CN116027905A - Double kayak upper limb motion capturing method based on inertial sensor - Google Patents
Double kayak upper limb motion capturing method based on inertial sensor Download PDFInfo
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Abstract
The invention belongs to the field of human motion capture, and provides an inertial sensor-based double kayak upper limb motion capture method which is applied to an actual boating scene and used for analyzing the kinematic gesture of the upper limb motion in the double kayak. The motion capture system based on the inertial sensor comprises six inertial nodes, a receiving node and an upper computer. Each inertia node can send data to an upper computer through a receiving node; after the initial calibration is completed, the data fusion precision is improved by using an extended Kalman filtering method, and the attitude information of each moment is calculated; and extracting joint angles from the motion information, and analyzing the posture synchronization effect of two rowers by using a dynamic time regulation method. The invention is suitable for kayak training in actual scenes, can monitor the motion postures of two persons simultaneously, synchronously analyze joint angles, and help a coach and an athlete to clearly train the effect and improve the technical actions.
Description
Technical Field
The invention relates to the field of human motion capture, in particular to a double kayak upper limb motion capture method based on an inertial sensor.
Background
Kayaks have a long history and begin to appear as water tools for fishing hunting in the original society. As production progresses and society progresses, kayaks have also become a common sport in recreational and sports games. A complete rowing motion requires a coordinated overall exertion involving muscle groups of the legs, buttocks, lower back, shoulders and arms. The rower adopts the posture of single knee kneeling and single paddle rowing, takes the waist as the axis, fully utilizes the rotation of the body, drives the shoulders and arms to move, and obtains the maximum paddle output power.
The athletic ability of a kayak depends on the physical ability and skill level of the athlete. During the rotation and force generation process of the body, arms mainly play a role of a tie, so that analysis of the posture of the upper limbs can help an athlete to more intuitively establish a movement mode. For double kayak exercises, two athletes are required to cooperate to achieve the best effect. Through double synchronous gesture analysis, can help the train to find best matching kayak sportsman and train, obtain better achievement.
The single coaching evaluation method is relatively subjective. When a kayak athlete learns new technology, visual training results cannot accurately locate technical action defects. Through motion capture, human body motions of the athlete can be tracked and recorded in real time, and body gestures, technical expertise, motion defects and the like of the athlete are analyzed. According to motion capture technology and application research review thereof, the invention selects an inertial motion capture method to solve the motion gesture of an athlete because the optical motion capture configuration environment has high requirements and is not suitable for an outdoor environment. And selecting an extended Kalman filtering method to perform data fusion, so that the motion capturing precision is improved. And analyzing the joint angles of the two persons by using a time sequence analysis method, providing reference indexes for coaches and athletes, and assisting in training.
Disclosure of Invention
The invention aims to provide an inertial sensor-based double kayak upper limb motion capturing method which can be applied to an actual kayak scene and is based on an inertial sensing technology.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
the utility model provides a double kayak upper limbs action capture method based on inertial sensor, is realized based on a double kayak upper limbs action capture system based on inertial sensor, and double kayak upper limbs action capture system based on inertial sensor includes: six inertia nodes, a receiving node and an upper computer;
each inertia node is internally provided with a triaxial gyroscope, a triaxial accelerometer and a triaxial magnetometer, and a wireless module and a memory card;
the receiving node is provided with a 32-bit microprocessor and a Lora wireless communication module, and is used for controlling data acquisition and uploading of the inertial node, so that data communication between the upper computer and the triaxial accelerometer, data of the triaxial gyroscope and data communication between the upper computer and the triaxial magnetometer are realized;
the upper computer is written by Qt; has good man-machine interaction interface.
