CN105759952A - Method and device for generating input information according to postures of four limbs - Google Patents
Method and device for generating input information according to postures of four limbs Download PDFInfo
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- 210000003414 extremity Anatomy 0.000 claims abstract description 108
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- 210000003423 ankle Anatomy 0.000 claims abstract description 9
- 210000000707 wrist Anatomy 0.000 claims abstract description 9
- 230000033001 locomotion Effects 0.000 claims description 46
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
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Abstract
The invention discloses a method for generating input information according to postures of four limbs. The method comprises the following steps of in the process that a player makes a specific action, recording data about the postures of the four limbs, which are obtained by action sensors arranged at wrists and ankles of the player; performing multidimensional processing on the recorded data about the postures of the four limbs to generate a posture matching library for recording the corresponding relationship between the specific actions and the data about the postures of the four limbs; in the game process, acquiring the data about the postures of the four limbs of the player, and adding the acquired data about the postures of the four limbs into a matching queue by the action sensors; matching the data about the postures of the four limbs at the head of the matching queue with the data about the postures of the four limbs in the posture matching library; if the matching is succeeded, enabling a game role to finish corresponding actions of the data about the postures of the four limbs, which are successfully matched, and removing the data about the postures of the four limbs, which are successfully matched, from the queue.
Description
Technical Field
The present application relates to interaction techniques between a user and a computer, and more particularly toMethod and device for generating input information according to postures of limbs。
Background
As games continue to become more and more popular, the interest of the games has become higher and the requirements of players on the game experience have become higher and higher. Players no longer satisfy the requirement of using a single mouse and keyboard to control game characters to obtain visual and auditory feelings, and hope to have more real game experience, and the game has more substituted feeling and an immersive feeling. The motion capture-based game input system can enable a player to control game characters through body motions, and more real game experience is obtained.
In the prior art, there is an optical tracking-based motion capture system, which is a whole body motion capture system, and the system includes a motion capture special clothes, a motion capture marker point, a motion capture camera and motion capture software. The system captures optical motion capture mark points on a player wearing the motion capture special clothes through a motion capture camera to obtain mark point position data, transmits the mark point position data into a computer, analyzes the mark point position data into motion data of the player through motion capture software, and further uses the analyzed motion data to control game characters of the player in a game.
The motion capture system has the following disadvantages: the installation of motion capture cameras is more complicated, needs to install a plurality of motion capture cameras, needs certain space to arrange these cameras to the use of motion capture software is also very complicated, needs to carry out a large amount of personnel training, and in the capture process of action, can't operate entire system alone, must be accomplished by the cooperation jointly of a plurality of operating personnel, is unfavorable for the player to play at any time. And the whole set of system is expensive, is generally used for game making and 3D animation making, has a price exceeding the bearing range of common players, cannot be put into use in a large scale, and cannot be popularized.
Disclosure of Invention
This application providesMethod and device for generating input information according to postures of limbsThe input information can be generated according to the actions of the four limbs of the human body at relatively low cost.
The method for generating the input information according to the postures of the limbs comprises the following steps:
A. recording the four-limb posture data obtained by the action sensors arranged at the wrists and the ankles of the player in the process of making the player perform the specified action; performing multidimensional processing on the recorded limb posture data to generate a posture matching library for recording the corresponding relation between the designated action and the limb posture data;
B. in the game process, the motion sensor collects the four-limb posture data of the player and adds the collected four-limb posture data into a matching queue;
C. and matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library, if the matching is successful, enabling the game role to complete the corresponding action of the successfully matched limb posture data, and removing the successfully matched limb posture data from the matching queue.
Optionally, the motion sensor is a three-axis gyroscope and accelerometer.
Optionally, the step of recording the data of the postures of the limbs obtained by the motion sensors installed at the wrists and the ankles of the player further comprises: and eliminating the drift of the gyroscope in measurement through Kalman filtering.
Optionally, the multidimensional processing on the recorded limb posture data includes: and processing the collected limb posture data by applying a rotation matrix.
