WO2018030734A1

WO2018030734A1 - 3d simulation method and apparatus

Info

Publication number: WO2018030734A1
Application number: PCT/KR2017/008506
Authority: WO
Inventors: 정주호; 정창근; 유성재
Original assignee: 주식회사 비플렉스
Priority date: 2016-08-09
Filing date: 2017-08-07
Publication date: 2018-02-15
Also published as: WO2018030743A1; CN109310913A; CN109414608B; CN109310913B; JP6765505B2; CN109414608A; JP2019531772A

Abstract

Disclosed are a 3D simulation apparatus and a method for same, the 3D simulation apparatus comprising: an parameter input unit for receiving input of model parameters, posture parameters, motion space parameters, and motion time parameters, based on external input; a 3D model-generating unit for generating a 3D model of a user based on the model parameters and the posture parameters; a motion track-generating unit for generating a motion track of the user based on the motion space parameters and the motion time parameters; and a 3D simulation generating-unit for generating a 3D simulation of the user by applying the motion track to the 3D model.

Description

3D simulation method and device

The present invention relates to a 3D simulation method and apparatus, and more particularly, to a method and apparatus for simulating a walking and driving motion of a user in three dimensions.

In general, it has been pointed out that the amount of exercise in daily life of modern people is quite insufficient to maintain proper physical health. Accordingly, there is a growing interest in systematic exercise methods that effectively promote health. Specifically, exercises such as exercises to quickly and systematically train the body, correction of postures to promote health in the long term, and exercises suitable for elderly people whose physical ability has deteriorated with a long lifespan Interest in sports is increasing. As one of the exercise methods that meet such various needs, there is a walking and driving exercise that can be easily performed by anyone.

Anyone who has no physical problems can walk and drive, and most people walk and drive in unconsciously accustomed posture. However, since the human body is not perfectly symmetrical, in most cases, walking and driving are performed in an unbalanced and incorrect posture. Continued walking and driving in the wrong posture may skew muscles and skeletons, and may also cause various systemic pains. In general, such a poor walking and driving posture causes a problem of deteriorating physical health, especially in the case of a growing child or an elderly person whose physical ability is deteriorated, body distortion and deterioration of health are more severe. On the other hand, professionals such as athletes and dancers need more physical skills than the general public, there is a problem such as generating a limit to improve the physical ability.

As such, correct walking and driving posture is important from the general public to professionals. Therefore, various studies have been conducted on how to recognize the walking and driving posture and how to perform the correction effectively. As such, there is an increasing demand for a technology for recognizing, detecting, and analyzing walking and driving in order to correct walking and driving posture.

The present invention has been made in view of the above-described technical problem, the object of the present invention is to substantially compensate for the various problems caused by the limitations and disadvantages in the prior art, the walking and driving movement of the user 3 The present invention provides a method and apparatus for simulating dimensions, and a computer readable recording medium having recorded thereon a program for executing the method.

According to an embodiment of the present invention, the 3D simulation method comprises: receiving a model parameter, a pose parameter, a motion space parameter, and a motion time parameter based on an external input; Generating a 3D model of a user based on the model parameter and the pose parameter; Generating a motion trajectory of the user based on the motion space parameter and the motion time parameter; And generating the 3D simulation of the user by applying the motion trajectory to the 3D model.

According to an embodiment of the present invention, the external input may be input from a user or may be input by recognizing a user's exercise from an exercise recognition device.

According to an embodiment of the present invention, the model parameter is a parameter related to the appearance of the user; The posture parameter is a parameter relating to the posture of the user; The motion space parameter is a parameter relating to a spatial trajectory of the user's motion; The motion time parameter is a parameter related to a time trajectory of the motion of the user.

According to an embodiment of the present invention, the model parameter includes at least one of height, weight, foot length, leg length, age, gender, and wearing information.

According to an embodiment of the present invention, the pose parameter includes at least one of interpolation, interpolation angle, and vertical viewing angle.