The double kayak upper limb motion capturing system based on the inertial sensor is stored in a storage card in an offline data acquisition mode; a plurality of inertia nodes start to collect at the same time, so that synchronization is ensured; after the athlete lands on the land, the data collected by the inertia node is transmitted to an upper computer through a receiving node for further processing and analysis;
the double kayak upper limb motion capturing method based on the inertial sensor comprises the following steps of:
a1, respectively wearing six inertial nodes on the waist, chest, left big arm, left small arm, right big arm and right small arm of a human body, and synchronously collecting the inertial movement data of the upper limbs of two athletes; each inertia node transmits data to an upper computer through a receiving node, and outputs triaxial accelerometer data, triaxial gyroscope data and triaxial magnetometer data;
a2, firstly standing the athlete in a static direction facing the north direction of the magnetic field for initial calibration, and aligning a sensor coordinate system with a carrier coordinate system; simultaneously, attitude quaternions from a sensor coordinate system to a geographic coordinate system are obtained, and finally, the athlete attitude information is described in the geographic coordinate system;
the motion pose of an object in space is generally described, usually with respect to spatial motion in a geographic coordinate system, while in an inertial coordinate system, acceleration and gyroscope information measured along the axis of the carrier coordinate system. Therefore, it is necessary to convert the measured values in the carrier coordinate system into measured values in the ground coordinate system using mathematical transformation, so that the pose information can be finally described in the geographic coordinate system. Converting the measured values in the carrier coordinate system into measured values in the geographic coordinate system by utilizing mathematical transformation, and finally describing athlete posture information in the geographic coordinate system;
in updating the pose of the carrier, this coordinate transformation system is also required as a reference for a change in the pose angle of the object in order to describe the motion pose of the object conveniently in a geographical coordinate system. Updating the attitude information by using a quaternion method; in an initial state, a geographic coordinate system coincident with the carrier coordinate system is rotated to a current posture through a yaw angle phi around a Z axis, a pitch angle theta around a Y axis and a roll angle phi around an X axis;
in the geographic coordinate system, the space state of the moving object moves according to the sequence of course angle, roll angle and pitch angle, the final synthesis result is consistent with the space motion state of the object, and the rotation matrix from the geographic coordinate system GCS to the carrier coordinate system BCS is expressed as follows:
according to the rotation transformation sequence of Z-Y-X, the initial attitude quaternion is expressed as:
since the sensors are fixed to the limb surface, the kayak player needs to stand north for a period of time while calibrating the initial stance, the initial alignment of ICS and BCS is approximately equal. Initial alignment of the BCS and GCS can be described as
The initial alignment is:
wherein ,
a rotation quaternion representing the sensor coordinate system to the geographic coordinate system; />
A rotation quaternion representing the carrier coordinate system to the sensor coordinate system; />
A rotation quaternion representing the carrier coordinate system to the geographic coordinate system;
after initial alignment, the actual positions of the tri-axis accelerometer, tri-axis gyroscope data and tri-axis magnetometer are represented by quaternions (1, 0) in the geographic coordinate system GCS; the gesture quaternion converges to an actual position in a plurality of iterations;
a3, carrying out data fusion by adopting an extended Kalman filtering method, and solving the body posture of the athlete; the human body is regarded as a group of rigid body models, and comprises a plurality of joints with customized lengths, and each joint part is represented by a gesture quaternion; the rigid body model is modeled as a line connected by friction-free joints, the waist is taken as a root node, the human body posture at each moment is updated, and the motion information is restored;
taking a posture quaternion, a triaxial gyroscope error, a human joint position and a human joint speed as system state quantity, and taking a triaxial magnetometer and position information and speed information calculated from a whole body posture as an extended Kalman filtering method of a system observation value to perform data fusion, wherein a state equation is as follows:
wherein ,px 、p y 、p z Respectively the positions of joints of human body v x ，v y ，v y The joint velocity, omega of human body xb ，ω yb ，ω zb Three-axis gyroscope errors, q 0 ，q 1 ，q 2 ，q 3 Respectively attitude quaternions;
the initial covariance matrix is:
wherein ,σp Is the covariance of the joint position of the human body, sigma v For the covariance of the joint velocity of the human body, sigma q Is the gesture quaternion covariance, sigma ωb Is the error covariance of the triaxial gyroscope; p is an initial value of error covariance, which represents confidence coefficient of current prediction state, and decides initial convergence speed; with iteration of the Kalman filter, the P value is continuously changed; when the system enters a steady state, the P value converges to the minimum estimated variance matrix to obtain the optimal Kalman gain;
the gesture quaternion updating process is as follows:
the state differential equation of the extended kalman system is:
wherein ,ax ，a y ，a z The g is a triaxial accelerometer value and is a gravitational acceleration;
the input noise matrix is:
wherein ,σω Error variance, sigma, of triaxial gyroscope a Error variance for the triaxial accelerometer;
the system observation equation is:
z＝[p x p y p z v x v y v z ω xm ω ym ω zm ] T
defining the gravity component as [0 0-1 ], obtaining an observation matrix as follows:
the observed noise matrix is:
wherein ,σm Error variance for the triaxial magnetometer;
the R value is the measurement noise; r is too large and the response of the kalman filter will slow. R is too small, and the system is not easy to stabilize. Keeping the triaxial gyroscope motionless during test and recordingOutputting data of the triaxial gyroscope in a period of time, wherein the output data is normally distributed; according to the 3σ principle, a normal distribution (3σ) is taken 2 As an initial value of R;
substituting the state equation, the initial covariance matrix, the state differential equation, the observation equation and the noise matrix into an extended Kalman filtering iterative process, and substituting the Kalman gain K k The method comprises the following steps:
wherein ,Pk For initial covariance matrix, H k The observation matrix is R, and the observation noise matrix is R;
the a priori matrix and the a posteriori matrix are updated as follows:
wherein ,
as state quantity, u k-1 For the system input value, Φ is a state transition matrix, Q is a covariance matrix of system noise, z k Is the observation equation, H k Is an observation matrix;
according to the posture position after data fusion, the waist is taken as a root node, the human body posture at each moment is updated, and the movement posture information is restored;
a4, extracting joint angles between adjacent joint segments according to the result of the gesture calculation of A3; synchronously analyzing the left shoulder, the right shoulder, the left elbow and the right elbow joint angles of two athletes;
defining the upper body as an eleven-segment structure, and defining the angle between adjacent articular segments as an articular angle; vector U and vector V represent the ipsilateral upper arm and ipsilateral forearm of the upper limb body vector, respectively; taking the right arm as an example, the joint angle of the right elbow is defined as the angle between the right upper arm and the right forearm. Vectors U and V are used to represent the upper right arm and forearm, respectively, of the upper body vector. The length of the vector U and the vector V are defined according to the length of the actual limb of the human body; the joint angle is calculated as follows:
wherein ,
respectively representing two adjacent limb segment vectors in a geographic coordinate system; the change of the joint angle in the sportsman's exercise process is a time sequence, and the dynamic time warping method DTW is used for measuring the correlation of the corresponding joint angle; DTW calculates the similarity between two time series by stretching and shortening the time series, and distorts one of the time series on the time axis to achieve alignment; calculating the DTW distance for measuring the similarity of the two time sequences according to the Euclidean distance; the calculation is as follows:
wherein ,d(xi ,y i ) Represents x i ,y i A distance therebetween; the DTW distance is normalized and used for measuring the similarity between two time sequences and providing quantized posture synchronization indexes for coaches and athletes.
The geographic coordinate system GCS is a spherical coordinate system of the earth; the gravity center of a human body is taken as an origin, the X axis points to the north of the earth, the Y axis points to the east of the earth, and the Z axis points to the lower direction perpendicular to the surface of the earth; the inertial coordinate system ICS takes the center of a sensor as an origin and the coordinate axis direction of the triaxial gyroscope as a reference; the carrier coordinate system BCS is defined, the direction pointing to the right side of the body of the athlete is an X axis, the direction pointing to the front of the body of the athlete is a Y axis, and the direction vertical to the horizontal plane of the body of the athlete is a Z axis; the sensor coordinate system takes the center of the sensor as an origin and the coordinate axis direction of the triaxial gyroscope as a reference.