Optionally, the matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library includes: performing correlation coefficient operation on the limb posture data at the head of the matching queue and the limb posture data in the posture matching library to find out the action corresponding to the maximum correlation coefficient; and judging whether the maximum correlation coefficient is larger than a preset threshold value, if so, successfully matching, and otherwise, unsuccessfully matching.
The embodiment of the present application further provides a device for generating input information according to the posture of the limbs, including:
the motion sensor module comprises four motion sensor devices which are respectively used for being fixed on the wrists or ankles of the players, collecting the posture data of the four limbs and sending the collected motion data to the receiving module;
a receiving module for receiving the four limbs posture data collected by the action sensor module,
the database module is used for recording the limb posture data received by the receiving module in the process that the player makes the appointed action; performing multidimensional processing on the recorded limb posture data to generate a posture matching library for recording the corresponding relation between the designated action and the limb posture data;
the matching module is used for adding the limb posture data received by the receiving module into a matching queue in the game process; matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library, if the matching is successful, informing an input module, and removing the successfully matched limb posture data from the matching queue;
and the input module is used for generating input information so that the game role completes the corresponding action of the successfully matched limb posture data.
Optionally, the motion sensor is a three-axis gyroscope and accelerometer.
Optionally, the motion sensor module further comprises: and the filtering unit is used for eliminating the drift of the gyroscope during measurement through Kalman filtering.
Optionally, the database module comprises: and the rotation processing unit is used for processing the acquired limb posture data by applying the rotation matrix.
Optionally, the matching module comprises:
the correlation coefficient calculation unit is used for carrying out correlation coefficient operation on the limb posture data at the head of the matching queue and the limb posture data in the posture matching library to find out the action corresponding to the maximum correlation coefficient;
the judging unit is used for judging whether the maximum correlation coefficient is larger than a preset threshold value or not, if so, the matching is successful, and an input module is informed; otherwise the matching is unsuccessful.
According to the technical scheme, the sensors are arranged on the four limbs of the player, the four limb posture data of the designated action of the player is collected firstly, and the data is stored in the matching library. In the game, the current four-limb posture data of the player is read and then matched with the data in the matching library, and corresponding input information is generated according to the matching result, so that the game role is operated. In the game experience, actions which can be taken by the player character are limited and do not need to be distinguished, so that the scheme of the application can meet the requirement of operating the game according to body actions and provide vivid game experience for the player.
Drawings
FIG. 1 shows a schematic view of aFlow schematic of method for generating input information according to limb postures provided by the embodiment of the applicationDrawing (A)；
FIG. 2Flow schematic for generating input information after entering game provided by embodiment of applicationDrawing (A)；
FIG. 3The embodiment of the application also provides a device schematic for generating input information according to the posture of the limbsDrawing (A)。
Detailed Description
In order to make the technical principle, characteristics and technical effects of the technical scheme of the present application clearer, the technical scheme of the present application is explained in detail with reference to specific embodiments below.
The method and the process for generating input information according to the posture of the limbs provided by the embodiment of the applicationAs shown in figure 1The method comprises the following steps:
step 101: a motion sensor is mounted at the wrist and ankle of the player.
Optionally, the motion sensor is a three-axis gyroscope and accelerometer.
Step 102: and indicating the player to finish the specified action, and recording the four-limb posture data obtained by the action sensor in the process of the player making the specified action.
For example, a novice checkpoint may be designed to guide a player to perform a designated action, such as squat, standing, running, jumping, walking, turning, waving, kicking, etc., to collect data from motion sensors mounted on the limbs of the player during the actions, to eliminate drift during gyroscope measurement by kalman filtering, and to record the limb posture data of the corresponding action.
The following describes how to record limb posture data by a specific embodiment.
A system incorporating a discrete control process, the system being described by a linear random differential equation:
X(k)＝AX(k-1)+BU(k)+W(k)
plus the system measurements:
Z(k)＝HX(k)+V(k)
in the above two equations, x (k) is the system state at time k, and u (k) is the control amount of the system at time k. A and B are system parameters, and for a multi-model system, A and B are matrices. Z (k) is the measured value at time k, H is a parameter of the measurement system, and H is a matrix for a multi-measurement system. W (k) and v (k) represent process and measurement noise, respectively. They are assumed to be white gaussian noise and their correlation coefficients are Q, R, respectively (here we assume that they do not change with system state changes).