According to an embodiment of the present invention, the motion space parameter may include a vertical section of the support section, a vertical section of the support section, a vertical section of the vertical section, the maximum vertical force load ratio, the average vertical force load ratio, the impact amount, the left and right uniformity, the left and right balance, the stride, the landing feet, , Left and right pelvis angle and at least one of the left and right eye angle.

According to an embodiment of the present invention, the motion time parameter includes at least one of one foot support time, one foot suspension time, and one minute repair.

According to an embodiment of the present invention, the motion space parameter may include at least one of up and down oscillation width, up and down oscillation width, left and right uniformity, left and right balance, stride length, landing foot, upper and lower pelvis angle, left and right pelvis angle and left and right eye angle. Include.

According to an embodiment of the present invention, the motion time parameter includes at least one of one foot support time, two feet support time, and one minute repair.

According to an embodiment of the present invention, the generating of the motion trajectory of the user may include: motion motion data modeling a predetermined motion motion; And basic motion data independent of the motion space parameter and the motion time parameter.

According to an embodiment of the present invention, when the predetermined exercise motion is driving, the motion motion data and the basic motion data are four-step data including a left foot support section, a left foot float section, a right foot support section, and a right foot float section. .

According to an embodiment of the present invention, when the predetermined exercise motion is walking, the motion motion data and the basic motion data are four-step data including a left foot support section, a biped support section, a right foot support section, and a biped support section.

According to an embodiment of the present invention, the four-step data includes motion trajectory values of the up, down, left, and right axes of the joints.

According to one embodiment of the invention said joint is at least one of the neck, shoulder, waist, knee, arm, elbow, ankle and toe.

According to an embodiment of the present invention, the generating of the motion trajectory of the user may include: generating a first adjustment value by adjusting a gain value based on the motion space parameter to reflect the motion motion data; And generating a second adjustment value by adjusting a gain value based on the motion time parameter to reflect the first adjustment value.

According to an embodiment of the present invention, the generating of the motion trajectory of the user may include: generating a first adjustment value by adjusting a gain value based on the motion time parameter by reflecting the motion motion data; And generating a second adjustment value by reflecting and adjusting the gain value based on the motion space parameter to the first adjustment value.

According to an embodiment of the present invention, generating the motion trajectory of the user further includes merging the basic motion data and the second adjustment value.

In addition, according to an embodiment of the present invention includes a computer-readable recording medium on which a program for performing the method is recorded.

According to an embodiment of the present invention, the 3D simulation apparatus includes a parameter input unit for receiving a model parameter, a posture parameter, a motion space parameter, and a motion time parameter based on an external input; A 3D model generator for generating a 3D model of the user based on the model parameter and the pose parameter; A motion trajectory generation unit generating a motion trajectory of the user based on the motion space parameter and the motion time parameter; And a 3D simulation generator configured to generate the 3D simulation of the user by applying the motion trajectory to the 3D model.

According to the present invention, a 3D model and a motion trajectory are generated on the basis of a model parameter, a posture parameter, a motion space parameter, and a motion time parameter input from an exercise recognition device or a user to provide a 3D simulation about an exercise posture, and the like. The user can recognize, detect and analyze the state of exercise effectively and accurately. Therefore, through the 3D simulation according to the present invention, the user can effectively and accurately recognize his or her motion and walking state, and can be used to correct his or her posture through 3D simulation analysis.

1 illustrates a 3D simulation result according to an embodiment of the present invention.

2 is a block diagram of a 3D simulation apparatus according to an embodiment of the present invention.

3 illustrates a 3D model of a user reflecting model parameters according to an embodiment of the present invention.

4 illustrates a 3D model of a user reflecting a posture parameter according to an embodiment of the present invention.

5 illustrates a four-step movement motion when the movement motion according to an embodiment of the present invention is driving.

6 illustrates a four-step motion motion when the motion motion is walking according to an embodiment of the present invention.

7 is a block diagram of a motion trajectory generation unit according to an embodiment of the present invention.