In step A2, the magnetometer is subject to magnetic interference during the measurement, i.e. the earth's magnetic field at the location of the magnetic sensor is shifted due to the presence of magnetic substances or substances that affect the local magnetic field strength. And in the measuring process, correcting the output of the triaxial magnetometer by utilizing an ellipsoid fitting method based on a least square method.
The internal coordinate systems of the triaxial gyroscope, the triaxial accelerometer and the triaxial magnetometer are consistent, are aligned in a strict orthogonal mode, and support signal output with high sampling frequency. This range may cover the maximum measurement of rowing boat motion.
The whole size of the inertia node is 4.5cm multiplied by 3.5cm multiplied by 2.25cm, and the gravity is 42g, so that the canoe is not affected by the athlete. Each inertial node is independently powered by a 3.7V (400 mAh) rechargeable lithium battery, and can perform data acquisition for two hours.
The invention has the beneficial effects that: the invention provides an inertial sensor-based double kayak upper limb motion capturing method, which adopts a wireless transmission mode to collect human motion information, and adopts an extended Kalman filtering method to improve data fusion precision and calculate human joint angles at all times. The invention is suitable for kayak training in actual scenes, can monitor the motion postures of two persons simultaneously, synchronously analyze joint angles, and help a coach and an athlete to clearly train the effect and improve the technical actions.
Drawings
FIG. 1 is a flow chart of a double kayak upper limb motion capturing method based on an inertial sensor;
FIG. 2 is a hardware schematic diagram of the inertial sensor-based double kayak upper limb motion capture system of the present invention;
FIG. 3 is a schematic diagram of a data fusion algorithm of the present invention;
FIG. 4 is a schematic illustration of rowing motion capture in accordance with an embodiment of the invention;
FIG. 5 is a schematic diagram of an inertial sensor node installation in example two of the present invention;
FIG. 6 (a) is a graph of an initial angular analysis of a joint in example two of the present invention;
FIG. 6 (b) is a graph showing the analysis of the joint alignment angle in example II of the present invention.
Detailed Description
In order to make the technical problems solved by the invention, the technical scheme adopted and the technical effects achieved clearer, the invention is further described in detail below with reference to the accompanying drawings and the embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the matters related to the present invention are shown in the accompanying drawings.
Fig. 1 is a flowchart of a double kayak upper limb motion capturing method based on an inertial sensor according to an embodiment of the present invention. The method for synchronously analyzing the upper limb gestures of the double kayak provided by the embodiment of the invention can be realized by the inertial sensor-based double kayak upper limb motion capturing system provided by the embodiment of the invention, and comprises the following steps:
a1, wearing six inertial nodes on the waist, chest, left big arm, left small arm, right big arm and right small arm of human body respectively through the bandage, and synchronously collecting the upper limb inertial motion data of two athletes. And each inertial sensing node transmits data to the upper computer through the receiving node, and outputs the data of the triaxial accelerometer, the triaxial gyroscope and the triaxial magnetometer.
An inertial sensor-based motion capture system of a double kayak upper limb motion capture system based on an inertial sensor comprises six inertial nodes, a receiving node and an upper computer. Each inertia node is internally provided with a triaxial gyroscope, a triaxial accelerometer and a triaxial magnetometer, and a wireless module and a memory card. The overall size of the node is 4.5cm multiplied by 3.5cm multiplied by 2.25cm, and the gravity is 42g, so that the canoe is not affected by the athlete. Each node is independently powered by a 3.7V (400 mAh) rechargeable lithium battery, and can perform data acquisition for two hours. The receiving node is provided with a 32-bit microprocessor and a Lora wireless communication module, so that data acquisition and uploading of the measuring node can be controlled, and data communication between the PC and each sensor is realized. The upper computer is written by Qt, and has a good man-machine interaction interface. The system adopts an off-line data acquisition mode to store in a storage card. And a plurality of nodes start to collect simultaneously, so that timely synchronization is ensured. After the kayak athlete lands on the shore, the data collected by the measuring node is transmitted to an upper computer through the receiving node for further processing and analysis.
