CN109737941B - Human body motion capture method - Google Patents
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Abstract
In the human body motion capture method provided by the invention, 4 inertial sensors are respectively fixed on the big arm and the small arm of two arms of a human body, and the motion data of bones is equivalent to the data collected by the sensors. When the human arm moves, the data acquisition chip acquires data, the data completes a series of data processing in the main control chip, wherein the data processing comprises data correction, integration, coordinate matching and the like, and then the data can be transmitted by opening the transmitting module. The other end of the human body model is used for receiving data through a receiving module which is configured in advance, and then the data are transmitted to an upper computer through a USB data line, and the upper computer matches the transmitted rotation increment to the corresponding arm position so as to drive the human body model. The human body model is driven by the rotation increment, so that the problem of sensitivity of sensor fixation is effectively solved.
Description
Technical Field
The invention relates to the technical field of gesture information acquisition, in particular to a human body motion capturing method.
Background
In the eighties and ninety years of the last century, key laboratories and scientific research institutions in developed countries in Europe and America have provided a plurality of schemes for animation technology, and practical demonstration and experiments are carried out on each scheme. After decades of continuous exploration, the animation motion capture technology is continuously developed and perfected. Motion capture systems have experienced a wide variety of different devices in the market, including mechanical, electrical, acoustic, electromagnetic, optical, and inertial devices, with continued effort and extensive development by researchers. Each of these motion capture systems has advantages and disadvantages, and thus the application scenarios are different. At present, the human body motion capture technology is successfully applied to the fields of human-computer interaction, movie and television production, virtual reality, motion analysis and the like, and the human body motion capture technology permeates aspects of national defense, industry, daily life and the like.
Although the inertial motion capture system starts late for China, the inertial motion capture system has become a research hotspot for China due to the excellent performance and the lower cost. And with the continuous development of society, more and more companies produce motion capture.
Mechanical solutions have emerged relatively early. With this arrangement, the target object needs to be fixed in a series of positions on the body
A rigid support. When the target object moves, the rigid support on the body moves together, and the sensor on the support can measure the angle change of the body part. However, this solution is too inflexible and greatly restricts the movement of the target object.
The electromagnetic scheme is composed of a magnetic field receiver and a magnetic field emission source. The magnetic field emission source generates a magnetic field with a certain rule, and the receiver arranged on the target object is responsible for receiving the magnetic field at a specific position. When the target object moves, the receiver on the target object can calculate the self moving position according to the received magnetic field characteristics. The requirement of the scheme on the surrounding environment is extremely high, namely, the magnetic field interference cannot exist around the target object.
The acoustic scheme is that a series of ultrasonic generators are installed on a target object, receivers around the target object are responsible for receiving ultrasonic waves, the receiving time is different due to the fact that the distance between each generator and the receiver is different, and the position of the target object can be calculated through the time difference. But the accuracy of this solution is too poor.
The optical scheme is widely applied in the market at present, and the camera is used for identifying the mark point on the target object so as to acquire the motion of the target object. However, the scheme is too high in cost, and one set of equipment is at least hundreds of thousands of equipment, so that the development of the motion capture market is severely restricted.
Aiming at the defects of the scheme, the inertial motion capture is carried out at the same time. The method has the advantages of miniaturization, low cost, wireless transmission and the like according to the needs, and is a research hotspot in the field rapidly.
However, current motion capture devices require specific locations, such as optical devices to mark specific points to facilitate the collection of motion information for those specific locations; the inertial motion capture devices on the market have specific requirements on the placement position because the inertial motion capture devices are calibrated when being shipped from the factory, namely, the inertial motion capture devices are placed at specific positions so as to acquire data accurately, otherwise, the motion capture devices can cause wrong acquisition of motion information. The high requirements for the installation position of the inertial sensor seriously affect the practicability of the device.