8 is an exemplary view of adjusting and adjusting a motion space parameter in the motion motion data according to an embodiment of the present invention.

9 is an exemplary view of adjusting and reflecting a motion time parameter in exercise motion data according to an embodiment of the present invention.

10 is a flowchart of a 3D simulation method according to an embodiment of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Like reference numerals in the drawings denote like elements, and the size of each element in the drawings may be exaggerated for clarity.

According to the present embodiment, the 3D simulation apparatus 200 receives a model parameter, a posture parameter, a motion space parameter, and a motion time parameter based on an external input, and generates a 3D model and a motion trajectory of a user who is exercising based on the input. This creates a 3D simulation. In the illustrated example, the model parameters such as the user's height, foot length and leg length, posture parameters such as interpolation and interpolation angle, motion space parameters such as stride length, left and right uniformity and left and right balance, and motion time parameters such as air suspension time Based on this, the 3D simulation apparatus 200 3D simulated walking and driving motions of the user.

The 3D simulation apparatus 200 includes a parameter input unit 210, a 3D model generator 230, a motion trajectory generator 250, and a 3D simulation generator 270.

The parameter input unit 210 receives the model parameter 211, the posture parameter 213, the motion space parameter 215, and the motion time parameter 217 based on the external input. The external input may be input from a user or may be input by recognizing a user's exercise from an exercise recognition device. For more information on the exercise recognition device, refer to Korean patent, 'A motion recognition method and device for pedestrian and driving monitoring' (application number: 10-2016-0101489, application date: 2016.08.09), 'pressure center path Method and Apparatus for Deriving Based Exercise Posture '(Application No .: 10-2016-0101491, Filed Date: 2016.08.09),' Method and Apparatus for Recognizing Exercise for Walking and Driving Monitoring '(Application No .: 10-2017-0030394 No. 10, 2017.03.10), 'Method and Apparatus for Deriving Pressure-Based Path-Based Exercise Posture' (Application No .: 10-2017-0030402, Filing Date: 2017.03.10) and 'Method and Apparatus for Quantifying Risk of Injury in Driving' (Application No. 10-2017-0079255, Application Date: June 22, 2017), which application patent is incorporated herein by reference.

The model parameter 211 is a parameter related to the user's appearance, and includes height, weight, foot length, leg length, age, gender, and wearing information. At least one of the. The wear information includes the type, name and brand of the product worn by the user. Products worn by the user include accessories such as watches, clothes, shoes, and the like.

The posture parameter 213 is a parameter related to a posture of a user and includes at least one of interpolation (Step width), interpolation (Step angle), and head vertical angle. Interpolation is the average value between the legs, and interpolation is the leg angle average value. Up-and-down eye angle means an average value of the vertical angle of the head.

The motion space parameter 215 is a parameter related to the trajectory of the user's motion. When the user moves, the motion space parameter 215 includes vertical oscillation during stance, vertical oscillation during flight, Instantaneous Vertical Loading Rate (IVLR), Average Vertical Loading Rate (AVLR), Impact, Impact, Stability, Balance, Step Length, At least one of a foot strike pattern, a pelvic vertical rotation, a pelvic lateral rotation, and a head lateral angle. The motion space parameter 215 may include vertical oscillation during single stance, vertical oscillation during double stance, left and right uniformity, left and right balance, stride length, landing foot, and the like when the user walks. At least one of the upper and lower pelvis angle, left and right pelvis angle and left and right eye angle.

The vertical vibration width of the support section means the vertical movement distance (meter) in the support section, and the vertical vibration width of the floating section means the vertical movement distance in the floating section. The maximum vertical force load rate is the instantaneous vertical force load rate (Newton / second), which means the maximum slope of the support section of the ground reaction force. Mean vertical force load ratio (Newton / second) means the average slope of the support section of ground reaction force. Impact amount refers to the impact force (Newton) of the support section of the ground reaction force.

