A method and system for optimizing the sensitivity settings of a game joystick

Abstract

This invention discloses a method for optimizing the sensitivity settings of a game joystick, including obtaining the current displacement coordinates and current hardware sensitivity value of the game joystick; determining whether the current hardware sensitivity value is in a normal state; if it is not in a normal state, performing hardware sensitivity optimization settings; and setting a preset time period T. When the time meets the preset time period T, performing periodic software sensitivity optimization settings. This invention uses artificial intelligence algorithms to determine the hardware sensitivity calibration coefficient corresponding to the gamer's joystick by analyzing the joystick's movement trajectory curve, physiological characteristic data, and activity characteristic data. The same joystick can be adapted to the sensitivity calibration coefficients of different gamers. The multimodal sensitivity recognition method, combined with real-time hardware sensitivity calibration and periodic software sensitivity calibration, improves the gamer's satisfaction with using the joystick.

Description

Technical Field

[0001] This invention relates to the field of game joystick sensitivity optimization technology, and more specifically, to a method and system for optimizing the sensitivity settings of a game joystick. Background Technology

[0002] The joystick is an important component of a game controller. It is typically used to adjust the field of view and control the movement of virtual characters. Sensitivity is one of the criteria for judging the quality of a game joystick.

[0003] Chinese Patent Application No. CN202210690085 discloses a method for setting the sensitivity of a game joystick, a game controller, and a storage medium, including the following steps: obtaining the total travel of the game joystick and determining the trajectory range of the game joystick; based on the trajectory range, dividing the trajectory range into a first region, a second region, and a third region sequentially along the diameter direction from the center position of the game joystick to the maximum trajectory boundary of the game joystick; sensing and obtaining the region where the game joystick is located; sensing the operation on the three scroll wheels in real time; obtaining a sensitivity adjustment value based on the one-to-one correspondence between the three scroll wheels and the three regions and the scroll operation performed on the scroll wheel corresponding to the region where the game joystick is located; setting the three sensitivity adjustment values ​​obtained according to the scroll operation performed on the corresponding scroll wheel when the game joystick is in the three regions as the sensitivity of the first region, the second region, and the third region, respectively. This application can improve the sensitivity of the game joystick.

[0004] The above solution also has several limitations, specifically: it cannot adaptively adjust the hardware sensitivity of the joystick according to individual user preferences; it does not consider the need for hardware sensitivity optimization settings for the same joystick under different abnormal conditions; it does not calibrate the sensitivity of the software program on which the joystick depends; and it does not consider a method that combines prioritizing real-time calibration of hardware sensitivity with periodic calibration of software sensitivity during the joystick sensitivity calibration process. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the sensitivity settings of a game joystick, in order to solve the aforementioned problems existing in the prior art.

[0006] The application is as follows:

[0007] A method for optimizing the sensitivity settings of a game joystick includes the following steps:

[0008] Get the current displacement coordinates and current hardware sensitivity value of the game joystick;

[0009] Determine if the current hardware sensitivity value is normal; if it is abnormal, perform sensitivity hardware optimization settings.

[0010] A preset time period T is set. When the time meets the preset time period T, the sensitivity software period optimization setting is executed.

[0011] The specific steps for the sensitivity hardware optimization settings are as follows:

[0012] The relative displacement coordinates of the joystick after calibration are obtained based on the joystick sensitivity optimization formula, the preset optimization strategy, and the displacement coordinates of the joystick.

[0013] The parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function;

[0014] The preset optimization strategy is as follows: based on the first player's first joystick movement trajectory curve, first physiological characteristic data, and first activity characteristic data, determine the preset sensitivity calibration coefficient of the first player.

[0015] Furthermore, the current hardware sensitivity value is calculated based on the sensing voltage corresponding to the joystick position. A preset sensing voltage deviation threshold is set. If the corresponding sensing voltage deviation value is greater than the preset sensing voltage deviation threshold within a preset time period, the current hardware sensitivity value is set to an abnormal state; otherwise, the current hardware sensitivity value is set to a normal state.

[0016] The formula for optimizing joystick sensitivity is expressed as follows:

[0017] game_x=game_scale*Fun(game_x_pos),

[0018] game_x_pos=SensitivitGetPos(x_pos),

[0019] game_y=game_scale*Fun(game_y_pos),

[0020] game_y_pos=SensitivitGetPos(y_pos),

[0021] Where game_scale represents the preset sensitivity calibration coefficient, Fun() represents the preset displacement calibration function, game_x represents the relative displacement coordinate of the joystick in the x-axis direction, game_y represents the relative displacement coordinate of the joystick in the y-axis direction, game_x_pos represents the sensitivity calibration of the joystick in the x-axis direction, game_y_pos represents the sensitivity calibration of the joystick in the y-axis direction, x_pos represents the current displacement coordinate of the joystick in the x-axis direction, and y_pos represents the current displacement coordinate of the joystick in the y-axis direction. The preset displacement calibration function is a nonlinear quadratic function.