Disclosure of Invention
The invention aims to provide a human body motion capture method, which adopts the rotation increment of an inertial sensor to drive a human body model by utilizing the rotation increment, and effectively solves the problem of sensitivity of fixation of the inertial sensor.
In order to achieve the above object, the present invention provides a human motion capture method, comprising:
installing an inertial sensor on each data acquisition node of an action acquirer, and sending motion data acquired by the inertial sensor to an upper computer, wherein a human body model is stored in the upper computer;
acquiring attitude data which are respectively measured by an accelerometer, a gyroscope and a magnetometer of the inertial sensor for multiple times and correcting zero data;
performing trigonometric function conversion on the corrected attitude data corresponding to the accelerometer and the magnetometer to convert the corrected attitude data corresponding to the accelerometer and the magnetometer into a quaternion corresponding to a first rotation angle, and performing integration on the corrected attitude data corresponding to the gyroscope to obtain corrected attitude data corresponding to the gyroscope which is converted into a quaternion corresponding to a second rotation angle;
performing adaptive linear interpolation on the normalized quaternions corresponding to the first rotation angle and the second rotation angle to obtain a rotation increment;
driving the mannequin with the rotational increments;
the step of correcting the zero point data of the attitude data comprises the following steps:
acquiring attitude data which are respectively measured by an accelerometer, a gyroscope and a magnetometer of the inertial sensor for multiple times and putting the attitude data into a two-dimensional array;
calculating the average value of a plurality of attitude data respectively measured by the accelerometer, the gyroscope and the magnetometer according to the two-dimensional array;
subtracting the corresponding average value of any attitude data measured by the accelerometer, the gyroscope and the magnetometer to obtain the zero drift of the accelerometer, the gyroscope and the magnetometer;
respectively calculating the scale factors of the accelerometer, the gyroscope and the magnetometer;
multiplying the zero drift of the accelerometer, the gyroscope and the magnetometer by respective scale factors to obtain corrected attitude data;
the scale factors of the accelerometer, the gyroscope and the magnetometer are respectively as follows:
wherein, the offset is an absolute value of the zero drift, PI is a circumferential rate, G is a gravitational acceleration, and the corrected attitude data is an euler angle.
Optionally, the step of converting the corrected attitude data into a quaternion includes:
integrating the corrected attitude data corresponding to the gyroscope to obtain a first rotation angle e0,e1,e2；
Obtaining a second rotation angle e according to the corrected attitude data corresponding to the accelerometer and the magnetometer through the following trigonometric function transformation formula0',e1',e2'；
Wherein, a0,a1,a2For the corrected attitude data, m, corresponding to the accelerometer0,m1,m2Corrected attitude data corresponding to the magnetometer;
respectively calculating the first rotation angles e0,e1,e2And a second angle of rotation e0',e1',e2' cosine and sine values to obtain said first rotation angle e0,e1,e2And a second angle of rotation e0',e1',e2A quaternion of.
Optionally, the step of normalizing the quaternion includes:
acquiring a modulus value of the quaternion;
and dividing each numerical value in the quaternion by the module value to obtain the normalized quaternion.
Optionally, the first rotation angle e is judged0,e1,e2And a second angle of rotation e0',e1',e2And if the correlation degree is less than a set value, adopting spherical linear interpolation, and if the correlation degree is greater than or equal to the set value, adopting adaptive linear interpolation.
Optionally, the rotation increment is obtained according to an interpolation coefficient of adaptive linear interpolation.