Stability refers to whether the state of movement is consistently maintained for each leg of the left and right foot in time, strength, etc., and expressed in% using the coefficient of variation (CV) of each leg. Obtain through

Stability (Left) = 1-std (Left indices) / mean (Left indices)

Stability (Right) = 1-std (Right indices) / mean (Right indices)

Values that can be used as index for evaluation index include maximum vertical force, vertical acceleration maximum, support section impact, support time, stray time, average vertical force load rate and maximum vertical force load rate.

Balance of left and right (Balance) represents the left and right unbalance (%), and is obtained by the following equation.

Balance = Left index / (Left index + Right index) * 100%

Step length refers to the distance traveled forward during the support section and the floating section, and the foot strike pattern indicates which foot to land on. For example, the landing foot may be one of a fore foot, a rear foot, and a mid foot. The pelvic vertical rotation and the pelvic lateral rotation mean the degree of vertical and horizontal distortion of the pelvis, respectively. Head lateral angle is an average value of the left and right angles of the head.

The motion time parameter 217 is a parameter related to the time trajectory of the user's motion. When the user moves, the motion time parameter 217 includes at least one of a single stance time, a single flight time, and a cadence per minute. It includes one. The motion time parameter 217 includes at least one of a single stance time, a double stance time, and a minute's reward when the user walks.

The 3D model generator 230 generates a 3D model of the user based on the model parameter 211 and the attitude parameter 213.

The motion trajectory generation unit 250 generates a motion trajectory of the user based on the motion space parameter 215 and the motion time parameter 217. The detailed operation of the motion trajectory generation unit 250 will be described later in detail with reference to FIGS. 5 to 9.

The 3D simulation generator 270 generates the 3D simulation of the user by applying the motion trajectory to the 3D model.

In the illustrated example, the 3D model is generated by reflecting the height of the user among the model parameters 211.

In the illustrated example, the 3D model is generated by reflecting the interpolation and the interpolation angle of the user among the posture parameters 213.

In the case of the driving movement, the movement movement is repeatedly performed in four stages including the left foot support section, the left foot float section, the right foot support section and the right foot float section.

In the case of a walking movement, the movement movement is repeatedly performed in four stages including a left foot support section, a biped support section, a right foot support section, and a foot support section.

The motion trajectory generation unit 700 includes the motion motion data 720 and the basic motion data 730. The motion motion data 720 is pre-stored data by modeling a predetermined motion motion, and the basic motion data 730 is data previously stored as motion data independent of the motion space parameter 715 and the motion time parameter 717. For example, the basic motion data 730 may be data about a tumbling motion such as an arm motion or an upper body distortion that has little change in movement.

When the predetermined movement motion is driving, the movement motion data 720 and the basic motion data 730 are four-step data including a left foot support section, a left foot float section, a right foot support section, and a right foot float section. When the predetermined motion is walking, the motion motion data 720 and the basic motion data 730 are four-step data including a left foot support section, a biped support section, a right foot support section, and a biped support section. In the running or walking movement, the above four steps are repeatedly reproduced. Each step includes three-axis motion trajectory values of the vertical axis (z axis), the left and right axis (y axis), and the front and rear axis (x axis) for each joint. The joint is at least one of a neck, shoulder, waist, knee, arm, elbow, ankle and toe.

The motion trajectory generation unit 700 further includes a motion space parameter adjusting unit 750 and a motion time parameter adjusting unit 770.

The motion space parameter adjusting unit 750 generates the first adjustment value by reflecting and adjusting the gain value based on the motion space parameter 715 in the motion motion data 720. For example, when the value of 'floating section up / down vibration', which is a motion space parameter, is C, and the maximum possible value of the corresponding value is C _max and the minimum possible value is C _min , the gain value by 'floating section up / down vibration width' value Is (C-C _min ) / (C _max -C _min ) can be set. Therefore, the gain value has a range of 0 <= gain value <= 1. Although the gain value setting has been exemplified above, it is apparent to those skilled in the art that the gain value can be set by other methods. The motion time parameter adjusting unit 770 generates a second adjustment value by reflecting and adjusting the gain value based on the motion time parameter 717 to the first adjustment value.

