CN116637360A - Motion sensing table tennis game method based on function fitting - Google Patents

Motion sensing table tennis game method based on function fitting Download PDF

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Publication number
CN116637360A
CN116637360A CN202310659934.5A CN202310659934A CN116637360A CN 116637360 A CN116637360 A CN 116637360A CN 202310659934 A CN202310659934 A CN 202310659934A CN 116637360 A CN116637360 A CN 116637360A
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Prior art keywords
swing
function
table tennis
fitting
data
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张可
姚远
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Shenzhen Shimi Network Technology Co ltd
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Shenzhen Shimi Network Technology Co ltd
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Priority to CN202310659934.5A priority Critical patent/CN116637360A/en
Publication of CN116637360A publication Critical patent/CN116637360A/en
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    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/40—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment
    • A63F13/42—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment by mapping the input signals into game commands, e.g. mapping the displacement of a stylus on a touch screen to the steering angle of a virtual vehicle
    • A63F13/428—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment by mapping the input signals into game commands, e.g. mapping the displacement of a stylus on a touch screen to the steering angle of a virtual vehicle involving motion or position input signals, e.g. signals representing the rotation of an input controller or a player's arm motions sensed by accelerometers or gyroscopes
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/20—Input arrangements for video game devices
    • A63F13/21—Input arrangements for video game devices characterised by their sensors, purposes or types
    • A63F13/211—Input arrangements for video game devices characterised by their sensors, purposes or types using inertial sensors, e.g. accelerometers or gyroscopes
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/20—Input arrangements for video game devices
    • A63F13/21—Input arrangements for video game devices characterised by their sensors, purposes or types
    • A63F13/212—Input arrangements for video game devices characterised by their sensors, purposes or types using sensors worn by the player, e.g. for measuring heart beat or leg activity
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/55—Controlling game characters or game objects based on the game progress
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/60—Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor
    • A63F13/67—Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/80—Special adaptations for executing a specific game genre or game mode
    • A63F13/812—Ball games, e.g. soccer or baseball
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/10—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals
    • A63F2300/1012—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals involving biosensors worn by the player, e.g. for measuring heart beat, limb activity
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/10—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals
    • A63F2300/105—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals using inertial sensors, e.g. accelerometers, gyroscopes
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/60—Methods for processing data by generating or executing the game program
    • A63F2300/6027—Methods for processing data by generating or executing the game program using adaptive systems learning from user actions, e.g. for skill level adjustment
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/60—Methods for processing data by generating or executing the game program
    • A63F2300/6045—Methods for processing data by generating or executing the game program for mapping control signals received from the input arrangement into game commands
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/80—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game specially adapted for executing a specific type of game
    • A63F2300/8011—Ball

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Human Computer Interaction (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Cardiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Heart & Thoracic Surgery (AREA)
  • User Interface Of Digital Computer (AREA)

Abstract

The invention discloses a body sensing table tennis game method, a device, equipment and a computer readable storage medium based on function fitting, wherein the method comprises the following steps: after the somatosensory game is started, acquiring gyroscope data from the bound somatosensory equipment; generating track data of motion of the somatosensory device in space according to the gyroscope data; fitting the track data to obtain a swing function representing the swing action of the user; classifying the swing function to obtain the swing action type of the user; and controlling the game role to execute the swing operation according to the swing action type. The motion sensing table tennis game method based on function fitting has the advantages of high motion recognition precision, strong generalization, high flexibility, simplicity, high efficiency and the like.

Description

Motion sensing table tennis game method based on function fitting
Technical Field
The present invention relates to the field of motion sensing game technologies, and in particular, to a motion sensing table tennis game method, device, apparatus and computer readable storage medium based on function fitting.
Background
Somatosensory games are a way of playing a game by capturing physical actions of a user to control the behavior of a character and progress of the game.
There are two main fluid-sensing game schemes on the market:
1. based on a Kinetic architecture, the amplitude and the direction of motion are judged by using a time slot depth image and a human skeleton through a track point model. 2. Based on the machine learning model, a model such as a support vector machine is trained by using gyroscope data, and actions of a user are recognized through the action recognition model.
Both of these schemes have drawbacks:
1. based on the scheme of the Kinetic architecture, generalization performance is limited because different human body structures are greatly different. 2. Based on the scheme of the machine learning model, the recognition accuracy is not high, because gyroscopes have the problem of linear drift, and different gyroscopes have different drift degrees.
Disclosure of Invention
The embodiment of the application provides a motion sensing table tennis game method based on function fitting, aiming at improving the accuracy and generalization of the recognition of the swing motion in the motion sensing table tennis game.
In order to achieve the above object, the embodiment of the present application provides a motion sensing table tennis game method based on function fitting, including:
after the somatosensory game is started, acquiring gyroscope data from the bound somatosensory equipment;
generating track data of motion of the somatosensory device in space according to the gyroscope data;
Fitting the track data to obtain a swing function representing the swing action of the user;
classifying the swing function to obtain the swing action type of the user;
and controlling the game role to execute the swing operation according to the swing action type.