A2, firstly standing the athlete in the north direction of the magnetic field in a static manner to perform initial calibration, aligning a sensor coordinate system with a carrier coordinate system, simultaneously obtaining a rotation quaternion from the sensor coordinate system to a geographic coordinate system, and finally describing attitude information in the geographic coordinate system.
Preferably, the triaxial magnetometer is susceptible to magnetic interference during the measurement process, i.e. due to the presence of magnetic substances or substances that can influence the local magnetic field strength, the earth's magnetic field at the location where the magnetic sensor is placed is shifted. The invention selects an ellipsoid fitting method based on a least square method to correct the output.
The motion pose of an object in space is generally described, usually with respect to spatial motion in a geographic coordinate system, while in an inertial coordinate system, acceleration and gyroscope information measured along the axis of the carrier coordinate system. Therefore, it is necessary to convert the measured values in the carrier coordinate system into measured values in the geographical coordinate system using a mathematical transformation, so that the pose information can be finally described in the geographical coordinate system.
The invention defines the coordinate system as follows:
geographic Coordinate System (GCS): the geographic coordinate system is the spherical coordinate system of the earth. The X axis points to the north of the earth, the Y axis points to the east of the earth, and the Z axis points downward perpendicular to the earth's surface.
Inertial Coordinate System (ICS): coordinates of inertial sensors fixed to the body surface are defined.
Carrier coordinate system (BCS): the direction pointing to the right side of the plane of the moving object is defined as an X axis, the direction pointing to the front of the object is defined as a Y axis, and the direction vertical to the horizontal plane of the object is defined as a Z axis.
In updating the pose of the carrier, this coordinate transformation system is also required as a reference for a change in the pose angle of the object in order to describe the motion pose of the object conveniently in a geographical coordinate system. The invention selects the quaternion method to update the gesture.
The geographic coordinate system, which coincides with the carrier coordinate system, is rotated to the current attitude by a yaw angle (ψ) about the Z-axis, a pitch angle (θ) about the Y-axis and a roll angle (Φ) about the X-axis.
In the geographic coordinate system, the space state of the moving object can move according to the sequence of the course angle, the roll angle and the pitch angle, and the final synthesis result is consistent with the space movement state of the object. The rotation matrix from GCS to BCS can be expressed as:
according to the rotation transformation order of Z-Y-X, the initial pose quaternion can be expressed as:
since the sensor is fixed on the body surface of the limbIn the face, the kayak player needs to stand north for a period of time while calibrating the initial pose, so the initial alignment of ICS and BCS is approximately equal. Initial alignment of the BCS and GCS can be described as
The initial alignment is:
the actual position of the sensor after initial alignment is represented by quaternion (1, 0) in the reference frame GCS. The quaternion converges to the actual position in several iterations.
And A3, carrying out data fusion by adopting an extended Kalman filtering method, and solving the body posture of the athlete. The human body is regarded as a set of rigid body models, comprising a plurality of joints of customized length, each joint part being represented by a quaternion. These body parts are modeled as lines connected by frictionless joints. The waist is taken as a root node, the human body posture at each moment is updated, and the motion information is restored.
Preferably, the invention selects the extended kalman filtering method which takes the attitude quaternion, the gyroscope error, the node position and the node speed as the system state quantity and takes the triaxial magnetometer and the position and speed information calculated from the whole body attitude as the system observation value to carry out data fusion, and the state equation is as follows:
x＝[p x p y p z v x v y v z q 0 q 1 q 2 q 3 ω xb ω yb ω zb ] T
the initial covariance matrix is:
p is an initial value of the error covariance, and represents the confidence level of the current prediction state, and determines the initial convergence rate. The value of P will change continuously with the iteration of the kalman filter. When the system goes into steady state, the value of P converges to a minimum estimated variance matrix, and the Kalman gain is optimal.