Meanwhile, according to another exemplary embodiment of the present invention, the motion time parameter adjusting unit 770 generates the first adjustment value by reflecting and adjusting the gain value based on the motion time parameter 717 in the motion motion data 720. The motion space parameter adjusting unit 750 generates a second adjustment value by reflecting and adjusting the gain value based on the motion space parameter 715 to the first adjustment value.

The motion trajectory generation unit 700 further includes a motion trajectory merger 790. The motion trajectory merging unit 790 merges the basic motion data and the second adjustment value to generate a motion trajectory of the user.

In the example shown, the typical waist triaxial motion trajectory value stored in the athletic motion data 720 is shown in solid blue for the driving motion. The motion space parameter adjusting unit 750 generates a first adjustment value by reflecting and adjusting the gain value based on the motion space parameter 715 to the 3-axis motion trajectory value of the waist. In the illustrated example, it can be seen that the first adjustment value of the z-axis among the 3-axis motion trajectory values of the waist is reduced in amplitude compared to the general waist 3-axis motion trajectory value stored in the motion motion data 720.

FIG. 9 is an exemplary view of adjusting and adjusting motion time parameters in exercise motion data according to another exemplary embodiment of the present invention.

In the example shown, the typical waist triaxial motion trajectory value stored in the athletic motion data 720 is shown in solid blue for the driving motion. The motion time parameter adjustment unit adjusts the gain value based on the motion time parameter to reflect the 3-axis motion trajectory value of the waist, thereby generating a first adjustment value. In the illustrated example, it can be seen that the first adjustment value of the z-axis among the three-axis motion trajectory values of the waist is reduced in the right foot support time compared to the general waist three-axis motion trajectory values stored in the motion motion data.

In operation 1010, the 3D simulation apparatus 200 according to an embodiment of the present invention receives a model parameter, a pose parameter, a motion space parameter, and a motion time parameter based on an external input. The external input may be input from a user or may be input by recognizing a user's exercise from an exercise recognition device.

In operation 1020, the 3D simulation apparatus 200 generates a 3D model of the user based on the model parameter and the pose parameter.

In operation 1030, the 3D simulation apparatus 200 according to an embodiment of the present invention generates a motion trajectory of the user based on the motion space parameter and the motion time parameter. The generating of the motion trajectory of the user uses motion motion data modeling a predetermined motion motion and basic motion data independent of the motion space parameter and the motion time parameter.

When the predetermined exercise motion is driving, the motion motion data and the basic motion data are four-step data including a left foot support section, a left foot float section, a right foot support section, and a right foot float section. When the predetermined exercise motion is walking, the motion motion data and the basic motion data are four-step data including a left foot support section, a biped support section, a right foot support section, and a biped support section. The four-step data includes the motion trajectory values of the up, down, left and right axes, and front and rear axes for each joint. The joint is at least one of a neck, shoulder, waist, knee, arm, elbow, ankle and toe.

The generating of the motion trajectory of the user may be performed by reflecting and adjusting the gain value based on the motion space parameter in the motion motion data, thereby generating a first adjustment value and adjusting the gain value based on the motion time parameter. The second adjustment value is generated by adjusting to reflect the value.

According to another exemplary embodiment of the present disclosure, the generating of the motion trajectory of the user may include adjusting the gain value based on the motion time parameter to reflect the motion motion data, thereby generating a first adjustment value and generating the motion space parameter. The second adjustment value is generated by adjusting the gain value based on the reflection on the first adjustment value.

The generating of the motion trajectory of the user according to an embodiment of the present invention further includes merging the basic motion data and the second adjustment value.

In operation 1040, the 3D simulation apparatus 200 according to an embodiment of the present invention generates the 3D simulation of the user by applying the motion trajectory to the 3D model.