In an embodiment, generating trajectory data of motion of the motion sensing device in space from the gyroscope data includes:
generating a grid map composed of a plurality of grid cells and a virtual rigid body matched with the somatosensory equipment on a game terminal for executing the somatosensory game;
updating the position of the virtual rigid body in the grid map according to the gyroscope data;
and recording coordinate data of grid cells passed by the virtual rigid body in the moving process of the virtual rigid body in the grid map as the track data.
In one embodiment, fitting the trajectory data to obtain a swing function representing a user swing motion includes:
substituting the track data into a preset linear regression equation for fitting so as to obtain the swing function.
In one embodiment, classifying the swing function to obtain a swing type of the user includes:
classifying the swing function through a plurality of pre-fitted table tennis action functions;
And obtaining the swing action type of the user according to the classification result.
In one embodiment, classifying the swing function by a pre-fit plurality of action functions includes:
calculating the similarity of the swing function compared with a plurality of table tennis action functions to obtain a plurality of similarity scores;
and obtaining a classification result of the swing function according to the similarity scores.
In one embodiment, calculating a similarity score for the swing function as compared to a table tennis action function includes:
score＝b T x；
where b is the coefficient vector of the linear regression model and x is the feature data vector.
In an embodiment, obtaining the classification result of the swing function according to the plurality of similarity scores includes:
and selecting the action type corresponding to the highest similarity score as a classification result.
In order to achieve the above object, the embodiment of the present application further provides a motion sensing table tennis game apparatus based on function fitting, including:
the acquisition module is used for acquiring gyroscope data from the bound somatosensory equipment after the somatosensory game is started;
the generation module is used for generating track data of the motion of the somatosensory equipment in space according to the gyroscope data;
the fitting module is used for fitting the track data to obtain a swing function representing the swing action of the user;
The identification module is used for classifying the swing function to obtain the swing action type of the user;
and the execution module is used for controlling the game role to execute the swing operation according to the swing action type.
In order to achieve the above objective, an embodiment of the present application further provides a motion sensing table tennis game device based on function fitting, which includes a memory, a processor, and a motion sensing table tennis game program based on function fitting stored in the memory and capable of running on the processor, wherein the processor implements the motion sensing table tennis game method based on function fitting according to any one of the above when executing the motion sensing table tennis game program based on function fitting.
To achieve the above object, an embodiment of the present application further provides a computer readable storage medium, where a motion sensing table tennis game program based on function fitting is stored on the computer readable storage medium, where the motion sensing table tennis game program based on function fitting implements the motion sensing table tennis game method based on function fitting according to any one of the above when executed by a processor.
It can be understood that according to the motion sensing table tennis game method based on function fitting in the technical scheme of the application, track data of motion sensing equipment in space is generated through gyroscope data, a swing function is obtained through fitting the track data, finally, the swing action type of a user is identified based on the swing function, and the game role is controlled to execute corresponding swing operation, so that on one hand, the function fitting method can model and describe the swing action of the user according to actual swing track data. By fitting the function, the details and the characteristics of the swing motion can be accurately captured, so that the real swing motion characteristics are reflected more accurately, and the accuracy of motion recognition is improved. On the other hand, the universal action mode and rule can be learned from the sample data through function fitting, so that the identification of the new swing action is realized, and the generalization of the swing action type identification is improved. In addition, the function fitting method can select different function forms to model the swing action according to actual requirements. The flexibility enables the function fitting method to adapt to different types of swing actions, and has high adaptability and expansibility. Moreover, the calculation and classification of the swing function can be completed in a shorter time, and the game running efficiency is improved. Compared with the traditional somatosensory game scheme, the somatosensory table tennis game method has the advantages of high motion recognition precision, strong generalization, high flexibility, simplicity, high efficiency and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of one embodiment of a motion sensing table tennis game apparatus based on function fitting in accordance with the present invention;
FIG. 2 is a schematic flow chart of an embodiment of a motion sensing table tennis game method based on function fitting according to the present invention;
FIG. 3 is a block diagram of one embodiment of a motion sensing table tennis game apparatus based on function fitting in accordance with the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order that the above-described aspects may be better understood, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. And the use of "first," "second," and "third," etc. do not denote any order, and the terms may be construed as names.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a server 1 (also called a somatosensory table tennis game device based on function fitting) of a hardware running environment according to an embodiment of the present invention.
The server provided by the embodiment of the invention is equipment with display function, such as 'Internet of things equipment', intelligent air conditioner with networking function, intelligent electric lamp, intelligent power supply, AR/VR equipment with networking function, intelligent sound box, automatic driving automobile, PC, intelligent mobile phone, tablet personal computer, electronic book reader, portable computer and the like.
As shown in fig. 1, the server 1 includes: memory 11, processor 12 and network interface 13.