Has the advantages that:
the positions of the inertial sensors placed by each motion collector are different, so that the obtained initial postures are different, the deviation exists in the initial calibration of the initial motion, and the rotation increment of the same bone is a fixed value when the same bone rotates, so that the human body model is driven by the rotation increment, the problem of fixed sensitivity of the inertial sensors is solved, the data is processed by adopting the self-adaptive linear interpolation, the problem of discontinuity of posture data is solved, and the ideal effect is achieved
Drawings
FIG. 1 is a flowchart of a human motion capture method according to an embodiment of the present invention;
FIG. 2 is a diagram of the movement angle of the same piece of bone of a human body according to an embodiment of the present invention;
FIG. 3 is a wiring diagram of an inertial sensor provided in an embodiment of the invention;
FIG. 4 is a wiring diagram of a transmitter module provided in an embodiment of the present invention;
FIG. 5 is a wiring diagram of the MCU provided in the embodiment of the present invention;
FIG. 6 is an overall block diagram of the overall hardware circuit design provided by the present invention;
fig. 7 is a diagram of motion data transmission according to an embodiment of the present invention.
Detailed Description
The following describes in more detail embodiments of the present invention with reference to the schematic drawings. Advantages and features of the present invention will become apparent from the following description and claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
It can be understood that the positions of the inertial sensors for each motion acquirer are different, so that the acquired initial postures are different, and the initial calibration of the initial motions has deviation.
The first step is as follows: acquiring attitude data;
the data collected by the inertial sensor is more, but only the attitude data of the accelerometer, the magnetometer and the gyroscope in the inertial sensor needs to be read. The data transmission bit number of the acquisition chip is 16 bits, so that the high 8 bits and the low 8 bits of the data need to be spliced together to form 16 bits of data for transmission. According to the chip data manual and the user instruction manual, the read data can be placed in an array defined by the user data manual, wherein the 0 th bit to the 5 th bit in the array are attitude data of the triaxial accelerometer, and the 0 th bit data is shifted to the left by 8 bits and then added with the 1 st bit data to be spliced into a first value of the accelerometer. The same method obtains a second value and a third value of the accelerometer. The 8 th bit to the 13 th bit in the array are attitude data of the gyroscope, the 15 th bit to the 19 th bit are attitude data of the magnetometer, and the data are spliced and transmitted by the method, so that the attitude data of the gyroscope and the magnetometer can be obtained.
The second step is that: correcting zero data;
the attitude data acquired by the inertial sensor has zero drift, and the zero drift is larger and larger along with the longer time, so that the stability of the system is seriously influenced. The data correction step comprises the following steps:
and (3) reading attitude data measured by an accelerometer and a gyroscope for 20 times, putting the attitude data into a two-dimensional array, storing corresponding numerical values into the same column, and solving the column average value of the corresponding numerical values. For example: putting one value of three data of the gyroscope into the 0 th column of the two-dimensional array, reading the values for 20 times, sequentially putting the values into the 0 th column of the 0 th row and the 0 th column of the 1 st row till the 0 th column of the 19 th row, and then taking the average value of the 0 th column;
and calculating zero drift, and subtracting a corresponding average value from the original attitude data for the zero drift, namely, the zero drift is the original attitude data-average value, wherein the original attitude data can be any one group of data read 20 times. Subtracting a corresponding average value from any attitude data measured by the accelerometer, the gyroscope and the magnetometer to obtain zero drift of the accelerometer, the gyroscope and the magnetometer;
calculating a scale factor, empirically, of the gyroscope
Wherein PI is a circumference ratio, namely PI is 3.14; scaling factor for an accelerometer
Wherein offset is the absolute value of zero drift, and G is the gravity acceleration; scale factor for a magnetometer
And acquiring corrected attitude data, and multiplying the zero drift of the accelerometer, the gyroscope and the magnetometer by respective scale factors to obtain the corrected attitude data, wherein the corrected attitude data is the Euler angle.
The third step: integrating the value of the gyroscope to obtain a first rotation angle e0,e1,e2. On the other hand, the second rotation angle e is obtained by trigonometric function transformation0',e1',e2'。
Dividing the angle value by the sampling rate to obtain the integral of the angle with respect to time, and obtaining the first rotation angle e0,e1,e2；
The second rotation angle e can be calculated by trigonometric function transformation0',e1',e2' is:
wherein, a0,a1,a2For the corrected attitude data, m, corresponding to the accelerometer0,m1,m2And the corrected attitude data corresponding to the magnetometer.