Although the preferred embodiment of the present invention has been described in detail above, the scope of the present invention is not limited thereto, and various modifications and equivalent other embodiments are possible. Therefore, the true technical protection scope of the present invention will be defined by the appended claims.

For example, an apparatus according to an exemplary embodiment of the present invention may include a bus coupled to units of each of the apparatus as shown, at least one processor coupled to the bus, and a command, received And a memory coupled to the bus for storing messages or generated messages, and coupled to at least one processor for performing instructions as described above.

In addition, the system according to the present invention can be embodied as computer readable codes on a computer readable recording medium. The computer-readable recording medium includes all kinds of recording devices in which data that can be read by a computer system is stored. The computer-readable recording medium may include a magnetic storage medium (eg, ROM, floppy disk, hard disk, etc.) and an optical reading medium (eg, CD-ROM, DVD, etc.). The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

Claims

A parameter input unit configured to receive a model parameter, a posture parameter, a motion space parameter, and a motion time parameter based on an external input;

A 3D model generator for generating a 3D model of the user based on the model parameter and the pose parameter;

A motion trajectory generation unit generating a motion trajectory of the user based on the motion space parameter and the motion time parameter; And

And a 3D simulation generator which generates the 3D simulation of the user by applying the motion trajectory to the 3D model.
The method of claim 1,

The external input is an input from a user, or a 3D simulation apparatus, characterized in that the input by recognizing the movement of the user from the exercise recognition device.
The method of claim 1,

The model parameter is a parameter relating to the appearance of the user;

The posture parameter is a parameter relating to the posture of the user;

The motion space parameter is a parameter relating to a spatial trajectory of the user's motion;

The motion time parameter is a parameter relating to the time trajectory of the motion of the user.
The method of claim 1,

The model parameter includes at least one of height, weight, foot length, leg length, age, gender and wearing information.
The method of claim 1,

The pose parameter includes at least one of interpolation, interpolation angle, and vertical viewing angle.
The method of claim 1,

The motion space parameters include the up and down vibration width of the support section, the up and down vibration width of the floating section, the maximum vertical force load ratio, average vertical force load ratio, impact amount, left and right uniformity, left and right balance, stride length, landing foot, upper and lower pelvis angle, left and right pelvis angle and left and right viewing angle. 3D simulation apparatus comprising at least one of.
The method of claim 1,

The motion time parameter is a 3D simulation apparatus comprising at least one of a pair of support time, a stray floating time, and a minute per minute.
The method of claim 1,

The motion space parameter is a 3D simulation, characterized in that it comprises at least one of the upper and lower oscillation amplitude of one foot section, the upper and lower oscillation width of both legs, left and right uniformity, left and right balance, stride length, landing foot, upper and lower pelvis, left and right pelvis Device.
The method of claim 1,

The motion time parameter may include at least one of one foot support time, two feet support time, and one minute repair.
The method of claim 1,

The motion trajectory generation unit

Kinetic motion data modeling a predetermined kinetic motion; And

And basic motion data independent of the motion space parameter and the motion time parameter.
The method of claim 10,

And the motion motion data and the basic motion data are four-stage data including a left foot support section, a left foot float section, a right foot support section, and a right foot float section when the predetermined motion movement is a driving.
The method of claim 10,

And the motion motion data and the basic motion data are four-step data including a left foot support section, a two-foot support section, a right foot support section, and a two-foot support section when the predetermined movement motion is walking.
The method of claim 11 or 12,

The four-stage data includes a motion trajectory value of the vertical axis, the left and right axis, and the fore and aft axis of each joint.
The method of claim 13,

And the joint is at least one of a neck, shoulder, waist, knee, arm, elbow, ankle and toe.
The method of claim 10,

The motion trajectory generation unit

A motion space parameter adjustment unit generating a first adjustment value by reflecting and adjusting a gain value based on the motion space parameter in the kinetic motion data; And

And a motion time parameter adjusting unit configured to generate a second adjustment value by reflecting and adjusting the gain value based on the motion time parameter to the first adjustment value.
The method of claim 10,

