses the problem that existing finger touch recognition technologies still suffer from insufficient feature analysis during finger touch, resulting in significant errors in finger touch recognition and suboptimal recognition accuracy.

[0005] To achieve the above objectives, this application provides a high-precision finger touch recognition method for capacitive touchscreens, comprising the following steps:

[0006] Construct a finger touch experimental group and record the touch experimental data in the finger touch experimental group through a capacitive screen;

[0007] Finger touch features are extracted from touch experiment data, including instantaneous change features, long-term change features, and touch shape features;

[0008] Independent and joint analyses of instantaneous and long-term variation characteristics are performed to obtain time variation standards.

[0009] Analyze touch shape features and the touch shape standards of touch shape features;

[0010] High-precision recognition of finger touch actions is achieved based on time-varying and touch shape standards.

[0011] Furthermore, constructing a finger touch experimental group and recording the touch experimental data in the finger touch experimental group using a capacitive screen includes the following sub-steps:

[0012] A finger touch experimental group was constructed, in which experimental subjects of random age and random height and weight conducted touch experiments on the capacitive screen.

[0013] The touch experiment involved the experimenter touching random locations on the capacitive screen with their finger.

[0014] Record touch experiment data, including monitoring time, capacitance distribution, and capacitance signal.

[0015] Further, extracting finger touch features from the touch experiment data, including instantaneous change features, long-term change features, and touch shape features, includes the following sub-steps:

[0016] Extract instantaneous and long-term variation features from touch experiment data;

[0017] Extract touch shape features from touch experiment data.

[0018] Furthermore, extracting instantaneous and long-term variation features from the touch experiment data includes the following sub-steps:

[0019] According to the chronological order of monitoring time, the capacitive sensors that first detected a capacitance signal are acquired and marked as monitoring sensors. These monitoring sensors are then numbered using the symbol P. m This indicates that m is a non-zero natural number and m is the index of P;

[0020] Acquire the capacitance signal from the monitoring sensor and name it the monitoring signal;

[0021] The monitoring times are numbered according to their chronological order, using the symbol T. n It indicates that it will be in T n P mThe corresponding monitoring signal is labeled S(n,m), where n is a non-zero natural number and n is the index of T, and (n,m) is the index of S;

[0022] A Cartesian coordinate system is established with monitoring time as the X-axis and monitoring signal as the Y-axis, and named the integrated time variation coordinate system.

[0023] The coordinate points in the integrated time-varying coordinate system are named integrated time-varying points. Each value of m is analyzed independently. S(n,m) is entered into the integrated time-varying coordinate system according to n, and the integrated time-varying point corresponding to S(n,m) is marked as TP. n ;

[0024] For any value of n, TP is crossed by a straight line. n With TP n+1 Connect them to obtain instantaneous change characteristics, through the symbol LI n express;

[0025] For TP n Perform linear regression analysis and name the resulting straight line as the long-term variation characteristic.

[0026] Furthermore, extracting touch shape features from the touch experiment data includes the following sub-steps:

[0027] The capacitance signal changes in a trend of first increasing and then decreasing. When any capacitance signal decreases, the monitoring time at this time is named the end time, and the monitoring time before the end time is obtained and named the shape analysis time.

[0028] Acquire the capacitance distribution at the moment of shape analysis. The capacitance distribution is the shape of the area enclosed by the capacitance sensor that detects the capacitance signal at the monitoring time, which can be directly obtained from the capacitive screen.

[0029] Draw the inner and outer circles of the capacitance distribution, and name them the inner shape feature and the outer shape feature, respectively. The inner shape feature and the outer shape feature are the touch shape features.

[0030] Furthermore, independent and joint analyses are performed on instantaneous and long-term variation characteristics to obtain the time variation standard, including the following sub-steps:

[0031] Independent analysis of instantaneous and long-term variation characteristics yields time-independent variation criteria.

[0032] By jointly analyzing the instantaneous and long-term variation characteristics, a time-joint variation standard is obtained.