This first angle of rotation e0,e1,e2And a second angle of rotation e0',e1',e2' should theoretically be equal, but there will be an error in the actual measurement, i.e. the two angles are not equal.
The fourth step: converting the corrected attitude data into quaternions;
firstly, the cosine value and the sine value of each Euler angle are calculated,
the quaternion is obtained as:
q0＝k0*g0*s0+k1*g1*s1；q0'＝k0'*g0'*s0'+k1'*g1'*s1'；
q1＝k1*g0*s0-k0*g1*s1；q1'＝k1'*g0'*s0'-k0'*g1'*s1'；
q2＝k0*g1*s0+k1*g0*s1；q2'＝k0'*g1'*s0'+k1'*g0'*s1'；
q3＝k0*g0*s1-k1*g1*s0；q3'＝k0'*g0'*s1'-k1'*g1'*s0'；
wherein q is0,q1,q2,q3、q0',q1',q2',q3' is a quaternion. The basic form of quaternion is:
its equivalent form is
The above formula yields the equivalent of a quaternion.
The fifth step: normalizing the quaternion;
taking the modulus of the quaternion:
dividing the quaternion numerical value by the module value to obtain a normalized quaternion;
and a sixth step: (optional/optimization step) adaptive linear interpolation;
the motion data collected by the inertial sensor is the attitude data of each position point and is discontinuous, and the system needs a certain time to process the attitude data, so that the motion data is discontinuous. The present invention employs adaptive linear interpolation to smooth motion data.
Adaptive linear interpolation is the combination of spherical linear differences and linear differences. Since the attitude data is expressed by quaternion, it can be regarded as a vector. When vector
And vector
When the angle between the two vectors is too large, namely the correlation degree of the two vectors is low, spherical linear interpolation is adopted. When vector
And vector
When the angle between the two vectors is relatively small, namely the correlation degree of the two vectors is relatively high, linear interpolation is relatively good. Therefore, the problem of the smoothness of system data can be solved, and the problem of the universal lock can be solved.
Computing vectors
And vector
Cosine value between:
cosθ＝q0q’0+q1q’1+q2q’2+q3q’3；
judging the correlation degree between the two vectors:
if the included angle between the two vectors is large, the cosine value is small, the correlation degree of the two vectors is smaller than a set value, and spherical linear interpolation is adopted. Otherwise, linear interpolation is used.
Calculating an adaptive linear interpolation coefficient:
if spherical linear interpolation is adopted, the interpolation coefficient is calculated as follows:
if linear interpolation is adopted, the interpolation coefficient is as follows:
A0＝1-t；
A1＝t；
wherein
The method for obtaining r is as follows:
wherein G is the gravitational acceleration value, a0,a1,a2As a measure of acceleration, m0,m1,m2Are measurements of a magnetometer.
The new pose data is then the interpolation coefficient multiplied by the coordinates of the corresponding point as follows:
p0＝A0e0+A1e’0；
p1＝A0e1+A1e’1；
p2＝A0e2+A1e2,；
p3＝A0e3+Ae’3；
wherein p is0,p1,p2,p3Is new attitude data, i.e. in the form of a quaternion of rotational increments. e.g. of the type0,e1,e2,e3Is a quaternion form of the angle θ derived by integration, e'0,e’1,e’2,e’3In the form of a quaternion of the angle theta derived by triangulation.
By way of example, the following illustrates how rotational increments solve the inertial sensor fixed sensitivity problem:
when the inertial sensor is fixed on the human skeleton, the motion of the human skeleton is replaced by the motion of the inertial sensor, namely the motion data of the skeleton is the data collected by the inertial sensor. The rotation increment is the value of the angular change of the rotating object during rotation, and the rotation of the bone generally carries the rotation, as shown in fig. 2, the rotation increment is explained below:
as is evident from fig. 2: when the bone rotates, the rotation angle theta 1 of the outer side of the bone is equal to the rotation angle theta 2 of the inner side of the bone. The same piece of bone is rotated by the same angle, i.e., the increment of rotation is the same. That is, the inertial sensor is fixed at any position of the same bone, and the obtained rotation increment is equal, so that the problem of sensitivity of the fixation of the inertial sensor is solved.