The motion trajectory generation unit

A motion time parameter adjusting unit generating a first adjustment value by reflecting and adjusting a gain value based on the motion time parameter in the motion motion data; And

And a motion space parameter adjusting unit configured to generate a second adjustment value by reflecting and adjusting the gain value based on the motion space parameter in the first adjustment value.
The method according to claim 15 or 16,

The motion trajectory generation unit

And a motion trajectory merging unit for merging the basic motion data and the second adjustment value.
Receiving a model parameter, a pose parameter, a motion space parameter, and a motion time parameter based on an external input;

Generating a 3D model of a user based on the model parameter and the pose parameter;

Generating a motion trajectory of the user based on the motion space parameter and the motion time parameter; And

And generating a 3D simulation of the user by applying the motion trajectory to the 3D model.
The method of claim 18,

The external input is an input from a user, or 3D simulation method characterized in that the input by recognizing the movement of the user from the exercise recognition device.
The method of claim 18,

The model parameter is a parameter relating to the appearance of the user;

The posture parameter is a parameter relating to the posture of the user;

The motion space parameter is a parameter relating to a spatial trajectory of the user's motion;

The motion time parameter is a parameter relating to the time trajectory of the motion of the user.
The method of claim 18,

The model parameter comprises at least one of height, weight, foot length, leg length, age, gender and wearing information.
The method of claim 18,

The posture parameter includes at least one of interpolation, interpolation angle, and vertical viewing angle.
The method of claim 18,

The motion space parameters include the up and down vibration width of the support section, the up and down vibration width of the floating section, the maximum vertical force load ratio, average vertical force load ratio, impact amount, left and right uniformity, left and right balance, stride length, landing foot, upper and lower pelvis angle, left and right pelvis angle and left and right viewing angle. 3D simulation method comprising at least one of.
The method of claim 18,

The motion time parameter includes at least one of a pair of supporting time, a pair of floating time, and a repair per minute.
The method of claim 18,

The motion space parameter is a 3D simulation, characterized in that it comprises at least one of the upper and lower oscillation amplitude of one foot section, the upper and lower oscillation width of both legs, left and right uniformity, left and right balance, stride length, landing foot, upper and lower pelvis, left and right pelvis Way.
The method of claim 18,

The motion time parameter includes at least one of one foot support time, two feet support time, and a minute's repair.
The method of claim 18,

Generating the motion trajectory of the user

Kinetic motion data modeling a predetermined kinetic motion; And

3D simulation method using basic motion data independent of the motion space parameter and the motion time parameter.
The method of claim 27,

And the motion motion data and the basic motion data are four-stage data including a left foot support section, a left foot float section, a right foot support section, and a right foot float section when the predetermined exercise motion is driving.
The method of claim 27,

And the motion motion data and the basic motion data are four-step data including a left foot support section, a two-foot support section, a right foot support section, and a two-foot support section when the predetermined movement motion is walking.
The method of claim 28 or 29,

The four-stage data includes a motion trajectory value of the vertical axis, the left and right axis, and the fore and aft axis of each joint.
The method of claim 30,

Wherein said joint is at least one of a neck, shoulder, waist, knee, arm, elbow, ankle and toe.
The method of claim 27,

Generating the motion trajectory of the user

Generating a first adjustment value by reflecting and adjusting a gain value based on the motion space parameter in the kinetic motion data; And

And generating a second adjustment value by reflecting and adjusting the gain value based on the motion time parameter to the first adjustment value.
The method of claim 27,

Generating the motion trajectory of the user

Generating a first adjustment value by reflecting and adjusting a gain value based on the motion time parameter in the kinetic motion data; And

And generating a second adjustment value by reflecting and adjusting the gain value based on the motion space parameter to the first adjustment value.
34. The method of claim 32 or 33,

Generating the motion trajectory of the user

And merging the basic motion data and the second adjustment value.
A computer-readable recording medium having recorded thereon a program for performing the method according to any one of claims 18 to 34.