[0022] Furthermore, it also includes the following steps:

[0023] The absolute coordinates of the joystick on the screen are obtained from the relative displacement coordinates, and are expressed as follows:

[0024] curr_game_x += game_x,

[0025] curr_game_y+=game_y, where curr_game_x represents the absolute coordinate of the joystick in the x-axis direction, and curr_game_y represents the absolute coordinate of the joystick in the y-axis direction.

[0026] Furthermore, before determining the preset sensitivity calibration coefficient for the first player, the process also includes:

[0027] Acquire the first physiological characteristic data of the first player controlling the first joystick, wherein the first physiological characteristic data includes at least historical first physiological characteristic data;

[0028] Acquire the first player's activity characteristic data when controlling the first joystick;

[0029] Obtain the trajectory curve of the first joystick when the first player controls the first joystick;

[0030] The step of obtaining the first physiological characteristic data of the first player who controls the first joystick includes:

[0031] A first sensor group for collecting visual data, pressure data, and contact area data is disposed on the surface of the first rocker arm;

[0032] The first sensor group is used to capture multi-view images of the first player to obtain three-dimensional data of the hand, including the palm, fingers and skin;

[0033] The pressure distribution data of the first player's fingers and palms when using the joystick are detected by the first sensor group;

[0034] The first sensor group is used to detect the change in the contact area of ​​the palm and fingers of the first operator when using the joystick;

[0035] The first sensor group is used to detect the change in the deflection angle of the palm and fingers of the first operator when using the joystick;

[0036] Using computer vision technology, semantic annotation is performed on the three-dimensional data of the hand, the pressure distribution data, the contact area change, and the deviation angle change to extract physiological feature data representing physiological characteristics;

[0037] Based on the obtained physiological feature data, a hand physiological feature parameter model containing the first physiological feature data is constructed using a neural network algorithm;

[0038] The steps for obtaining the first player's first activity feature data when controlling the first joystick include:

[0039] Using an inertial sensor, data on the motion changes of the hand joystick of the first player in three-dimensional space, including movement, acceleration, and posture, are obtained.

[0040] An electromagnetic position tracker is positioned directly above the first player to calculate the three-dimensional coordinates of the hand based on the distribution of magnetic fields in space.

[0041] The first player wears a multi-channel smart monitoring bracelet to monitor hand physiological data in real time during the operation process. The hand physiological data includes at least changes in the electrical conductivity and temperature of the palm skin.

[0042] Natural language processing techniques are used to semantically annotate the motion change data, the three-dimensional coordinates of the palm, and the physiological data of the hand, and to extract activity state feature data representing the activity state.

[0043] Furthermore, obtaining the trajectory curve of the first joystick when the first player manipulates the first joystick includes:

[0044] A laser tracker is configured on the first joystick to obtain the joystick coordinates in the two-dimensional plane of the active surface of the first joystick in real time.

[0045] The high-speed motion tracking sensor built into the first joystick detects the joystick's motion data in a preset three-dimensional space.

[0046] By combining the joystick coordinates with the joystick motion data, and using a data fusion algorithm, the position coordinate sequence of the first joystick in the three-dimensional space is calculated;

[0047] The position coordinate sequence is imported into the 3D modeling software, and the 3D curve fitting tool is used to generate the first joystick swing trajectory curve from all the discrete coordinate points.

[0048] Further, the step of determining the preset sensitivity calibration coefficient of the first player based on the first joystick movement trajectory curve, the first physiological characteristic data, and the first activity characteristic data includes:

[0049] Hand physiological features and activity features are extracted from the first physiological feature data and the first activity feature data, respectively;

[0050] Based on the physiological characteristics of the hands and the activity characteristics, a clustering algorithm is used to identify the joystick control mode used by the first player.

[0051] Based on the first joystick movement trajectory curve, analyze the user's speed preference and joystick amplitude preference, and fit the joystick control parameters of the first player.

[0052] Based on the joystick control mode and the joystick control parameters, a preset sensitivity calibration coefficient matching the joystick is selected from the knowledge base using a BP neural network algorithm.