The memory 11 includes at least one type of readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the server 1, such as a hard disk of the server 1. The memory 11 may in other embodiments also be an external storage device of the server 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the server 1.
Further, the memory 11 may also include an internal storage unit of the server 1 as well as an external storage device. The memory 11 may be used not only for storing application software installed in the server 1 and various kinds of data such as codes of the motion sensing table tennis game program 10 based on function fitting, but also for temporarily storing data that has been output or is to be output.
Processor 12 may in some embodiments be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing data stored in memory 11, such as executing a function fitting based motion sensing table tennis game program 10 or the like.
The network interface 13 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), typically used to establish a communication connection between the server 1 and other electronic devices.
The network may be the internet, a cloud network, a wireless fidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local Area Network (LAN), and/or a Metropolitan Area Network (MAN). Various devices in a network environment may be configured to connect to a communication network according to various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, at least one of the following: transmission control protocol and internet protocol (TCP/IP), user Datagram Protocol (UDP), hypertext transfer protocol (HTTP), file Transfer Protocol (FTP), zigBee, EDGE, IEEE 802.11, light fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communications, wireless Access Points (APs), device-to-device communications, cellular communication protocol and/or bluetooth (bluetooth) communication protocol, or combinations thereof.
Optionally, the server may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or a display unit, for displaying information processed in the server 1 and for displaying a visual user interface.
Fig. 1 shows only a server 1 having components 11-13 and a motion sensing table tennis game program 10 based on function fitting, it will be understood by those skilled in the art that the structure shown in fig. 1 is not limiting of the server 1 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In this embodiment, the processor 12 may be configured to call the motion sensing table tennis game program based on function fitting stored in the memory 11, and perform the following operations:
after the somatosensory game is started, acquiring gyroscope data from the bound somatosensory equipment;
generating track data of motion of the somatosensory device in space according to the gyroscope data;
fitting the track data to obtain a swing function representing the swing action of the user;
classifying the swing function to obtain the swing action type of the user;
and controlling the game role to execute the swing operation according to the swing action type.
In one embodiment, the processor 12 may be configured to invoke the motion sensing table tennis game program stored in the memory 11 based on function fitting and perform the following operations:
generating a grid map composed of a plurality of grid cells and a virtual rigid body matched with the somatosensory equipment on a game terminal for executing the somatosensory game;
Updating the position of the virtual rigid body in the grid map according to the gyroscope data;
and recording coordinate data of grid cells passed by the virtual rigid body in the moving process of the virtual rigid body in the grid map as the track data.
In one embodiment, the processor 12 may be configured to invoke the motion sensing table tennis game program stored in the memory 11 based on function fitting and perform the following operations:
substituting the track data into a preset linear regression equation for fitting so as to obtain the swing function.
In one embodiment, the processor 12 may be configured to invoke the motion sensing table tennis game program stored in the memory 11 based on function fitting and perform the following operations:
classifying the swing function through a plurality of pre-fitted table tennis action functions;
and obtaining the swing action type of the user according to the classification result.
In one embodiment, the processor 12 may be configured to invoke the motion sensing table tennis game program stored in the memory 11 based on function fitting and perform the following operations:
calculating the similarity of the swing function compared with a plurality of table tennis action functions to obtain a plurality of similarity scores;
and obtaining a classification result of the swing function according to the similarity scores.
In one embodiment, the processor 12 may be configured to invoke the motion sensing table tennis game program stored in the memory 11 based on function fitting and perform the following operations:
calculating a similarity score for the swing function as compared to a table tennis action function, comprising:
score＝b T x；
where b is the coefficient vector of the linear regression model and x is the feature data vector.
In one embodiment, the processor 12 may be configured to invoke the motion sensing table tennis game program stored in the memory 11 based on function fitting and perform the following operations:
and selecting the action type corresponding to the highest similarity score as a classification result.
Based on the hardware architecture of the motion sensing table tennis game device based on function fitting, the embodiment of the motion sensing table tennis game method based on function fitting is provided. The invention discloses a motion sensing table tennis game method based on function fitting, which aims to improve accuracy and generalization of recognition of a swing motion in the motion sensing table tennis game.
Referring to fig. 2, fig. 2 is an embodiment of a body feeling table tennis game method based on function fitting according to the present invention, the body feeling table tennis game method based on function fitting includes the following steps:
s10, after the somatosensory game is started, acquiring gyroscope data from the bound somatosensory equipment.
The body sensing game is a body sensing table tennis game, which is a virtual reality game based on a body sensing technology, and a player can participate in a table tennis experience in a real mode through special sensors and equipment. Compared with the traditional game handle or keyboard, the somatosensory table tennis game converts the real actions and gestures of a player into table tennis actions in the game by capturing the real actions and gestures of the player.
Alternatively, the motion sensing table tennis game may be a web-based web game, an html 5-based applet, or an independently running app.