[0033] Furthermore, independent analysis of instantaneous and long-term variation characteristics yields the following sub-steps for obtaining time-independent variation criteria:

[0034] Get LI n The slope, denoted as K n With n as the horizontal axis, K n Establish a Cartesian coordinate system for the vertical axis, named the instantaneous slope change graph, and set K... n Enter the instantaneous slope change graph according to n;

[0035] Linear regression analysis was performed on the instantaneous slope change graph, and the slope of the regression function was marked as the instantaneous slope change characteristic;

[0036] By analyzing different touch experiment data, the range of all instantaneous slope change characteristics in the touch experiment data is extracted to obtain the instantaneous independent change standard;

[0037] By analyzing different touch test data, the range of slopes of all long-term variation features in the touch test data is extracted to obtain the long-term independent variation standard;

[0038] The instantaneous independent change standard and the long-term independent change standard are collectively referred to as the time-independent change standard.

[0039] Furthermore, a joint analysis of instantaneous and long-term variation characteristics yields a time-joint variation standard, including the following sub-steps:

[0040] The slope of the long-term variation feature is denoted as KC, and K is calculated. n / KC, label the calculation result as KH n ;

[0041] Get KH n Find the minimum value in the list, label it G, and get the index n corresponding to G, label it N;

[0042] Calculate N / max(n) and name the result as the point where similar slopes exist;

[0043] By analyzing the points where similar slopes exist in all touch experiment data, statistically analyzing the range of these points, and obtaining the time-joint variation standard, we can obtain the standard for the change of similar slopes.

[0044] Furthermore, the touch shape features are analyzed. The touch shape criteria for analyzing touch shape features include the following sub-steps:

[0045] Obtain the centers of the inscribed and circumscribed shape features, and name them the inscribed center and circumscribed center, respectively.

[0046] Obtain the distance between the inscribed center and the circumscribed center, and name it the touch feature value;

[0047] Analyze the touch feature values ​​of all touch experiment data, statistically analyze the range of touch feature values, and obtain the touch shape standard.

[0048] Furthermore, high-precision recognition of finger touch actions based on time-varying and touch shape standards includes the following sub-steps:

[0049] When any capacitance sensor detects a capacitance signal, finger touch recognition is activated, and the monitoring time, capacitance distribution, and capacitance signal are recorded.

[0050] Based on the monitoring time, capacitance distribution, and capacitance signal analysis, the instantaneous slope change characteristics, long-term slope change characteristics, similar slope existence points, and touch feature values ​​are respectively named the first feature, the second feature, the third feature, and the fourth feature.

[0051] The system determines whether the first feature is within the instantaneous independent change standard, whether the second feature is within the long-term independent change standard, whether the third feature is within the time-joint change standard, and whether the fourth feature is within the touch shape standard. If all of the above conditions are met, the action is determined to be a finger touch action; otherwise, it is determined to be a non-finger touch action.

[0052] The beneficial effects of this invention are as follows: This invention constructs a finger touch experimental group, records the touch experimental data in the finger touch experimental group through a capacitive screen, and then extracts the instantaneous change features and long-term change features in the touch experimental data. At the same time, it extracts the touch shape features in the touch experimental data. The advantage is that it fully considers the change features of the capacitance signal generated when the finger touches, including instantaneous features and long-term features, thereby improving the accuracy and rationality of finger touch recognition.

[0053] This invention obtains time-independent change standards by independently analyzing instantaneous and long-term change features, and obtains time-joint change standards by jointly analyzing instantaneous and long-term change features. Then, it analyzes touch shape features to obtain touch shape standards. Finally, it performs high-precision recognition of finger touch actions based on time change standards and touch shape standards. The advantage is that it fully analyzes the change standards of instantaneous and long-term change features, that is, the judgment standards of finger touch, and combines touch shape analysis to ensure that the capacitive signal generated by touch fully matches the characteristics of finger touch, thereby improving the accuracy and comprehensiveness of finger touch recognition. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0055] Figure 2 This is a schematic diagram of the integrated time-varying coordinate system of the present invention;

[0056] Figure 3 Schematic diagrams of LI1 to LI9 of the present invention;

[0057] Figure 4 This is a schematic diagram illustrating the long-term variation characteristics of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1, please refer to Figure 1 As shown, this application provides a high-precision finger touch recognition method for capacitive screens, including the following steps:

[0060] Step S1 involves constructing a finger touch experimental group and recording the touch experimental data in the finger touch experimental group using a capacitive screen. Step S1 includes the following sub-steps:

[0061] Step S101: Construct a finger touch experimental group, in which experimental subjects of random age and random height and weight conduct touch experiments on the capacitive screen.