[0053] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0054] A first deep learning model is preset, which is used to identify when the first player switches to the second player. If a player switching operation occurs, the current hardware sensitivity value is set to an abnormal state.

[0055] The input to the first deep learning model is the player's first physiological characteristic data and first activity characteristic data.

[0056] Furthermore, the specific implementation process of the preset time period T, where the time meets the preset time period T, and the sensitivity software period optimization setting is as follows:

[0057] Extract the joystick temperature and wiggle resistance corresponding to a preset time period T. Identify the inertial joystick temperature and inertial wiggle resistance of the first player within the preset time period T. Then, extract the inertial joystick temperature T of each software program within the preset time period T. c and inertial rocker resistance R c ;

[0058] Extract the joystick detection temperature T during a preset time period T. c1 and shaking detection resistance R c1 The compensation function for the sensitivity adjustment of each software program as it increases over time is analyzed as follows:

[0059]

[0060] Then, when each software program meets the preset time period T, the sensitivity is adjusted as follows:

[0061]

[0062] Where W represents the compensation weight for the temperature of the inertial joystick, and Q represents the compensation weight for the resistance of the inertial joystick.

[0063] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0064] Obtain the preset sensitivity detection signal transmission frequency;

[0065] The control host continuously sends sensitivity detection signals to the first joystick at the sensitivity detection signal transmission frequency. Each data packet of the sensitivity detection signal carries the first detection time when the host sends the sensitivity detection signal and the data packet number.

[0066] The control host receives the sensitivity detection response signal returned by the joystick. The data packet of the sensitivity detection response signal carries the second detection time when the first joystick sent the sensitivity detection response signal and the data packet number of the sensitivity detection signal it responded to.

[0067] If no sensitivity detection response signal in response to the sensitivity detection signal is received from the first joystick within a preset time after the first detection time, the sensitivity detection signal is marked as not responding.

[0068] Record the number of sensitivity detection signals that do not respond;

[0069] Determine whether the number of non-responding sensitivity detection signals per unit time is greater than a preset response count threshold;

[0070] If the number of unresponsive sensitivity detection signals per unit time exceeds the preset response count threshold, the current hardware sensitivity value of the first joystick is set to an abnormal state.

[0071] A sensitivity setting optimization system for a game joystick, comprising:

[0072] The sensitivity hardware calibration module is used to obtain the current displacement coordinates and current hardware sensitivity value of the joystick; determine whether the current hardware sensitivity value is in a normal state; if it is not in a normal state, perform sensitivity hardware optimization settings.

[0073] The sensitivity software calibration module is used to preset the time period T. When the time meets the preset time period T, the sensitivity software period optimization setting is executed.

[0074] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0075] This invention provides a method for obtaining the current displacement coordinates and current hardware sensitivity value of a game joystick; determining whether the current hardware sensitivity value is in a normal state; if it is not in a normal state, performing sensitivity hardware optimization settings; the specific steps of the sensitivity hardware optimization settings are as follows: obtaining the calibrated relative displacement coordinates of the joystick according to the joystick sensitivity optimization formula, a preset optimization strategy, and the joystick's displacement coordinates; the parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function; the preset optimization strategy is: determining the preset sensitivity calibration coefficient of the first player based on the first player's first joystick movement trajectory curve, first physiological characteristic data, and first activity characteristic data; this invention uses artificial intelligence algorithms to determine the hardware sensitivity calibration coefficient corresponding to the player's joystick by analyzing the player's joystick movement trajectory curve, physiological characteristic data, and activity characteristic data. The same joystick can adapt to the sensitivity calibration coefficients of different players. The multi-modal sensitivity recognition method, combining real-time hardware sensitivity calibration and periodic software sensitivity calibration, improves player satisfaction with the joystick. Attached Figure Description

[0076] Figure 1 This is a flowchart illustrating a method for optimizing the sensitivity settings of a game joystick according to an embodiment of the present invention.

[0077] Figure 2 This is a schematic diagram of the sensitivity hardware optimization setting process of a game joystick sensitivity setting optimization method provided in an embodiment of the present invention;

[0078] Figure 3 This is a system diagram of a game joystick sensitivity setting optimization system provided in an embodiment of the present invention. Detailed Implementation

[0079] The present invention will now be described in detail with reference to the accompanying drawings.

[0080] Example 1

[0081] This invention provides a method for optimizing the sensitivity settings of a game joystick, such as... Figure 1 This includes the following steps:

[0082] S1: Get the current displacement coordinates and current hardware sensitivity value of the game joystick;

[0083] S2: Determine if the current hardware sensitivity value is normal. If it is not normal, perform sensitivity hardware optimization settings.