Somatosensory devices are a class of devices used to capture, recognize and translate physical actions of players. They typically include sensors, controllers, and associated hardware components, intended for use in conjunction with interactive experience techniques such as electronic games, virtual reality, augmented reality, and the like. In the technical scheme of the application, the somatosensory equipment collects somatosensory data of a user, wherein the somatosensory data comprises three-axis angular velocity data and three-axis acceleration data.
Alternatively, the somatosensory device adopted in the technical scheme of the application comprises, but is not limited to, a mobile phone with a gyroscope, a bracelet, a watch, a ring, a handle, a wrist strap and the like.
Further, the gyroscope measures the direction and angular velocity of the device by sensing the force and acceleration of rotation and rotation to output gyroscope data. Gyroscopes typically provide a data output in three axes (X, Y, Z). For each axis, the gyroscope will provide a continuously varying value indicative of the rotational rate or angular change in that axis. These values are typically expressed in units of angular velocity (e.g., degrees/second) or units of angle (e.g., degrees).
Alternatively, the connection between the motion sensing device and the game terminal can be realized by USB, bluetooth or 2.4G, and the game terminal is a terminal running motion sensing game, and can be a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palm machine, or a fixed terminal such as a desktop computer, a home host machine, etc.
After the motion sensing table tennis game is started, the game terminal can receive motion sensing device gyroscope data based on a connection protocol with the motion sensing device.
S20, generating track data of the motion of the somatosensory device in space according to the gyroscope data.
Wherein the track data is used for recording the motion track of the somatosensory device in the space so as to represent the ping-pong swing action track of the user.
Specifically, the gyroscope data may be converted into spatial coordinate values by a rotation matrix, a quaternion algorithm, or the like, and the spatial coordinate values are mapped with a time sequence to generate trajectory data of the somatosensory device in space.
It should be noted that when converting gyroscope data, we can perform preprocessing such as filtering, interpolation, normalization, etc. on the data to improve the quality and usability of the data.
S30, fitting the track data to obtain a swing function representing the swing action of the user.
Wherein the swing function is a functional expression obtained by modeling and fitting data of the user swing motion. This function describes the law of variation of the trajectory, speed, acceleration or other relevant parameters of the user's swing.
Further, the swing function is significant in modeling and describing the swing of the user. Through the swing function, the game system can acquire detailed information about the user's swing motion, such as the start point, end point, speed change, shape of swing trajectory, etc. of the motion. The information can be used for judging the type of the swing action of the user, such as the forward batting, the backward batting, the ball serving and the like, so as to control the game role to make corresponding reactions.
In addition, the swing function may also be used to evaluate the swing technique and motion quality of the user. By analyzing parameters and curve characteristics of the swing function, factors such as accuracy, stability, strength and the like of the swing of the user can be evaluated, so that feedback and guidance are provided, and the user is helped to improve the swing technology.
In particular, fitting trajectory data to obtain a swipe function typically involves selecting an appropriate functional form and using a fitting algorithm. When the method is applied, a proper function form can be selected as a model of the swing function according to the characteristics of the swing motion and the data analysis requirement. For example, a polynomial function, a trigonometric function, a gaussian function, etc. may be selected. The selected model is then fixed in the game and the trajectory data is prepared in a form suitable for fitting as the game progresses. It is often necessary to convert the trajectory data into a set of input-output pairs (input-output pairs), where the input is an argument associated with the swing motion and the output is the corresponding trajectory data. Next, a suitable fitting algorithm is selected to fit the trajectory data and obtain a swing function. Common fitting algorithms include least squares, maximum likelihood estimation, curve fitting algorithms, and the like.
Wherein, when the wave function is obtained by fitting, the parameters of the wave function can be estimated by using a selected fitting algorithm. This typically involves an optimization algorithm to minimize fit errors or maximize likelihood functions.
It should be noted that fitting the trajectory data to the result of the swing function may not describe all swing actions perfectly. The decision of function selection, parameter estimation and the like in the fitting process can influence the fitting result. Therefore, in practical application, adjustment and optimization are needed according to specific situations to obtain an optimal swing function model.
S40, classifying the swing function to obtain the swing action type of the user.
Specifically, by classifying and analyzing the swing function, the swing action type of the user, such as a forward shot, a reverse shot, or the like, can be determined.
In particular, a classification model may be constructed using machine learning or deep learning algorithms to classify the swing function. Common classification algorithms include support vector machines (Support Vector Machine), random Forest (Random Forest), neural networks, and the like.
Further, when using machine learning or deep learning algorithms to construct a classification model to classify a swing function, we typically need to do the following:
1. and (3) data acquisition: a set of swing function data of known motion types is collected as training samples. These samples should cover different types of swing motions, such as a forward shot, a reverse shot, a tee shot, etc.
2. Feature extraction: key features are extracted from each swing function. These features may include curve shape of the swipe function, peak position, slope change, etc. The goal of feature extraction is to transform the swing function into a set of distinguishing numerical features.
3. Data marking: and marking the training samples, and indicating the type of the swing action corresponding to each sample. Numbers or labels may be used to represent different action types.