[0062] Step S102, the touch experiment specifically involves the experimenter touching a random location on the capacitive screen with their finger;

[0063] Step S103: Record the touch experiment data, which includes monitoring time, capacitance distribution, and capacitance signal.

[0064] In practice, experimental participants of random age and random height and weight were used because different groups of people have different finger sizes, resulting in significant differences in the tactile experimental data. Therefore, using experimental participants of random age and random height and weight was intended to accommodate all groups of people as much as possible. The monitoring time only started recording when a capacitance signal was detected.

[0065] Step S2 involves extracting finger touch features from the touch experiment data. These features include instantaneous change features, long-term change features, and touch shape features. Step S2 includes the following sub-steps:

[0066] Step S201: Extract instantaneous and long-term change features from the touch experiment data;

[0067] Step S201 includes the following sub-steps:

[0068] Step S201.1: According to the chronological order of monitoring time, acquire the capacitance sensor that first detected the capacitance signal and mark it as the monitoring sensor. Number the monitoring sensors and use the symbol P. m This indicates that m is a non-zero natural number and m is the index of P;

[0069] Step S201.2: Obtain the capacitance signal of the monitoring sensor and name it the monitoring signal;

[0070] Step S201.3: Number the monitoring times according to their chronological order, using the symbol T. n It indicates that it will be in T n P m The corresponding monitoring signal is labeled S(n,m), where n is a non-zero natural number and n is the index of T, and (n,m) is the index of S;

[0071] In practice, according to the chronological order of monitoring time, the capacitive sensor that first detects a capacitance signal is acquired and marked as the monitoring sensor. In essence, the capacitive sensor that detects a capacitance signal at time T1 is marked as the monitoring sensor, and numbered from P1 to P4, i.e., 1≤m≤4. When analyzing instantaneous and long-term variation characteristics, the monitoring time only includes the time corresponding to the shape analysis moment; that is, only the touch experiment data before the capacitance signal becomes relatively small is analyzed. The time T is obtained through numbering. n , 1≤n≤500, taking S(3,2) as an example, S(3,2) represents the capacitance signal at P2 during the monitoring time of the 3rd frame.

[0072] Please see Figure 2 As shown, in step S201.4, a Cartesian coordinate system is established with the monitoring time as the X-axis and the monitoring signal as the Y-axis, and named the integrated time variation coordinate system;

[0073] Step S201.5: Name the coordinate points in the integrated time variation coordinate system as integrated time variation points. Analyze each value of m independently. Enter S(n,m) into the integrated time variation coordinate system according to n. Mark the integrated time variation point corresponding to S(n,m) as TP. n ;

[0074] Please see Figure 3 As shown, in step S201.6, for any value of n, TP is connected by a straight line. n With TP n+1 Connect them to obtain instantaneous change characteristics, through the symbol LI n express;

[0075] Please see Figure 4 As shown, in step S201.7, the TP is... nPerform linear regression analysis and name the resulting straight line as the long-term variation characteristic;

[0076] In practice, due to the large amount of data, displaying all of it in this embodiment would result in overly dense coordinate points, making it impossible to demonstrate the specific analysis process in subsequent analyses. Therefore, this embodiment only uses a portion of the data for demonstration and illustration, aiming to show the operation process of each step; the comprehensive time-varying coordinate system is constructed as follows: Figure 2 As shown, only touch experiment data from the same monitoring sensor are analyzed at the same time. In this embodiment, P1 is used as an example. Figure 2 Only data from P1 in a single touch experiment was entered. S(n,1) was entered into the integrated time-varying coordinate system, and TP1 to TP were obtained according to the original data volume. 500 However, this embodiment only shows a portion of the data, therefore TP1 to TP are labeled. 10 Connecting LI1 to LI9 as follows Figure 3 As shown, long-term variation characteristics were obtained through linear regression analysis, as follows: Figure 4 As shown.