[0084] S3: Preset time period T. When the time meets the preset time period T, the sensitivity software period optimization setting is executed.

[0085] The specific steps for the sensitivity hardware optimization settings are as follows: Figure 2 :

[0086] S21: Obtain the relative displacement coordinates of the joystick after calibration based on the joystick sensitivity optimization formula, the preset optimization strategy, and the current displacement coordinates;

[0087] S22: The parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function;

[0088] S23: The preset optimization strategy is to determine the preset sensitivity calibration coefficient of the first player based on the first joystick movement trajectory curve, the first physiological characteristic data, and the first activity characteristic data.

[0089] Furthermore, the current hardware sensitivity value is calculated based on the sensing voltage corresponding to the joystick position. A preset sensing voltage deviation threshold is set. If the corresponding sensing voltage deviation value is greater than the preset sensing voltage deviation threshold within a preset time period, the current hardware sensitivity value is set to an abnormal state; otherwise, the current hardware sensitivity value is set to a normal state.

[0090] Specifically, the current induced voltage of the joystick is obtained by a voltage sensor, and the deviation value of the induced voltage is calculated and compared with a preset induced voltage deviation threshold. This method is a type of multimodal hardware sensitivity identification method. This method is for the sensitivity identification of the joystick hardware. If the joystick hardware itself is abnormally sensitive, it can be identified by calculating the induced voltage of the joystick.

[0091] The formula for optimizing joystick sensitivity is expressed as follows:

[0092] game_x=game_scale*Fun(game_x_pos),

[0093] game_x_pos=SensitivitGetPos(x_pos),

[0094] game_y=game_scale*Fun(game_y_pos),

[0095] game_y_pos = SensitivitGetPos(y_pos), where game_scale represents the preset sensitivity calibration coefficient, Fun() represents the preset displacement calibration function, game_x represents the relative displacement coordinate of the joystick in the x-axis direction, game_y represents the relative displacement coordinate of the joystick in the y-axis direction, game_x_pos represents the sensitivity calibration of the joystick in the x-axis direction, game_y_pos represents the sensitivity calibration of the joystick in the y-axis direction, x_pos represents the current displacement coordinate of the joystick in the x-axis direction, and y_pos represents the current displacement coordinate of the joystick in the y-axis direction. The preset displacement calibration function is a nonlinear quadratic function.

[0096] Specifically, by using the displacement coordinate function and the joystick's displacement coordinates on different coordinate axes, the displacement ratio of the joystick on different coordinate axes can be obtained. For example, by using SensitivitGetPos(x_pos), game_x_pos is obtained, which represents the current displacement ratio of the joystick in the x-axis direction. In this embodiment, its value range is defined as (-1, 1). When its value is 0, it means that the joystick has not moved. When its value is -1, it means that the joystick has moved to the leftmost position. When its value is 1, it means that the joystick has moved to the rightmost position. When the joystick moves upward, its value is positive, and when the joystick moves downward, its value is negative. Similarly, SensitivitGetPos(game_pos) represents the current displacement ratio of the joystick on the y-axis, and the value range of game_scale is [0, 1].

[0097] Furthermore, it also includes the following steps:

[0098] The absolute coordinates of the joystick on the screen are obtained from the relative displacement coordinates, and are expressed as follows:

[0099] curr_game_x += game_x,

[0100] curr_game_y+=game_y, where curr_game_x represents the absolute coordinate of the joystick in the x-axis direction, and curr_game_y represents the absolute coordinate of the joystick in the y-axis direction.

[0101] Furthermore, before determining the preset sensitivity calibration coefficients for the first player, the following steps are also included:

[0102] Acquire the first physiological characteristic data of the first player controlling the first joystick, wherein the first physiological characteristic data includes at least historical first physiological characteristic data;

[0103] Acquire first activity feature data of the first player while controlling the first joystick;

[0104] Obtain the trajectory curve of the first joystick during the process of the first player controlling the first joystick;

[0105] The step of obtaining the first physiological characteristic data of the first player who controls the first joystick includes:

[0106] A first sensor group for collecting visual data, pressure data, and contact area data is disposed on the surface of the first rocker arm;

[0107] The first sensor group is used to capture multi-view images of the first player to obtain three-dimensional data of the hand, including the palm, fingers and skin;

[0108] Specifically, the first sensor group includes, but is not limited to, infrared reflective sensors and high frame rate cameras.