The acquisition nodes of the inertial sensor are responsible for acquiring information of each joint of a human body, the information is subjected to data processing to obtain required rotation increment data, then the rotation increment data are sent to an upper computer through a transmitting terminal, and the upper computer drives the human body model by using the data after receiving the rotation increment data. The hardware circuit of the invention mainly comprises a data acquisition unit, a main control unit and a transmitting unit.
In the existing inertial motion capture equipment, the adopted nine-axis sensor is usually formed by splicing a three-axis magnetometer, a three-axis accelerometer and a three-axis gyroscope, while the MPU9250 is adopted in the invention, the integrated design greatly simplifies the space occupied by the equipment, and more importantly, the programming complexity is also simplified to a great extent. The wiring diagram of the MPU9250 is shown in fig. 3. The radio frequency chip adopts nRF24L 01P. The chip can complete the receiving and sending of information without an additional antenna. The chip may also select SPI or I2C for communication according to the designer's custom. The wiring diagram of the rf module is shown in fig. 4. The MCU master control chip adopts STM32F 301. The chip is responsible for controlling the MPU9250 and the transmitting module, and in addition, the chip also carries out preprocessing on data collected by the MPU 9250. The wiring diagram of the main control chip is shown in fig. 5. The overall block diagram of the overall hardware circuit design is shown in fig. 6.
After the hardware circuit is set, the following steps of calculating the attitude of the system are explained. The method for calculating the attitude is different according to different designers and different methods. Moreover, the chip of STM32 is not precise in self-contained DMP attitude calculation, but is a way for common design. The design draws past experience and designs a new algorithm on the basis of the past, and the steps are as follows:
the first step is as follows: reading attitude data;
the second step is that: data correction, namely correcting the data due to zero drift of the data;
the third step: and integrating the data of the gyroscope to obtain the rotation angle. Deriving another angular form by trigonometric transformation of nine-axis sensor data
The fourth step: converting the data into quaternions and normalizing;
the fifth step: and carrying out self-adaptive linear interpolation on the angles obtained through the two different forms to obtain a rotation increment.
And a sixth step: the resulting rotational increments are converted to a left-handed coordinate system. The right-hand coordinate system is used for data acquisition, the left-hand coordinate system is used for the model world, namely the upper computer, and the coordinate systems are not matched. The coordinate system can be matched by: x, y, z. Namely, the left-hand coordinate system is obtained by taking the negative value of the Z axis of the coordinate system.
Furthermore, as shown in fig. 7, 4 pieces of inertia sensors are respectively fixed on the big arm and the small arm of two arms of a human body, as shown in fig. 7, the motion data of bones is equivalent to the data collected by the inertia sensors. When the human arm moves, the data acquisition chip acquires data, the main control chip reads the data by adopting timer interruption, and the data completes a series of data processing in the main control chip, wherein the data processing comprises data correction, integration, coordinate matching and the like, and then the data can be transmitted by opening the transmitting module. The other end of the human body model is used for receiving data through a receiving module which is configured in advance, and then the data are transmitted to an upper computer through a USB data line, and the upper computer matches the transmitted rotation increment to the corresponding arm position so as to drive the human body model.