[0077] Step S202: Extract touch shape features from the touch experiment data;

[0078] Step S202 includes the following sub-steps:

[0079] Step S202.1: The trend of the capacitance signal is first increasing and then decreasing. When any capacitance signal decreases, the monitoring time at this time is named the end time. The monitoring time before the end time is obtained and named the shape analysis time.

[0080] Step S202.2: Obtain the capacitance distribution at the time of shape analysis. The capacitance distribution is the shape of the area enclosed by the capacitance sensor that detects the capacitance signal at the monitoring time, which can be directly obtained from the capacitive screen.

[0081] Step S202.3: Draw the inner circle and outer circle of the capacitance distribution, and name them the inner shape feature and outer shape feature respectively. The inner shape feature and outer shape feature are the touch shape features.

[0082] In practice, at the moment of shape analysis, the user's finger is about to leave the capacitive screen. At this moment, the shape recorded by the user's finger on the capacitive screen is the most complete. Therefore, the analysis is based on the capacitance distribution at the moment of shape analysis. Due to the structure of human fingers, the capacitance distribution basically presents an irregular polygon similar to a convex shape, rather than a quadrilateral. The missing part is because there is curvature on the finger, which will cause the center of the inscribed circle and the circumscribed circle to be different. This difference is related to the curvature of the finger, so it can be used for the analysis of finger touch recognition.

[0083] Step S3 involves independently and jointly analyzing the instantaneous and long-term variation characteristics to obtain the time variation standard. Step S3 includes the following sub-steps:

[0084] Step S301: Independently analyze the instantaneous change characteristics and long-term change characteristics to obtain the time-independent change standard;

[0085] Step S301 includes the following sub-steps:

[0086] Step S301.1, obtain LI n The slope, denoted as K n With n as the horizontal axis, K n Establish a Cartesian coordinate system for the vertical axis, named the instantaneous slope change graph, and set K... n Enter the instantaneous slope change graph according to n;

[0087] Step S301.2: Perform linear regression analysis on the instantaneous slope change graph and mark the slope of the regression function as the instantaneous slope change characteristic;

[0088] Step S301.3: Analyze different touch test data, extract the range of all instantaneous slope change features in the touch test data, and obtain the instantaneous independent change standard;

[0089] Step S301.4: Analyze different touch test data, extract the slope range of all long-term variation features in the touch test data, and obtain the long-term independent variation standard;

[0090] Step S301.5, the instantaneous independent variation standard and the long-term independent variation standard are collectively referred to as the time-independent variation standard;

[0091] In practice, K1 to K9 are obtained, an instantaneous slope change graph is constructed, and its instantaneous slope change feature is extracted. The instantaneous slope change feature is -1.15. The instantaneous slope change graph reveals the change amplitude of the capacitance signal in each frame. Since the magnitude of the capacitance signal varies in different usage scenarios, it is impossible to determine whether it is finger touch based on the magnitude of the capacitance signal. However, the change of the capacitance signal during finger touch has a certain pattern. Therefore, it can be analyzed by the change amplitude of the capacitance signal in each frame. The long-term change feature reveals the overall change pattern of the capacitance signal during finger touch. By statistically analyzing their ranges, the instantaneous independent change standard and the long-term independent change standard can be obtained, which are [-1.2849, -0.8672] and [5.0468, 5.8284], respectively. The instantaneous independent change standard represents the fluctuation range of the change amplitude of the capacitance signal in each frame, while the long-term independent change standard represents the fluctuation range of the change amplitude of the capacitance signal during the finger touch behavior.