[0109] The pressure distribution data of the first player's fingers and palms when using the joystick are detected by the first sensor group;

[0110] The first sensor group is used to detect the change in the contact area of ​​the palm and fingers of the first operator when using the joystick;

[0111] The first sensor group is used to detect the change in the deflection angle of the palm and fingers of the first operator when using the joystick;

[0112] Using computer vision technology, semantic annotation is performed on the three-dimensional data of the hand, the pressure distribution data, the contact area change, and the deviation angle change to extract physiological feature data representing physiological characteristics;

[0113] Based on the obtained physiological feature data, a hand physiological feature parameter model containing the first physiological feature data is constructed using a neural network algorithm;

[0114] The steps for obtaining the first player's first activity feature data when controlling the first joystick include:

[0115] Using an inertial sensor, data on the motion changes of the hand joystick of the first player in three-dimensional space, including movement, acceleration, and posture, are obtained.

[0116] An electromagnetic position tracker is positioned directly above the first player to calculate the three-dimensional coordinates of the hand based on the distribution of magnetic fields in space.

[0117] The first player wears a multi-channel smart monitoring bracelet to monitor hand physiological data in real time during the operation process. The hand physiological data includes at least changes in the electrical conductivity and temperature of the palm skin.

[0118] Natural language processing techniques are used to semantically annotate the motion change data, the three-dimensional coordinates of the palm, and the physiological data of the hand, and to extract activity state feature data representing the activity state.

[0119] Specifically, in this step, image processing algorithms such as segmentation and edge detection are used to label areas such as the palm and fingers in the hand image; in the three-dimensional coordinate model, machine learning algorithms are used to identify different fingers and knuckles and label the image areas; a pressure heatmap obtained from a pressure test is used; the three-dimensional structural parameters, pressure value distribution, and corresponding contact area changes in each semantically labeled area are statistically analyzed as the features of that area; the above multi-source features are input into the machine learning algorithm to train a parametric model for predicting the physiological structure of the hand; through this combination of image analysis and artificial intelligence technology, the physiological characteristics of the hand can be obtained automatically and efficiently.

[0120] Furthermore, obtaining the trajectory curve of the first joystick when the first player manipulates the first joystick includes:

[0121] A laser tracker is configured on the first joystick to obtain the joystick coordinates in the two-dimensional plane of the active surface of the first joystick in real time.

[0122] The high-speed motion tracking sensor built into the first joystick detects the joystick's motion data in a preset three-dimensional space.

[0123] By combining the joystick coordinates with the joystick motion data, and using a data fusion algorithm, the position coordinate sequence of the first joystick in the three-dimensional space is calculated;

[0124] The position coordinate sequence is imported into the 3D modeling software, and the 3D curve fitting tool is used to generate the first joystick swing trajectory curve from all the discrete coordinate points.

[0125] Further, the step of determining the preset sensitivity calibration coefficient of the first player based on the first joystick movement trajectory curve, the first physiological characteristic data, and the first activity characteristic data includes:

[0126] Hand physiological features and activity features are extracted from the first physiological feature data and the first activity feature data, respectively;

[0127] Specifically, in this step, the extracted physiological feature data is first analyzed and transformed to construct more discriminative feature vectors; the feature-engineered dataset is used as the sample space, and the physiological parameter values ​​corresponding to each sample are labeled; the fitting and generalization performance of different statistical learning algorithms on this sample set is compared, and appropriate algorithms such as decision tree models, SVM, and deep learning are selected; the selected machine learning algorithm and training dataset are used to train the parameters and learn the mapping relationship between physiological features and parameter values; the model performance is evaluated on an independent test set, and the prediction performance is further improved by tuning parameters and adjusting the framework; the model is further trained using newly collected hand physiological feature data to adapt it to more people, or adversarial networks can be used to generate diverse feature data to improve the model's generalization and recognition capabilities and enhance robustness.

[0128] Based on the physiological characteristics of the hands and the activity characteristics, a clustering algorithm is used to identify the joystick control mode used by the first player.

[0129] Based on the first joystick movement trajectory curve, analyze the user's speed preference and joystick amplitude preference, and fit the joystick control parameters of the first player.

[0130] Based on the joystick control mode and the joystick control parameters, a preset sensitivity calibration coefficient matching the joystick is selected from the knowledge base using a BP neural network algorithm.

[0131] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0132] A first deep learning model is preset, which is used to identify when the first player switches to the second player. If a player switching operation occurs, the current hardware sensitivity value is set to an abnormal state.

[0133] The input to the first deep learning model is the player's first physiological characteristic data and first activity characteristic data.