4. Model training: a classification model is constructed using machine learning or deep learning algorithms. The characteristics are used as input, the corresponding swing action type is used as output, and the association between the swing function and the action type is learned through a training model.
5. Model evaluation: and testing and evaluating the trained classification model by using an evaluation data set, and checking performance indexes such as accuracy, recall rate, accuracy and the like of the model. If the performance of the model is not ideal, parameters of the model can be adjusted or improved by adopting other algorithms.
6. Action classification: and classifying the new swing function by using the trained classification model. And inputting the swing function of the unknown action type into the classification model, and predicting and outputting the corresponding action type by the model.
It is worth noting that the performance of the classification model depends on the quality and diversity of the training samples, as well as the accuracy of feature extraction. Therefore, before performing swing classification, it is ensured that there is enough and representative training data, and a proper feature extraction method and classification algorithm are selected to improve the accuracy and stability of the classification model.
It can be appreciated that the recognition of the type of the ping-pong swing is performed by using function fitting, and on one hand, the function fitting method can model and describe the swing motion of the user according to the actual swing track data. By fitting the function, the details and characteristics of the swing motion, including the starting point, the end point, the track shape, the speed change and the like, can be accurately captured. Compared with other sensors or methods based on rules, the function fitting reflects the actual swing motion characteristics more accurately, and the accuracy of motion recognition is improved.
On the other hand, the function fitting method can obtain a universal swing function model through fitting based on the collected multiple groups of sample data. The model can be used for identifying the swing action of different users under different scenes and has certain generalization capability. Through function fitting, a universally applicable action mode and rule can be learned from sample data, and the identification of a new swing action is realized. The generalization of the method enables the function fitting method to have strong adaptability and practicability.
In addition, the function fitting method can select different function forms to model the swing action according to actual requirements. Polynomial functions, trigonometric functions, gaussian functions, etc. may be selected to fit the swing trajectory data, as the case may be. The flexibility enables the function fitting method to adapt to different types of swing actions, and has high adaptability and expansibility. And the function fitting method can be used for quickly calculating and deducing, so that the action recognition can be applied in a real-time scene. The calculation and classification of the swing function can be completed in a short time, and the game running efficiency is improved.
S50, controlling the game role to execute the swing operation according to the swing action type.
Specifically, in a motion-sensing table tennis game, there is a corresponding game operation instruction for each swing type. For example, for a hand batting action, it may be mapped to a forward swipe operation in a game; for a backhand batting action, it may be mapped to a side swing in the game.
Based on the above, after determining the type of the swing action executed by the user, the corresponding instruction can be transmitted to the game character for execution according to the game operation instruction corresponding to the identified action type. This may be accomplished through interaction with a game engine or controller, such as by way of a programming interface, controller input, or the like, that communicates instructions to the gaming system. Thus, the reproduction of the swing action of the user can be realized.
It can be understood that according to the motion sensing table tennis game method based on function fitting in the technical scheme of the application, track data of motion sensing equipment in space is generated through gyroscope data, a swing function is obtained through fitting the track data, finally, the swing action type of a user is identified based on the swing function, and the game role is controlled to execute corresponding swing operation, so that on one hand, the function fitting method can model and describe the swing action of the user according to actual swing track data. By fitting the function, the details and the characteristics of the swing motion can be accurately captured, so that the real swing motion characteristics are reflected more accurately, and the accuracy of motion recognition is improved. On the other hand, the universal action mode and rule can be learned from the sample data through function fitting, so that the identification of the new swing action is realized, and the generalization of the swing action type identification is improved. In addition, the function fitting method can select different function forms to model the swing action according to actual requirements. The flexibility enables the function fitting method to adapt to different types of swing actions, and has high adaptability and expansibility. Moreover, the calculation and classification of the swing function can be completed in a shorter time, and the game running efficiency is improved. Compared with the traditional somatosensory game scheme, the somatosensory table tennis game method has the advantages of high motion recognition precision, strong generalization, high flexibility, simplicity, high efficiency and the like.
In some embodiments, generating trajectory data of motion of the somatosensory device in space from the gyroscope data comprises:
s21, generating a grid map formed by a plurality of grid units and a virtual rigid body matched with the somatosensory equipment on a game terminal for executing the somatosensory game.
Among these, the grid map is an image representation method that divides a space into regular grid cells. It divides the entire space into discrete small areas, each of which is referred to as a grid cell, which may be square, rectangular or other shape. Each grid cell has a unique identifier and coordinates that represent its location throughout the map. In the technical scheme of the application, the grid map is used for representing the layout of the game scene. Each grid cell may correspond to a region or grid of fixed size for recording the position of the virtual rigid body in the game.
Specifically, after the motion sensing table tennis game is started, a grid map can be constructed based on preset initialization parameters, and the grid map can be a two-dimensional map (such as a plane map) or a three-dimensional map (such as a stereoscopic scene). It should be noted that, according to the preset initialization parameters, the size of each grid cell and the coordinate value of each grid cell are determined while generating the grid map.