[0092] Step S302: Perform joint analysis on instantaneous and long-term change characteristics to obtain the joint time change standard;

[0093] Step S302 includes the following sub-steps:

[0094] Step S302.1: Label the slope of the long-term variation characteristic as KC, and calculate K. n / KC, label the calculation result as KH n ;

[0095] Step S302.2, obtain KH n Find the minimum value in the list, label it G, and get the index n corresponding to G, label it N;

[0096] Step S302.3, calculate N / max(n), and name the calculation result as the point where similar slopes exist;

[0097] Step S302.4: Analyze the points where similar slopes exist in all touch experiment data, statistically analyze the range of points where similar slopes exist, and obtain the time joint change standard;

[0098] In practice, since the capacitance signal exhibits a trend of first rising and then falling, and during the rising process, its amplitude decreases from large to small (i.e., the earlier the monitoring time, the larger the amplitude, and vice versa), there must be a slope in the instantaneous change feature that is very close to the long-term change feature, and it usually appears in the middle of the instantaneous change feature. Therefore, by analyzing the location of the point where the similar slope exists, we can determine whether the relationship between the instantaneous change feature and the long-term change feature conforms to the characteristics of finger touch. Taking this embodiment as an example, the minimum value among KH1 to KH9 is calculated and obtained as KH5, i.e., N=5, max(n) This is the maximum value of n, which is 10. Therefore, the calculated similar slope point is 0.5, which usually appears in the middle of the instantaneous change characteristics. Since this embodiment only uses some data for specific explanation, the data of the similar slope point seems to be relatively fixed. However, in the actual analysis process, max(n) is 500, and the obtained N is 268. Therefore, the actual calculated similar slope point is 0.536, which has a fluctuating range. Therefore, the range of the similar slope points of all touch experiment data is statistically analyzed, and the time joint change standard is obtained as [0.4726, 0.5422].

[0099] Step S4 involves analyzing the touch shape features and determining the touch shape criteria. Step S4 includes the following sub-steps:

[0100] Step S401: Obtain the center of the inscribed shape feature and the center of the circumscribed shape feature, and name them the inscribed center and the circumscribed center, respectively.

[0101] Step S402: Obtain the distance between the inscribed center and the circumscribed center, and name it the touch feature value;

[0102] Step S403: Analyze the touch feature values ​​of all touch experiment data, statistically analyze the range of touch feature values, and obtain the touch shape standard;

[0103] In practice, the distance between the inscribed center and the circumscribed center is obtained, and the touch feature value is 53mm. The standard touch shape is calculated to be [48mm, 54mm].

[0104] Step S5 involves high-precision recognition of finger touch actions based on time change and touch shape standards; Step S5 includes the following sub-steps:

[0105] Step S501: When any capacitance sensor detects a capacitance signal, enable finger touch recognition and record the monitoring time, capacitance distribution and capacitance signal.

[0106] Step S502: Based on the monitoring time, capacitance distribution and capacitance signal analysis, the instantaneous slope change characteristics, the slope of the long-term change characteristics, the points where similar slopes exist and the touch feature value are named as the first feature, the second feature, the third feature and the fourth feature, respectively.

[0107] Step S503: Determine whether the first feature is within the instantaneous independent change standard, whether the second feature is within the long-term independent change standard, whether the third feature is within the time joint change standard, and whether the fourth feature is within the touch shape standard. If all the above conditions are met, it is determined to be a finger touch action; otherwise, it is determined to be a non-finger touch action.

[0108] In practice, finger touch is only recognized when the monitoring time, capacitance distribution, and capacitance signal simultaneously meet all the judgment conditions. During the testing process, it was found that when humans use capacitive screens for touch, they can complete the touch operation within 0.5 seconds. That is, the entire journey from the finger not touching to touching and then leaving takes only 0.5 seconds. Therefore, in actual analysis, the capacitance distribution and capacitance signal within the first 0.5 seconds are continuously analyzed. If all the judgment conditions are met simultaneously, finger touch can be successfully recognized.