[0134] Specifically, the preset first deep learning model in this embodiment uses different statistical machine learning algorithms to assess the fitting and generalization performance of the sample set. Suitable algorithms such as decision trees, random forests, and deep learning can be selected. The selected machine learning model and training dataset are used for parameter training to learn the mapping relationship between physiological features and activity features to distinguish the characteristic behaviors of different players. Diverse data can be generated through adversarial networks for the test set, thereby enhancing the model's generalization ability and robustness. Prediction performance can be further improved through parameter tuning and framework adjustment.

[0135] It should be noted that recognizing the switching actions of different gamers using the game joystick through a deep learning model can improve gamers' satisfaction with the joystick. Different gamers use different sensitivity parameters. The input of the deep learning model is the gamer's first physiological feature data and first activity feature data, which can speed up feature extraction and recognition. There is no need to use other features for calculation. The existing features that have been extracted are used, thereby speeding up the recognition process.

[0136] Furthermore, the specific implementation process of the preset time period T, where the time meets the preset time period T, and the sensitivity software period optimization setting is as follows:

[0137] Extract the joystick temperature and wiggle resistance corresponding to a preset time period T. Identify the inertial joystick temperature and inertial wiggle resistance of the first player within the preset time period T. Then, extract the inertial joystick temperature T of each software program within the preset time period T. c and inertial rocker resistance R c ;

[0138] Extract the joystick detection temperature T during a preset time period T. c1 and shaking detection resistance R c1 The compensation function for the sensitivity adjustment of each software program as it increases over time is analyzed as follows:

[0139]

[0140] Then, when each software program meets the preset time period T, the sensitivity is adjusted as follows:

[0141]

[0142] Where W represents the compensation weight for the temperature of the inertial joystick, and Q represents the compensation weight for the resistance of the inertial joystick.

[0143] Specifically, the preset time period T can be set to one day or one week, and the time period can be set according to the needs.

[0144] This embodiment analyzes the compensation function of the sensitivity adjustment of each software program as it increases over time, and adjusts the control sensitivity of each software program within a preset time period T. As the user's continuous use time during the joystick operation increases, the sensitivity may need to be fine-tuned due to various factors (such as joystick temperature fatigue, joystick resistance fatigue, joystick aging, etc.). Therefore, through the compensation function of the software program as it increases over time, the sensitivity of each software program can be automatically adjusted to ensure that the user can maintain the best operation satisfaction during the game.

[0145] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0146] Obtain the preset sensitivity detection signal transmission frequency;

[0147] The control host continuously sends sensitivity detection signals to the first joystick at the sensitivity detection signal transmission frequency. Each data packet of the sensitivity detection signal carries the first detection time when the host sends the sensitivity detection signal and the data packet number.

[0148] The control host receives the sensitivity detection response signal returned by the joystick. The data packet of the sensitivity detection response signal carries the second detection time when the first joystick sent the sensitivity detection response signal and the data packet number of the sensitivity detection signal it responded to.

[0149] If no sensitivity detection response signal in response to the sensitivity detection signal is received from the first joystick within a preset time after the first detection time, the sensitivity detection signal is marked as not responding.

[0150] Record the number of sensitivity detection signals that do not respond;

[0151] Determine whether the number of non-responding sensitivity detection signals per unit time is greater than a preset response count threshold;

[0152] If the number of unresponsive sensitivity detection signals per unit time exceeds the preset response count threshold, the current sensitivity value of the first joystick is set to an abnormal state.

[0153] Specifically, the control host continuously sending network detection signals to the joystick at the sensitivity detection response signal transmission frequency means that the control host continuously sends network detection signals to the joystick at a time interval corresponding to the sensitivity detection response signal transmission frequency. The network detection signal transmission frequency corresponds to a small time interval, for example, it can be taken as the time interval between 5 milliseconds and 50 milliseconds. The control host includes, but is not limited to, a processor. The control host can be connected to the first joystick through a wired network or a wireless network. The sensitivity detection signal transmission frequency can be set to 2 minutes or 5 minutes.

[0154] It should be noted that abnormal game joystick sensitivity is sometimes caused by a loss of network connection. In this case, the only way to determine whether the joystick sensitivity is normal is by obtaining the network sensitivity status. Adding this method of obtaining sensitivity status can improve the redundancy of the method and further improve the player's satisfaction.

[0155] It should be noted that during the joystick sensitivity calibration process, the real-time hardware sensitivity calibration of the joystick should be prioritized, because the joystick sensitivity is mainly reflected in the hardware sensitivity. However, combining the relevant software programs on which the joystick depends for software sensitivity calibration can further optimize the user experience of the joystick, since the joystick also requires software programs to control its operation.