In summary, in the human motion capture method provided in the embodiment of the present invention, 4 data acquisition nodes and 1 data receiving node are used. The data acquisition nodes acquire data and send the data to the data receiving module, the data receiving module transmits the data to the upper computer through a USB data line, the upper computer adopts Unity 3D, after the upper computer is installed, a needed animation model is downloaded, and then the needed data is matched to the corresponding position, so that the action display of the upper half body can be completed.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A human motion capture method, comprising:
installing an inertial sensor on each data acquisition node of an action acquirer, and sending motion data acquired by the inertial sensor to an upper computer, wherein a human body model is stored in the upper computer;
acquiring attitude data which are respectively measured by an accelerometer, a gyroscope and a magnetometer of the inertial sensor for multiple times and correcting zero data;
performing trigonometric function conversion on the corrected attitude data corresponding to the accelerometer and the magnetometer to convert the corrected attitude data corresponding to the accelerometer and the magnetometer into a quaternion corresponding to a first rotation angle, and performing integration on the corrected attitude data corresponding to the gyroscope to obtain corrected attitude data corresponding to the gyroscope which is converted into a quaternion corresponding to a second rotation angle;
performing adaptive linear interpolation on the normalized quaternions corresponding to the first rotation angle and the second rotation angle to obtain a rotation increment;
driving the mannequin with the rotational increments;
the step of correcting the zero point data of the attitude data comprises the following steps:
acquiring attitude data which are respectively measured by an accelerometer, a gyroscope and a magnetometer of the inertial sensor for multiple times and putting the attitude data into a two-dimensional array;
calculating the average value of a plurality of attitude data respectively measured by the accelerometer, the gyroscope and the magnetometer according to the two-dimensional array;
subtracting the corresponding average value of any attitude data measured by the accelerometer, the gyroscope and the magnetometer to obtain the zero drift of the accelerometer, the gyroscope and the magnetometer;
respectively calculating the scale factors of the accelerometer, the gyroscope and the magnetometer;
multiplying the zero drift of the accelerometer, the gyroscope and the magnetometer by respective scale factors to obtain corrected attitude data;
the scale factors of the accelerometer, the gyroscope and the magnetometer are respectively as follows:
wherein, the offset is an absolute value of the zero drift, PI is a circumferential rate, G is a gravitational acceleration, and the corrected attitude data is an euler angle.
2. The human motion capture method of claim 1, wherein the step of converting the modified pose data to a quaternion comprises:
integrating the corrected attitude data corresponding to the gyroscope to obtain a first rotation angle e0,e1,e2；
Obtaining a second rotation angle e according to the corrected attitude data corresponding to the accelerometer and the magnetometer through the following trigonometric function transformation formula0',e1',e2'；
Wherein, a0,a1,a2For the corrected attitude data, m, corresponding to the accelerometer0,m1,m2Corrected attitude data corresponding to the magnetometer;
respectively calculating the first rotation angles e0,e1,e2And a second angle of rotation e0',e1',e2' cosine and sine values to obtain said first rotation angle e0,e1,e2And a second angle of rotation e0',e1',e2A quaternion of.
3. The human motion capture method of claim 2, wherein normalizing the quaternion comprises:
acquiring a modulus value of the quaternion;
and dividing each numerical value in the quaternion by the module value to obtain the normalized quaternion.
4. The human motion capture method of claim 3, wherein the first rotation angle e is determined0,e1,e2And a second angle of rotation e0',e1',e2And if the correlation degree is less than a set value, adopting spherical linear interpolation, and if the correlation degree is greater than or equal to the set value, adopting adaptive linear interpolation.
5. The human motion capture method of claim 4, wherein the rotation increment is derived from interpolation coefficients of adaptive linear interpolation.
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| CN107016342A (en) * | 2017-03-06 | 2017-08-04 | 武汉拓扑图智能科技有限公司 | A kind of action identification method and system |
| CN107898466B (en) * | 2017-10-17 | 2020-12-11 | 深圳大学 | Body motion capturing system and method based on inertial sensor |
| CN108720841A (en) * | 2018-05-22 | 2018-11-02 | 上海交通大学 | Wearable lower extremity movement correction system based on cloud detection |
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2019
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