The quaternion update process is as follows:
the state differential equation of the extended kalman system is:
the input noise matrix is:
the system observation equation is:
z＝[p x p y p z v x v y v z ω xm ω ym ω zm ] T define the gravity component as [0 0-1 ]]The observation matrix is obtained as follows:
the observed noise matrix is:
the R value is the measurement noise. R is too large and the response of the kalman filter will slow. R is too small, and the system is not easy to stabilize. Keeping the gyroscope motionless during testing, and recording a section of gyroscopeAnd outputting data of the gyroscope in time, wherein the data are approximately in normal distribution. According to the 3σ principle, a normal distribution (3σ) is taken 2 As an initial value of R.
Substituting the formula into the iterative process of the extended Kalman filter, and obtaining the Kalman gain K k The method comprises the following steps:
the a priori and posterior matrices are updated as follows:
and updating the human body posture at each moment by taking the waist as a root node according to the posture position after data fusion, and restoring the motion information.
And A4, extracting joint angles between adjacent joint segments according to the result of the A3 gesture calculation. And synchronously analyzing the left shoulder, the right shoulder, the left elbow and the right axis joint angles of two athletes, and providing accurate and visual synchronous analysis results of double postures for coaches and athletes.
Preferably, the body is defined as an eleven-segment structure in the present invention, and the angle between adjacent articular segments is defined as the joint angle. Taking the right arm as an example, the joint angle of the right elbow is defined as the angle between the right upper arm and the right forearm. Vectors U and V are used to represent the upper right arm and forearm, respectively, of the upper body vector. The length is defined according to the actual limb length of the human body. The joint angle is calculated as follows:
wherein ,
representing two adjacent limb segment vectors in the geographic coordinate system respectively.
The change in joint angle during kayak exercises can be seen as a time series. A dynamic time warping (Dynamic Time Warping, DTW) method may be used to measure the correlation of the corresponding joint angles. DTW can calculate the similarity between two time series by stretching and shortening the time series, warping one of the series on the time axis to achieve better alignment. And (3) dynamic time-ordered time sequences, and calculating the DTW distance for measuring the similarity of the two time sequences according to the Euclidean distance. The calculation is as follows:
wherein ,d(xi ,y i ) Represents x i ,y i Distance between them. The DTW distance normalized can be used to measure the similarity between two time series, and the parameter can provide a quantized posture synchronization index for coaches and athletes.
The embodiment of the invention provides a double kayak upper limb motion capturing system based on an inertial sensing technology, which adopts a wireless transmission mode to collect human motion information, adopts an extended Kalman filtering method to improve data fusion precision, calculates human joint angles at all times, and performs kinematic synchronous analysis on double kayak gestures.
The invention is further illustrated by way of example:
example 1,
The athlete dresses the node on the upper limb, performs rowing action in the outdoor environment, and the experimental result is shown in fig. 4. The human body posture information at each moment can be intuitively restored through the inertial motion capturing device. According to the human skeleton model, the joint angle between the adjacent joint segments can be accurately calculated.
Example two,
In the double rowing process, two athletes wear inertial nodes to synchronously acquire data, and the dynamic time alignment method is used for analyzing the synchronism of the double postures, wherein the wearing schematic is shown in fig. 5, and the inertial nodes are respectively worn on the waist, the chest, the left big arm, the left small arm, the right big arm and the right small arm of the two athletes. The experimental results are shown in fig. 6 (a) and 6 (b), and the dynamic time alignment method can align two joint angle time sequences which are not synchronous in phase, and the result can be used as an index for measuring the synchronism of two persons.