The kalman filter is the optimal information processor for satisfying the above conditions (linear random differential system, process and measurement are both gaussian white noise). The optimized output of the system is evaluated in combination with their correlation coefficients.
First, a process model of the system is used to predict the system for the next state. Assuming that the present system state is k, according to the model of the system, the present state can be predicted based on the last state of the system:
X(k|k-1)＝AX(k-1|k-1)+BU(k)………..(1)
in the formula (1), X (k | k-1) is the result predicted by the previous state, X (k-1| k-1) is the optimum result of the previous state, and U (k) is the control amount of the current state, and if there is no control amount, it may be 0.
The system results have been updated so far, however, the correlation coefficient corresponding to X (k | k-1) has not been updated. The correlation coefficient is denoted by P:
P(k|k-1)＝AP(k-1|k-1)A’+Q………(2)
in the formula (2), P (k | k-1) is a correlation coefficient corresponding to X (k | k-1), P (k-1| k-1) is a correlation coefficient corresponding to X (k-1| k-1), A' represents a transposed matrix of A, and Q is a correlation coefficient of the system process. Equations 1 and 2 are the first two of 5 equations in the kalman filter, i.e., the prediction of the system.
We now have a prediction of the current state and we then collect the measurements of the current state. Combining the predicted values and the measured values, we can obtain an optimized estimated value X (k | k) of the current state (k):
X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1))………(3)
where Kg is the Kalman gain:
Kg(k)＝P(k|k-1)H’/(HP(k|k-1)H’+R)………(4)
so far we have obtained the optimal estimate X (k | k) in the k state. However, in order to make the kalman filter continuously operate until the system process is finished, we also update the correlation coefficient of X (k | k) in the k state:
P(k|k)＝(I-Kg(k)H)P(k|k-1)………(5)
where I is a matrix of 1, I ═ 1 for single model single measurements. When the system enters the k +1 state, P (k | k) is P (k-1| k-1) of equation (2). Thus, the algorithm can proceed with autoregressive operation.
The principle of the kalman filter basically describes that equations 1, 2, 3, 4 and 5 are his 5 basic formulas. This 5 formula can be easily implemented by computer programming.
The single chip timer sets the sampling interval dt to be 0.01s, and the Kalman filtering initial parameters are as follows:
step 103: and performing multidimensional processing on the recorded limb posture data to generate a posture matching library for recording the corresponding relation between the specified action and the limb posture data.
Optionally, the step may specifically include: and processing the collected limb posture data by applying a rotation matrix, carrying out transformation of various angles on the collected limb posture data by modifying angle values, and storing the transformed limb posture data serving as action data corresponding to the specified action in a posture matching library.
The processing of the acquired data using the rotation matrix is represented as follows:
wherein,in order to provide the attitude data before transformation,for the transformed pose data, R is a rotation matrix. Rx(θ)、Ry(θ)、Rz(θ) are the projections of the rotation matrix on the x, y and z axes, respectively.
The acquired data can be transformed in various angles by modifying the angle values.
Step 104: in the game process, the motion sensor collects the four-limb posture data of the player, and the collected four-limb posture data is added into the matching queue.
Step 105: and matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library, if the matching is successful, enabling the game role to complete the action corresponding to the successfully matched limb posture data, and removing the successfully matched limb posture data from the matching queue.
And then returns to step 104 for data acquisition and matching of the next action until the game is finished.
Please refer toFIG. 2In this embodiment, the process of generating the input information after entering the game includes:
step 201: starting a game program, and after a player character enters a game, judging whether a posture matching library corresponding to the player character is generated, if so, executing step 206, otherwise, executing step 202.
Step 202: and playing a novice guide, designating a player to make a specific action at a specific time point, and acquiring the four-limb posture data of the player by using a motion sensor.
Step 203: and storing the limb posture data collected at the preset time point.
Step 204: and performing multidimensional processing on the collected limb posture data, and storing the corresponding relation between the processing result and the action in a posture matching library.