Further, a rigid body refers to an object having a fixed shape and mass in the physical world, and not being deformed or bent. The virtual rigid body is then a virtual model with corresponding physical properties. In the technical scheme of the application, the virtual rigid body is used for recording the moving track of the somatosensory equipment in the grid map.
S22, updating the position of the virtual rigid body in the grid map according to the gyroscope data.
Specifically, the gyroscope data can be converted into coordinate values of the grid map by a motion equation, an integral method (such as an euler method or a Longer-Kutta method) and the like, so as to update the position of the virtual rigid body in the grid map.
S23, recording coordinate data of grid cells passing through in the moving process of the virtual rigid body in the grid map as the track data.
Specifically, a data structure may be created to store coordinate data of grid cells through which the virtual rigid body passes, and the trajectory data may be represented using data structures such as arrays, lists, matrices, and the like. In the game process, the coordinate values of the grid unit where the virtual rigid body is located can be recorded into a track data structure according to the time sequence to be used as track data of the somatosensory equipment in the space.
It will be appreciated that the above scheme may convert a continuous sequence of coordinates into a discrete sequence of grid cells by representing the trajectory data of the somatosensory device as processed grid coordinates. In this way, small changes between adjacent coordinates in the middle are removed, so that redundancy and repetition of data can be reduced, representation and storage of the data are optimized, and calculation efficiency of a motion track can be improved.
In some embodiments, fitting the trajectory data to obtain a swing function representing a user swing motion includes:
substituting the track data into a preset linear function to perform fitting so as to obtain the swing function.
Wherein the expression of the preset linear function is: y=ax+b;
wherein y is a coordinate value of the grid map in the y-axis direction; x is the coordinate value of the x-axis direction in the grid map; a and b are fitting parameters to be solved.
Specifically, a and b represent the slope and intercept, respectively, of the linear function described above.
It will be appreciated that the linear function y=ax+b is a simple and intuitive linear function expression that is easy to understand and implement. Meanwhile, due to the simple characteristic, the method can be quickly calculated in practical application, so that fitting efficiency is improved. Furthermore, the slope parameter a reflects the degree of inclination of the fitting line, and the intercept parameter b represents the intersection of the fitting line with the coordinate axis. The values of these parameters can be used to interpret the fit results so that the current table tennis action can be visually represented.
Of course, the design of the present application is not limited thereto, and in other embodiments, other linear functions may be used as the preset linear function, such as y=mx; y=a 0 +a 1 x 1 +a 2 x 2 +...+a n x n ；y＝a 0 +a 1 x 1 +a 2 x 1 2 +...+a n x 1 n Etc.
Alternatively, a predetermined linear function may be fitted by a least square method, and the required fitting parameters a and b may be found by a minimum fit.
Specifically, the steps of fitting a preset linear function by the least square method and calculating the fitting parameters a and b are specifically as follows:
(1) Calculating the mean value of the independent variable x and the dependent variable y in the track data, which are respectively expressed asAnd->
(2) Calculating the deviation between each data sample point and the mean value, respectively expressed asAnd
(3) Calculating the slope:
(4) Calculating intercept:
it will be appreciated that linear functions generally have relatively stable properties over a range. Thus, although the drift degree of different gyroscopes can be different, the fitting of the linear function can approximately capture the overall trend of the gyroscope data, so that the data change in a smaller range can obtain a better fitting effect. Therefore, the influence of different gyroscope drift degrees can be balanced to a certain extent through linear function fitting, and the recognition accuracy is improved.
Moreover, the linear function fitting method is simple and efficient to calculate relative to more complex nonlinear functions or machine learning models. It does not need a lot of training data and complex training process, nor complex parameter tuning. This makes the linear function fit easier to implement and apply, reducing the complexity of processing gyroscope data.
In addition, the method can be used for quickly adapting to different data sets and action types by adjusting the preset linear function, so that the adaptation capability of the motion sensing table tennis game to different users can be improved, and the action recognition scheme has stronger generalization.
It should be noted that the design of the present application is not limited thereto, and in other embodiments, the preset linear function may be fitted by a gradient descent method, a maximum likelihood method, or the like.
It is also worth noting that in other embodiments, other more complex functional or nonlinear regression methods may be tried to better fit the characteristics and patterns of the swing.
In some embodiments, classifying the swing function to obtain a swing type for the user includes:
s41, classifying the swing function through a plurality of pre-fitted table tennis action functions.
Wherein, the pre-fitted multiple table tennis action functions refer to function models obtained by modeling and training aiming at different table tennis action types in advance. The function models can be set according to expert knowledge and experience, and can also be obtained by a method of data analysis and machine learning of real table tennis movement data.
The pre-fitted table tennis action function may be used to represent characteristics and patterns of different types of table tennis swing actions. For example, for different types of actions such as a forward shot, a reverse shot, a service, etc., the corresponding function models may be fitted separately.