Claims (3)
1. The double kayak upper limb motion capturing method based on the inertial sensor is characterized in that the double kayak upper limb motion capturing system based on the inertial sensor is realized, and the double kayak upper limb motion capturing system based on the inertial sensor comprises the following steps: six inertia nodes, a receiving node and an upper computer;
each inertia node is internally provided with a triaxial gyroscope, a triaxial accelerometer and a triaxial magnetometer, and a wireless module and a memory card;
the receiving node is provided with a 32-bit microprocessor and a Lora wireless communication module, and is used for controlling data acquisition and uploading of the inertial node, so that data communication between the upper computer and the triaxial accelerometer, data of the triaxial gyroscope and data communication between the upper computer and the triaxial magnetometer are realized;
the upper computer is written by Qt;
the double kayak upper limb motion capturing system based on the inertial sensor is stored in a storage card in an offline data acquisition mode; a plurality of inertia nodes start to collect at the same time, so that synchronization is ensured; after the athlete lands on the land, the data collected by the inertia node is transmitted to an upper computer through a receiving node for further processing and analysis;
the double kayak upper limb motion capturing method based on the inertial sensor comprises the following steps of:
a1, respectively wearing six inertial nodes on the waist, chest, left big arm, left small arm, right big arm and right small arm of a human body, and synchronously collecting the inertial movement data of the upper limbs of two athletes; each inertia node transmits data to an upper computer through a receiving node, and outputs triaxial accelerometer data, triaxial gyroscope data and triaxial magnetometer data;
a2, firstly standing the athlete in a static direction facing the north direction of the magnetic field for initial calibration, and aligning a sensor coordinate system with a carrier coordinate system; simultaneously obtaining attitude quaternions from a sensor coordinate system to a geographic coordinate system with the center of gravity of a human body as an origin, wherein three coordinate axes respectively point to the geographic north, the geographic east and the ground, and finally describing athlete attitude information in the geographic coordinate system;
converting the measured values in the carrier coordinate system into measured values in the geographic coordinate system by utilizing mathematical transformation, and finally describing athlete posture information in the geographic coordinate system;
updating the attitude information by using a quaternion method; in an initial state, a geographic coordinate system coincident with the carrier coordinate system is rotated to a current posture through a yaw angle phi around a Z axis, a pitch angle theta around a Y axis and a roll angle phi around an X axis;
in the geographic coordinate system, the space state of the moving object moves according to the sequence of course angle, roll angle and pitch angle, the final synthesis result is consistent with the space motion state of the object, and the rotation matrix from the geographic coordinate system GCS to the carrier coordinate system BCS is expressed as follows:
according to the rotation transformation sequence of Z-Y-X, the initial attitude quaternion is expressed as:
the initial alignment of the carrier coordinate system BCS and the geographic coordinate system GCS is described as
The initial alignment is:
wherein ,
a rotation quaternion representing the sensor coordinate system to the geographic coordinate system; />
A rotation quaternion representing the carrier coordinate system to the sensor coordinate system; />
A rotation quaternion representing the carrier coordinate system to the geographic coordinate system;
after initial alignment, the actual positions of the tri-axis accelerometer, tri-axis gyroscope data and tri-axis magnetometer are represented by quaternions (1, 0) in the geographic coordinate system GCS; the gesture quaternion converges to an actual position in a plurality of iterations;
a3, carrying out data fusion by adopting an extended Kalman filtering method, and solving the body posture of the athlete; the human body is regarded as a group of rigid body models, and comprises a plurality of joints with customized lengths, and each joint part is represented by a gesture quaternion; the rigid body model is modeled as a line connected by friction-free joints, the waist is taken as a root node, the human body posture at each moment is updated, and the motion information is restored;
taking a posture quaternion, a triaxial gyroscope error, a human joint position and a human joint speed as system state quantity, and taking a triaxial magnetometer and position information and speed information calculated from a whole body posture as an extended Kalman filtering method of a system observation value to perform data fusion, wherein a state equation is as follows:
x＝[p x p y p z v x v y v z q 0 q 1 q 2 q 3 ω xb ω yb ω zb ] T
wherein ,px 、p y 、p z Respectively the positions of joints of human body v x ，v y ，v y The joint velocity, omega of human body xb ，ω yb ，ω zb Three-axis gyroscope errors, q 0 ，q 1 ，q 2 ，q 3 Respectively attitude quaternions;