Step 205: and judging whether the novice guidance is finished, if so, returning to the step 201, and otherwise, returning to the step 202.
Step 206: the motion sensor collects data of four limbs of the player, and the collected posture data of the four limbs are added into the matching queue.
Step 207: and matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library.
In this embodiment, the matching method may be: and performing correlation coefficient operation on the limb posture data at the head of the matching queue and the limb posture data in the posture matching library to find out the action corresponding to the maximum correlation coefficient. And judging whether the maximum correlation coefficient is larger than a preset threshold value, if so, successfully matching, and otherwise, unsuccessfully matching.
Step 208: and judging whether the matching is successful, if so, executing step 209, otherwise, executing step 210.
Step 209: and controlling the game role to perform the action corresponding to the maximum correlation coefficient.
Step 210: and judging whether the game is finished, if so, finishing the process, and otherwise, returning to the step 206.
Embodiments of the present application also provide an apparatus for generating input information according to postures of limbs,as shown in fig. 3As shown, the apparatus includes:
the motion sensor module 301 comprises four motion sensor devices which are respectively used for being fixed on the wrists or ankles of the players, collecting the posture data of the four limbs and sending the collected motion data to the receiving module 302;
a receiving module 302, configured to receive the limb posture data collected by the motion sensor module,
the database module 303 is used for recording the limb posture data received by the receiving module in the process that the player makes the designated action; performing multidimensional processing on the recorded limb posture data to generate a posture matching library for recording the corresponding relation between the designated action and the limb posture data;
the matching module 304 is used for adding the limb posture data received by the receiving module into a matching queue in the game process; matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library, if the matching is successful, informing an input module, and removing the successfully matched limb posture data from the matching queue;
the input module 305 is configured to generate input information, so that the game character completes a corresponding action of the successfully matched limb posture data.
Optionally, the motion sensor is a three-axis gyroscope and accelerometer.
Optionally, the motion sensor module 301 further comprises: and the filtering unit is used for eliminating the drift of the gyroscope during measurement through Kalman filtering.
Optionally, the database module 303 includes: and the rotation processing unit is used for processing the collected limb posture data by applying a rotation matrix and carrying out transformation of various angles on the collected limb posture data by modifying angle values.
Optionally, the matching module 304 includes:
the correlation coefficient calculation unit is used for carrying out correlation coefficient operation on the limb posture data at the head of the matching queue and the limb posture data in the posture matching library to find out the action corresponding to the maximum correlation coefficient;
the judging unit is used for judging whether the maximum correlation coefficient is larger than a preset threshold value or not, if so, the matching is successful, and an input module is informed; otherwise the matching is unsuccessful.
It should be understood that while the specification has been described in terms of various embodiments, not every embodiment includes only oneIndependent of each otherThe description of the embodiments is for clarity only, and those skilled in the art should be able to combine the embodiments as a whole to form other embodiments as would be understood by those skilled in the art.
The above description is only a preferred embodiment of the present application and should not be taken as limiting the scope of the present application, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the technical solution of the present application should be included in the scope of the present application.
Claims (10)
1. A method of generating input information from limb gestures, comprising:
A. recording the four-limb posture data obtained by the action sensors arranged at the wrists and the ankles of the player in the process of making the player perform the specified action; performing multidimensional processing on the recorded limb posture data to generate a posture matching library for recording the corresponding relation between the designated action and the limb posture data;
B. in the game process, the motion sensor collects the four-limb posture data of the player and adds the collected four-limb posture data into a matching queue;
C. and matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library, if the matching is successful, enabling the game role to complete the corresponding action of the successfully matched limb posture data, and removing the successfully matched limb posture data from the matching queue.
2. The method of claim 1, wherein the motion sensor is a three-axis gyroscope and accelerometer.
3. The method of claim 2, wherein the step of recording limb posture data from motion sensors mounted on the player's wrists and ankles further comprises: and eliminating the drift of the gyroscope in measurement through Kalman filtering.
4. The method of claim 1, wherein the multi-dimensionally processing the recorded extremity pose data comprises: and processing the collected limb posture data by applying a rotation matrix.