It should be noted that the pre-fitted table tennis action functions and the swing function are the same type of functions, such as linear functions, polynomial functions, trigonometric functions, and the like.
Specifically, the user's swing function is matched to a pre-fitted table tennis action function. And determining the table tennis action function which is most matched with the user swing function by calculating the similarity or distance between the swing function and each action function.
S42, obtaining the swing action type of the user according to the classification result.
Specifically, according to the matching result of the user's swing function and the pre-fitted multiple swing functions, classifying the user's swing function into the corresponding table tennis action type. And determining the type of the swing action of the user according to the type of the matched table tennis action function. For example, if the user's swing function best matches the pre-fitted hand shot function, it may be classified as a type of hand shot action.
Based on the above steps, on one hand, by fitting a plurality of table tennis action functions in advance, an accurate function model can be established according to real data and a kinematics principle. The function models can well capture the characteristics and modes of different swing actions, so that the classification accuracy is improved. By matching the metrics with the pre-fit function, the particular swing type performed by the user can be more accurately determined.
On the other hand, the pre-fitted functional model may cover a variety of table tennis action types, such as forward batting, reverse batting, tee-shot, etc. The classification method can adapt to different swing action requirements and can be conveniently expanded to new action types. Only a new pre-fit function model needs to be added without retraining the entire classification system.
In addition, since the pre-fitted function model is calculated and stored in advance, the classification process can be performed under the condition of high real-time requirements. Once the user's swing function is obtained, the matching with the pre-fitting function can be quickly performed, and the action type can be quickly determined, so that real-time feedback and response are realized.
Moreover, the pre-fitted function model has certain robustness and can tolerate noise and variation of input data to a certain extent. The classification method has certain fault tolerance capability for small changes and noise in a plurality of swing functions, and the stability and the reliability of classification are improved.
In conclusion, the pre-fitting function is adopted to classify the swing function, so that the accuracy, the expandability, the real-time performance and the robustness are high. The method can provide accurate action classification and finer and reliable user experience for applications such as somatosensory table tennis games.
In some embodiments, classifying the swing function by a pre-fit plurality of action functions includes:
s411, calculating the similarity of the swing function compared with the plurality of table tennis action functions, and obtaining a plurality of similarity scores.
Wherein, the similarity is a measure for measuring the degree of similarity or similarity between two objects. In the scheme of the application, the similarity is used for evaluating the similarity degree between the swing function and the pre-fitted table tennis action function.
In some embodiments, calculating a similarity score for the swing function as compared to a plurality of table tennis action functions includes:
score＝b T x；
where b is the coefficient vector of the linear regression model and x is the feature data vector.
Specifically, the characteristic data vector is a one-dimensional array that contains some characteristic data of a table tennis player, such as speed, acceleration, angle, strength, or time. These characteristic data may be used to describe the state of motion and skill level of the table tennis player. For example, one feature data vector might be [10,5,30,8,0.5], indicating that the speed of a table tennis player is 10 m/s, the acceleration is 5 m/s 2, the angle is 30 degrees, the force is 8 newtons, and the time is 0.5 seconds.
Further, the action scoring formula (i.e., the similarity score calculation formula) is to perform a point multiplication on the coefficient vector and the feature data vector to obtain a scalar value, which represents the score. The higher the score, the more suitable the current feature data, i.e., the more likely it is for the user to perform such an action. For example, if the score of a ball is 0.8, the score of the right hand is 0.6, and the score of the left hand is 0.4, then the predicted outcome is a ball because it has the highest score.
Specifically, the action scoring formula is based on the principle of linear regression, which is a statistical method used to estimate the linear relationship between two or more variables. The basic form of linear regression is: y= bTx +; where y is the dependent variable (i.e., the table tennis action to be predicted), x is the independent variable (i.e., characteristic data of the table tennis player), b is the coefficient vector of the linear regression model, and e is the error term. The goal of linear regression is to find an optimal set of coefficient vectors b that minimizes the sum of squares of the error terms, i.e. minimizes the difference between the predicted value and the observed value.
Compared with the linear regression formula, the action scoring formula ignores the error term, and only uses the coefficient vector and the characteristic data vector to calculate the value of the dependent variable, namely the score of the table tennis action. The meaning of this formula is that it can reflect the similarity or consistency between the feature data vector and the coefficient vector, the larger the two vectors are if they are closer, the smaller they are if they are multiplied by points. Thus, the action scoring formula may be used to measure the similarity between each type of table tennis action and the user's characteristic data, the higher the similarity, the more likely such an action is to be performed by the user.
And calculating the similarity scores of the swing function and the plurality of table tennis action functions based on the action scoring formula.
Of course, the design of the present application is not limited thereto, and in other embodiments, the similarity score may be measured by using different methods, such as euclidean distance, cosine similarity, or correlation coefficient, to measure the similarity between the swing function and the pre-fit action function.
S412, obtaining a classification result of the swing function according to the similarity scores.