[0109] Example 2: This application provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps as described in a high-precision capacitive touch recognition method for a capacitive screen, to achieve the following functions: constructing a finger touch experimental group and recording touch experimental data from the group using a capacitive screen; extracting instantaneous change features, long-term change features, and touch shape features from the touch experimental data; performing independent and joint analysis on the instantaneous and long-term change features to obtain a time change standard; analyzing the touch shape features to obtain a touch shape standard; and performing high-precision recognition of finger touch actions based on the time change standard and the touch shape standard.

[0110] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a high-precision finger touch recognition method for capacitive screens provided by the methods described above. The method includes: constructing a finger touch experimental group and recording touch experimental data in the finger touch experimental group through a capacitive screen; extracting instantaneous change features, long-term change features, and touch shape features from the touch experimental data; performing independent and joint analysis on the instantaneous change features and long-term change features to obtain a time change standard; analyzing the touch shape features to obtain a touch shape standard; and performing high-precision recognition of finger touch actions based on the time change standard and the touch shape standard.

[0112] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-described high-precision finger touch recognition method for capacitive screens to achieve the following functions: constructing a finger touch experimental group and recording touch experimental data in the finger touch experimental group through a capacitive screen; extracting instantaneous change features, long-term change features, and touch shape features from the touch experimental data; performing independent and joint analysis on the instantaneous change features and long-term change features to obtain a time change standard; analyzing the touch shape features to obtain a touch shape standard; and performing high-precision recognition of finger touch actions based on the time change standard and the touch shape standard.

[0113] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0114] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A high-precision finger touch recognition method for capacitive touchscreens, characterized in that, Includes the following steps: Construct a finger touch experimental group and record the touch experimental data in the finger touch experimental group through a capacitive screen; Finger touch features are extracted from touch experiment data, including instantaneous change features, long-term change features, and touch shape features; Independent and joint analyses of instantaneous and long-term variation characteristics are performed to obtain time variation standards. Analyze touch shape features and the touch shape standards of touch shape features; High-precision recognition of finger touch actions based on time change standards and touch shape standards; Extracting instantaneous and long-term variation features from touch experiment data includes the following sub-steps: According to the chronological order of monitoring time, the capacitive sensors that first detected a capacitance signal are acquired and marked as monitoring sensors. These monitoring sensors are then numbered using the symbol P. m This indicates that m is a non-zero natural number and m is the index of P; Acquire the capacitance signal from the monitoring sensor and name it the monitoring signal; The monitoring times are numbered according to their chronological order, using the symbol T. n It indicates that it will be in T n P m The corresponding monitoring signal is labeled S(n,m), where n is a non-zero natural number and n is the index of T, and (n,m) is the index of S; A Cartesian coordinate system is established with monitoring time as the X-axis and monitoring signal as the Y-axis, and named the integrated time variation coordinate system. The coordinate points in the integrated time-varying coordinate system are named integrated time-varying points. Each value of m is analyzed independently. S(n,m) is entered into the integrated time-varying coordinate system according to n, and the integrated time-varying point corresponding to S(n,m) is marked as TP. n ; For any value of n, TP is crossed by a straight line. n With TP n+1 Connect them to obtain instantaneous change characteristics, through the symbol LI n express; For TP n Perform linear regression analysis and name the resulting straight line as the long-term variation characteristic; Independent and joint analyses of instantaneous and long-term variation characteristics are performed to obtain time variation standards, including the following sub-steps: Independent analysis of instantaneous and long-term variation characteristics yields time-independent variation criteria. By jointly analyzing the instantaneous and long-term variation characteristics, a time-joint variation standard is obtained.

2. The high-precision finger touch recognition method for capacitive touchscreens according to claim 1, characterized in that, Constructing a finger touch experimental group and recording touch experimental data from the finger touch experimental group using a capacitive screen includes the following sub-steps: A finger touch experimental group was constructed, in which experimental subjects of random age and random height and weight conducted touch experiments on the capacitive screen. The touch experiment involved the experimenter touching random locations on the capacitive screen with their finger. Record touch experiment data, including monitoring time, capacitance distribution, and capacitance signal.