[0156] Example 2, as Figure 3 ,

[0157] A sensitivity setting optimization system for a game joystick, comprising:

[0158] The sensitivity hardware calibration module is used to obtain the current displacement coordinates and current hardware sensitivity value of the joystick; determine whether the current hardware sensitivity value is in a normal state; if it is not in a normal state, perform sensitivity hardware optimization settings.

[0159] The sensitivity software calibration module is used to preset the time period T. When the time meets the preset time period T, the sensitivity software period optimization setting is executed.

[0160] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0161] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0162] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0163] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0164] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0165] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the apparatus according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

Claims

1. A method for optimizing the sensitivity settings of a game joystick, characterized in that, Includes the following steps: Get the current displacement coordinates and current hardware sensitivity value of the joystick; The acquisition of the current hardware sensitivity value includes: acquiring a preset sensitivity detection signal transmission frequency; The control host continuously sends sensitivity detection signals to the joystick at the sensitivity detection signal transmission frequency. Each data packet of the sensitivity detection signal carries the first detection time when the host sends the sensitivity detection signal and the data packet number. The control host receives the sensitivity detection response signal returned by the joystick. The data packet of the sensitivity detection response signal carries the second detection time when the joystick sent the sensitivity detection response signal and the data packet number of the sensitivity detection signal it responded to. If no sensitivity detection response signal in response to the sensitivity detection signal is received from the joystick within a preset time after the first detection time, the sensitivity detection signal is marked as not responding. Record the number of sensitivity detection signals that do not respond; Determine whether the number of non-responding sensitivity detection signals per unit time is greater than a preset response count threshold; If the number of unresponsive sensitivity detection signals per unit time exceeds the preset response count threshold, the current hardware sensitivity value of the joystick is set to an abnormal state. Determine if the current hardware sensitivity value is normal; if it is abnormal, perform sensitivity hardware optimization settings. A preset time period T is set. When the time meets the preset time period T, the sensitivity software period optimization setting is executed. The specific steps for the sensitivity hardware optimization settings are as follows: The relative displacement coordinates of the joystick after calibration are obtained based on the joystick sensitivity optimization formula, the preset optimization strategy, and the current displacement coordinates. The parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function; The preset optimization strategy is as follows: based on the joystick movement trajectory curve of the first player, the first physiological characteristic data, and the first activity characteristic data, determine the preset sensitivity calibration coefficient of the first player.

2. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that, The current hardware sensitivity value is calculated based on the sensing voltage corresponding to the joystick position. A preset sensing voltage deviation threshold is set. If the corresponding sensing voltage deviation value is greater than the preset sensing voltage deviation threshold within a preset time period, the current hardware sensitivity value is set to an abnormal state; otherwise, the current hardware sensitivity value is set to a normal state. The formula for optimizing joystick sensitivity is expressed as follows: game_x=game_scale*Fun(game_x_pos), game_x_pos=SensitivitGetPos(x_pos), game_y=game_scale*Fun(game_y_pos), game_y_pos=SensitivitGetPos(y_pos), Where game_scale represents the preset sensitivity calibration coefficient, Fun() represents the preset displacement calibration function, game_x represents the relative displacement coordinate of the joystick in the x-axis direction, game_y represents the relative displacement coordinate of the joystick in the y-axis direction, game_x_pos represents the sensitivity calibration of the joystick in the x-axis direction, game_y_pos represents the sensitivity calibration of the joystick in the y-axis direction, x_pos represents the current displacement coordinate of the joystick in the x-axis direction, and y_pos represents the current displacement coordinate of the joystick in the y-axis direction. The preset displacement calibration function is a nonlinear quadratic function.

3. The method for optimizing the sensitivity setting of a game joystick according to claim 2, characterized in that, It also includes the following steps: The absolute coordinates of the joystick on the screen are obtained from the relative displacement coordinates, and are expressed as follows: curr_game_x += game_x, curr_game_y+=game_y, Where curr_game_x represents the absolute coordinate of the joystick in the x-axis direction, and curr_game_y represents the absolute coordinate of the joystick in the y-axis direction.

4. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that, Before determining the preset sensitivity calibration coefficient for the first player, the process also includes: Acquire the first physiological characteristic data of the first player controlling the joystick, wherein the first physiological characteristic data includes at least historical first physiological characteristic data; Acquire the first player's activity data while controlling the joystick; Obtain the joystick movement trajectory curve of the first player when controlling the joystick; The step of obtaining the first physiological characteristic data of the first player who controls the joystick includes: A first sensor group for collecting visual data, pressure data, and contact area data is disposed on the surface of the rocker arm; The first sensor group is used to capture multi-view images of the first player to obtain three-dimensional data of the hand, including the palm, fingers and skin; The pressure distribution data of the first player's fingers and palms when using the joystick are detected by the first sensor group; The first sensor group is used to detect the change in the contact area of ​​the palm and fingers of the first operator when using the joystick; The first sensor group is used to detect the change in the deflection angle of the palm and fingers of the first operator when using the joystick; Using computer vision technology, semantic annotation is performed on the three-dimensional data of the hand, the pressure distribution data, the contact area change, and the deviation angle change to extract physiological feature data representing physiological characteristics; Based on the obtained physiological feature data, a hand physiological feature parameter model containing the first physiological feature data is constructed using a neural network algorithm; The steps for obtaining the first player's initial activity data while controlling the joystick include: Using an inertial sensor, data on the motion changes of the hand joystick of the first player in three-dimensional space, including movement, acceleration, and posture, are obtained. An electromagnetic position tracker is positioned directly above the first player to calculate the three-dimensional coordinates of the hand based on the distribution of magnetic fields in space. The first player wears a multi-channel smart monitoring bracelet to monitor hand physiological data in real time during the operation process. The hand physiological data includes at least changes in the electrical conductivity and temperature of the palm skin. Natural language processing techniques are used to semantically annotate the motion change data, the three-dimensional coordinates of the palm, and the physiological data of the hand, and to extract activity state feature data representing the activity state.

5. The method for optimizing the sensitivity setting of a game joystick according to claim 4, characterized in that, The acquisition of the joystick movement trajectory curve when the first player controls the joystick includes: A laser tracker is configured on the joystick to obtain the joystick coordinates in a two-dimensional plane of the joystick's active surface in real time. The joystick's motion data in a preset three-dimensional space is detected by a high-speed motion tracking sensor built into the joystick. By combining the joystick coordinates with the joystick motion data, and using a data fusion algorithm, the position coordinate sequence of the joystick in the three-dimensional space is calculated; The position coordinate sequence is imported into the 3D modeling software, and the 3D curve fitting tool is used to generate the joystick swing trajectory curve from all the discrete coordinate points.

6. The method for optimizing the sensitivity setting of a game joystick according to claim 5, characterized in that, The step of determining the preset sensitivity calibration coefficient of the first player based on the joystick movement trajectory curve, the first physiological characteristic data, and the first activity characteristic data includes: Hand physiological features and activity features are extracted from the first physiological feature data and the first activity feature data, respectively; Based on the physiological characteristics of the hands and the activity characteristics, a clustering algorithm is used to identify the joystick control mode used by the first player. Based on the joystick movement trajectory curve, analyze the user's speed preference and joystick amplitude preference, and fit the joystick control parameters of the first player. Based on the joystick control mode and the joystick control parameters, a preset sensitivity calibration coefficient matching the joystick is selected from the knowledge base using a BP neural network algorithm.

7. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that, The method for obtaining the current hardware sensitivity value also includes: A first deep learning model is preset, which is used to identify when the first player switches to the second player. If a player switching operation occurs, the current hardware sensitivity value is set to an abnormal state. The input to the first deep learning model is the player's first physiological characteristic data and first activity characteristic data.

8. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that, The specific implementation process of performing sensitivity software cycle optimization settings when the preset time period T is satisfied is as follows: Extract the joystick temperature and shaking resistance corresponding to a preset time period T. Identify the inertial joystick temperature and inertial shaking resistance of the first player within the preset time period T. Then, extract the inertial joystick temperature corresponding to each software program within the preset time period T. and inertial sway resistance ; Extract the joystick detection temperature over a preset time period T. and shaking to detect resistance The compensation function for the sensitivity adjustment of each software program as it increases over time is analyzed as follows: ， Then, when each software program meets the preset time period T, the sensitivity is adjusted as follows: ， Where W represents the compensation weight for the temperature of the inertial rocker arm, and Q represents the compensation weight for the inertial rocking resistance.

9. A sensitivity setting optimization system for a game joystick, used to execute a sensitivity setting optimization method for a game joystick as described in any one of claims 1 to 8, characterized in that, include: The sensitivity hardware calibration module is used to obtain the current displacement coordinates and current hardware sensitivity value of the joystick. Determine if the current hardware sensitivity value is normal; if it is abnormal, perform sensitivity hardware optimization settings. The sensitivity software calibration module is used to preset the time period T. When the time meets the preset time period T, the sensitivity software period optimization setting is executed.

Images