Specifically, a classification result of the swing function is determined according to the plurality of similarity scores. The action type corresponding to the highest score may be selected as the classification result, representing that the swing function is closest to the function model of that action type. Of course, the threshold may also be set, and only action types with similarity scores higher than the threshold are considered as classification results.
In addition, referring to fig. 3, the embodiment of the application further provides a motion sensing table tennis game device based on function fitting, and the motion sensing table tennis game device based on function fitting comprises:
an acquisition module 110, configured to acquire gyroscope data from the bound somatosensory device after the somatosensory game is started;
a generating module 120, configured to generate trajectory data of motion of the somatosensory device in space according to the gyroscope data;
A fitting module 130, configured to fit the trajectory data to obtain a swing function representing a swing motion of the user;
the identification module 140 is configured to classify the swing function to obtain a swing action type of the user;
and the execution module 150 is used for controlling the game role to execute the swing operation according to the swing action type.
The steps implemented by each functional module of the motion sensing table tennis game device based on function fitting may refer to each embodiment of the motion sensing table tennis game method based on function fitting of the present invention, and will not be described herein.
In addition, the embodiment of the invention also provides a computer readable storage medium, which can be any one or any combination of a plurality of hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory and the like. The computer readable storage medium includes the motion sensing table tennis game program 10 based on function fitting, and the specific embodiment of the computer readable storage medium of the present invention is substantially the same as the above-mentioned motion sensing table tennis game method based on function fitting and the specific embodiment of the server 1, and will not be repeated here.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. A somatosensory table tennis game method based on function fitting is characterized by comprising the following steps:
after the somatosensory game is started, acquiring gyroscope data from the bound somatosensory equipment;
generating track data of motion of the somatosensory device in space according to the gyroscope data;
fitting the track data to obtain a swing function representing the swing action of the user;
classifying the swing function to obtain the swing action type of the user;
and controlling the game role to execute the swing operation according to the swing action type.
2. The function fitting-based motion sensing table tennis game method of claim 1, wherein generating trajectory data of motion of a motion sensing device in space from the gyroscope data comprises:
generating a grid map composed of a plurality of grid cells and a virtual rigid body matched with the somatosensory equipment on a game terminal for executing the somatosensory game;
Updating the position of the virtual rigid body in the grid map according to the gyroscope data;
and recording coordinate data of grid cells passed by the virtual rigid body in the moving process of the virtual rigid body in the grid map as the track data.
3. The method for motion sensing table tennis game based on function fitting of claim 1, wherein fitting the trajectory data to obtain a swing function representing a user swing motion comprises:
substituting the track data into a preset linear regression equation for fitting so as to obtain the swing function.
4. The function fitting-based somatosensory table tennis game method according to claim 3, wherein classifying the swing function to obtain a swing type of a user comprises:
classifying the swing function through a plurality of pre-fitted table tennis action functions;
and obtaining the swing action type of the user according to the classification result.
5. The function-fitting-based motion sensing table tennis game method of claim 4, wherein classifying said swing function by a pre-fitted plurality of action functions comprises:
calculating the similarity of the swing function compared with a plurality of table tennis action functions to obtain a plurality of similarity scores;
And obtaining a classification result of the swing function according to the similarity scores.
6. The method for motion sensing table tennis game based on function fitting of claim 5, wherein calculating a similarity score for said swing function as compared to a table tennis action function comprises:
score＝b T x；
where b is the coefficient vector of the linear regression model and x is the feature data vector.
7. The method for motion sensing table tennis game based on function fitting of claim 6, wherein obtaining the classification result of the swing function based on the plurality of similarity scores comprises:
and selecting the action type corresponding to the highest similarity score as a classification result.
8. A motion sensing table tennis game apparatus based on function fitting, comprising:
the acquisition module is used for acquiring gyroscope data from the bound somatosensory equipment after the somatosensory game is started;
the generation module is used for generating track data of the motion of the somatosensory equipment in space according to the gyroscope data;
the fitting module is used for fitting the track data to obtain a swing function representing the swing action of the user;
the identification module is used for classifying the swing function to obtain the swing action type of the user;
And the execution module is used for controlling the game role to execute the swing operation according to the swing action type.
9. A motion sensing table tennis game apparatus based on function fitting, comprising a memory, a processor and a motion sensing table tennis game program based on function fitting stored on the memory and executable on the processor, wherein the processor implements the motion sensing table tennis game method based on function fitting of any one of claims 1-7 when executing the motion sensing table tennis game program based on function fitting.
10. A computer readable storage medium, wherein a somatosensory table tennis game program based on function fitting is stored on the computer readable storage medium, and when the somatosensory table tennis game program based on function fitting is executed by a processor, the somatosensory table tennis game method based on function fitting according to any one of claims 1-7 is realized.
CN202310659934.5A 2023-06-05 2023-06-05 Motion sensing table tennis game method based on function fitting Pending CN116637360A (en)

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