3. The high-precision finger touch recognition method for capacitive touchscreens according to claim 2, characterized in that, Extracting finger touch features from touch experiment data, including instantaneous change features, long-term change features, and touch shape features, includes the following sub-steps: Extract instantaneous and long-term variation features from touch experiment data; Extract touch shape features from touch experiment data.

4. The high-precision finger touch recognition method for capacitive touchscreens according to claim 3, characterized in that, Extracting touch shape features from touch experiment data includes the following sub-steps: The capacitance signal changes in a trend of first increasing and then decreasing. When any capacitance signal decreases, the monitoring time at this time is named the end time, and the monitoring time before the end time is obtained and named the shape analysis time. Acquire the capacitance distribution at the moment of shape analysis. The capacitance distribution is the shape of the area enclosed by the capacitance sensor that detects the capacitance signal at the monitoring time, which can be directly obtained from the capacitive screen. Draw the inner and outer circles of the capacitance distribution, and name them the inner shape feature and the outer shape feature, respectively. The inner shape feature and the outer shape feature are the touch shape features.

5. The high-precision finger touch recognition method for capacitive touchscreens according to claim 4, characterized in that, Independent analysis of instantaneous and long-term variation characteristics to obtain time-independent variation criteria includes the following sub-steps: Get LI n The slope, denoted as K n With n as the horizontal axis, K n Establish a Cartesian coordinate system for the vertical axis, named the instantaneous slope change graph, and set K... n Enter the instantaneous slope change graph according to n; Linear regression analysis was performed on the instantaneous slope change graph, and the slope of the regression function was marked as the instantaneous slope change characteristic; By analyzing different touch experiment data, the range of all instantaneous slope change characteristics in the touch experiment data is extracted to obtain the instantaneous independent change standard; By analyzing different touch test data, the range of slopes of all long-term variation features in the touch test data is extracted to obtain the long-term independent variation standard; The instantaneous independent change standard and the long-term independent change standard are collectively referred to as the time-independent change standard.

6. The high-precision finger touch recognition method for capacitive touchscreens according to claim 5, characterized in that, The joint analysis of instantaneous and long-term variation characteristics yields a time-joint variation standard, including the following sub-steps: The slope of the long-term variation feature is denoted as KC, and K is calculated. n / KC, label the calculation result as KH n ; Get KH n Find the minimum value in the list, label it G, and get the index n corresponding to G, label it N; Calculate N / max(n) and name the result as the point where similar slopes exist; By analyzing the points where similar slopes exist in all touch experiment data, statistically analyzing the range of these points, and obtaining the time-joint variation standard, we can obtain the standard for the change of similar slopes.

7. The high-precision finger touch recognition method for capacitive touchscreens according to claim 6, characterized in that, The analysis of touch shape features, including touch shape criteria, comprises the following sub-steps: Obtain the centers of the inscribed and circumscribed shape features, and name them the inscribed center and circumscribed center, respectively. Obtain the distance between the inscribed center and the circumscribed center, and name it the touch feature value; Analyze the touch feature values ​​of all touch experiment data, statistically analyze the range of touch feature values, and obtain the touch shape standard.

8. The high-precision finger touch recognition method for capacitive touchscreens according to claim 7, characterized in that, High-precision recognition of finger touch actions based on time-varying and touch shape standards includes the following sub-steps: When any capacitance sensor detects a capacitance signal, finger touch recognition is activated, and the monitoring time, capacitance distribution, and capacitance signal are recorded. Based on the monitoring time, capacitance distribution, and capacitance signal analysis, the instantaneous slope change characteristics, long-term slope change characteristics, similar slope existence points, and touch feature values ​​are respectively named the first feature, the second feature, the third feature, and the fourth feature. The system determines whether the first feature is within the instantaneous independent change standard, whether the second feature is within the long-term independent change standard, whether the third feature is within the time-joint change standard, and whether the fourth feature is within the touch shape standard. If all of the above conditions are met, the action is determined to be a finger touch action; otherwise, it is determined to be a non-finger touch action.

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