5. The method of claim 1, wherein matching the extremity pose data at the head of the matching queue with extremity pose data in a pose matching library comprises: performing correlation coefficient operation on the limb posture data at the head of the matching queue and the limb posture data in the posture matching library to find out the action corresponding to the maximum correlation coefficient; and judging whether the maximum correlation coefficient is larger than a preset threshold value, if so, successfully matching, and otherwise, unsuccessfully matching.
6. An apparatus for generating input information from a posture of a limb, comprising:
the motion sensor module comprises four motion sensor devices which are respectively used for being fixed on the wrists or ankles of the players, collecting the posture data of the four limbs and sending the collected motion data to the receiving module;
a receiving module for receiving the four limbs posture data collected by the action sensor module,
the database module is used for recording the limb posture data received by the receiving module in the process that the player makes the appointed action; performing multidimensional processing on the recorded limb posture data to generate a posture matching library for recording the corresponding relation between the designated action and the limb posture data;
the matching module is used for adding the limb posture data received by the receiving module into a matching queue in the game process; matching the limb posture data at the head of the matching queue with the limb posture data in the posture matching library, if the matching is successful, informing an input module, and removing the successfully matched limb posture data from the matching queue;
and the input module is used for generating input information so that the game role completes the corresponding action of the successfully matched limb posture data.
7. The apparatus of claim 6, wherein the motion sensor is a three-axis gyroscope and accelerometer.
8. The apparatus of claim 7, wherein the motion sensor module further comprises: and the filtering unit is used for eliminating the drift of the gyroscope during measurement through Kalman filtering.
9. The apparatus of claim 6, wherein the database module comprises: and the rotation processing unit is used for processing the acquired limb posture data by applying the rotation matrix.
10. The apparatus of claim 6, wherein the matching module comprises:
the correlation coefficient calculation unit is used for carrying out correlation coefficient operation on the limb posture data at the head of the matching queue and the limb posture data in the posture matching library to find out the action corresponding to the maximum correlation coefficient;
the judging unit is used for judging whether the maximum correlation coefficient is larger than a preset threshold value or not, if so, the matching is successful, and an input module is informed; otherwise the matching is unsuccessful.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101479680A (en) * | 2006-05-05 | 2009-07-08 | 埃森哲环球服务有限公司 | Action recognition and interpretation using a precision positioning system |
| CN101694693A (en) * | 2009-10-16 | 2010-04-14 | 中国科学院合肥物质科学研究院 | Human body movement recognition system based on acceleration sensor and method |
| CN101706873A (en) * | 2009-11-27 | 2010-05-12 | 东软集团股份有限公司 | Identification method and device of digital-class limitation marking |
| CN102023700A (en) * | 2009-09-23 | 2011-04-20 | 吴健康 | Three-dimensional man-machine interactive system |
| US20130018494A1 (en) * | 2011-07-14 | 2013-01-17 | Alexander Andre Amini | System and method for motion analysis and feedback with ongoing dynamic training orientation determination |
| CN103488291A (en) * | 2013-09-09 | 2014-01-01 | 北京诺亦腾科技有限公司 | Immersion virtual reality system based on motion capture |
-
2015
- 2015-12-28 CN CN201510998896.1A patent/CN105759952A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101479680A (en) * | 2006-05-05 | 2009-07-08 | 埃森哲环球服务有限公司 | Action recognition and interpretation using a precision positioning system |
| CN102023700A (en) * | 2009-09-23 | 2011-04-20 | 吴健康 | Three-dimensional man-machine interactive system |
| CN101694693A (en) * | 2009-10-16 | 2010-04-14 | 中国科学院合肥物质科学研究院 | Human body movement recognition system based on acceleration sensor and method |
| CN101706873A (en) * | 2009-11-27 | 2010-05-12 | 东软集团股份有限公司 | Identification method and device of digital-class limitation marking |
| US20130018494A1 (en) * | 2011-07-14 | 2013-01-17 | Alexander Andre Amini | System and method for motion analysis and feedback with ongoing dynamic training orientation determination |
| CN103488291A (en) * | 2013-09-09 | 2014-01-01 | 北京诺亦腾科技有限公司 | Immersion virtual reality system based on motion capture |
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