the initial covariance matrix is:
wherein ,σp For the variance of the joint position of the human body, sigma v Variance, sigma, of the velocity of joints of human body q Is the gesture quaternion variance, sigma ωb Error variance of the triaxial gyroscope; p is an initial value of error covariance, which represents confidence coefficient of current prediction state, and decides initial convergence speed; with iteration of the Kalman filter, the P value is continuously changed; when the system enters a steady state, the P value converges to the minimum estimated variance matrix to obtain the optimal Kalman gain;
the gesture quaternion updating process is as follows:
the state differential equation of the extended kalman system is:
wherein ,ax ，a y ，a z The g is a triaxial accelerometer value and is a gravitational acceleration;
the input noise matrix is:
wherein ,σω Error variance, sigma, of triaxial gyroscope a Error variance for the triaxial accelerometer;
the system observation equation is:
z＝[p x p y p z v x v y v z ω xm ω ym ω zm ] T
defining the gravity component as [0 0-1 ], obtaining an observation matrix as follows:
the observed noise matrix is:
wherein ,σm Error variance for the triaxial magnetometer;
the R value is the measurement noise; the triaxial gyroscope is kept motionless during testing, and output data of the triaxial gyroscope in a period of time are recorded, wherein the output data are normally distributed; according to the 3σ principle, a normal distribution (3σ) is taken 2 As an initial value of R;
substituting the state equation, the initial covariance matrix, the state differential equation, the observation equation and the noise matrix into an extended Kalman filtering iterative process, and substituting the Kalman gain K k The method comprises the following steps:
wherein ,Pk For initial covariance matrix, H k The observation matrix is R, and the observation noise matrix is R;
the a priori matrix and the a posteriori matrix are updated as follows:
wherein ,
as state quantity, u k-1 For the system input value, Φ is a state transition matrix, Q is a covariance matrix of system noise, z k Is the observation equation, H k Is an observation matrix;
according to the posture position after data fusion, the waist is taken as a root node, the human body posture at each moment is updated, and the movement posture information is restored;
a4, extracting joint angles between adjacent joint segments according to the result of the gesture calculation of A3; synchronously analyzing the left shoulder, the right shoulder, the left elbow and the right elbow joint angles of two athletes;
defining the upper body as an eleven-segment structure, and defining the angle between adjacent articular segments as an articular angle; vector U and vector V represent the ipsilateral upper arm and ipsilateral forearm of the upper limb body vector, respectively; the length of the vector U and the vector V are defined according to the length of the actual limb of the human body; the joint angle is calculated as follows:
wherein ,
respectively representing two adjacent limb segment vectors in a geographic coordinate system; the change of the joint angle in the sportsman's exercise process is a time sequence, and the dynamic time warping method DTW is used for measuring the correlation of the corresponding joint angle; DTW calculates the similarity between two time series by stretching and shortening the time series, and distorts one of the time series on the time axis to achieve alignment; calculating the DTW distance for measuring the similarity of the two time sequences according to the Euclidean distance; the calculation is as follows:
wherein ,d(xi ,y i ) Represents x i ,y i A distance therebetween; the DTW distance is normalized and used for measuring the similarity between two time sequences and providing quantized posture synchronization indexes for coaches and athletes.
2. The inertial sensor-based double kayak upper limb motion capture method of claim 1, wherein the geographic coordinate system GCS is a spherical coordinate system of the earth; the gravity center of a human body is taken as an origin, the X axis points to the north of the earth, the Y axis points to the east of the earth, and the Z axis points to the lower direction perpendicular to the surface of the earth; the inertial coordinate system ICS takes the center of a sensor as an origin and the coordinate axis direction of the triaxial gyroscope as a reference; the carrier coordinate system BCS is defined, the direction pointing to the right side of the body of the athlete is an X axis, the direction pointing to the front of the body of the athlete is a Y axis, and the direction vertical to the horizontal plane of the body of the athlete is a Z axis; the sensor coordinate system takes the center of the sensor as an origin and the coordinate axis direction of the triaxial gyroscope as a reference.
3. The inertial sensor-based double kayak upper limb motion capture method of claim 1 or 2, wherein during the measurement process, the tri-axial magnetometer output is corrected using a least square method-based ellipsoid fitting method.
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| CN114533039A (en) * | 2021-12-27 | 2022-05-27 | 重庆邮电大学 | Human body joint position and angle calculating method based on redundant sensors |
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