CN114565978B - Joint rotation center optimization method and device based on motion capture point cloud - Google Patents

Joint rotation center optimization method and device based on motion capture point cloud Download PDF

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CN114565978B
CN114565978B CN202210457321.9A CN202210457321A CN114565978B CN 114565978 B CN114565978 B CN 114565978B CN 202210457321 A CN202210457321 A CN 202210457321A CN 114565978 B CN114565978 B CN 114565978B
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蒯声政
张睿
陈姿宇
颜滨
周文钰
李文翠
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Shenzhen Second Peoples Hospital
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Abstract

A joint rotation center optimization method and equipment based on motion capture point cloud belong to the technical field of motion capture and human muscle and bone modeling. The method aims to solve the problem that joint motion parameters calculated based on the mark points are sensitive to sticking point errors and have large errors. The method comprises the steps of marking point data under the relative static state and the relative motion state of a first bone and a second bone which are respectively connected with an optimized joint based on collected optimized joints, overlapping the marking point data under the relative motion state of the first bone to the marking point data under the relative static state, and converting a motion track of the marking point data into the rotation motion of the first bone which keeps still and the second bone around the first bone through the optimized joint based on the corresponding rotation translation relation; and (3) the second bone mark point of the current frame is superposed on the mark point corresponding to the second bone of the previous frame, and the rotation centers of all the two adjacent frames of the rotation and translation matrixes in the complete gait cycle are converted into an optimization problem to realize optimization. The method is mainly used for optimizing the joint rotation center.

Description

Joint rotation center optimization method and equipment based on motion capture point cloud
Technical Field
The invention specifically designs a joint rotation center optimization method, and belongs to the technical field of motion capture and human muscle and bone modeling.
Background
Motion capture and musculoskeletal modeling are the most common techniques used for human motion characterization. At present, the method is widely applied to the fields of sports performance evaluation, orthopedic disease function evaluation, movie animation production and the like. The evaluation is mainly implemented by sticking an optical reflective ball on a bony marker of a human body and capturing the three-dimensional space coordinate of the reflective ball through a motion capture system, so that parameters such as joint motion angle and the like are calculated according to the space coordinate of the reflective ball. The joint angle is calculated mainly in two modes, one mode is based on the connecting line of the mark points, and the included angle between line segments is calculated; and the other method is to drive a motion model (such as an Anybody or opensmim structure model) with preset joint constraints by using the optical mark points so as to calculate the joint angle. The first mode is simple and convenient, but the calculation method completely based on the marking points is very sensitive to the errors of the pasting positions of the marking points, and is easy to cause larger deviation of joint angle calculation. The second method is relatively complex, the calculation process is closer to the real motion of the human body, but the central point of each joint needs to be preset in the modeling process (realized by adjusting the length of the body segment and the rotation angle of the joint). The traditional joint center point presetting method generally adopts the midpoint of optical mark points attached to the inner side and the outer side of a joint when the joint is statically stood as the preset joint center point. Therefore, the error of the sticking point directly affects the preset joint central point, thereby affecting the calculation accuracy of the joint angle. Therefore, how to reduce the influence caused by the pointing error is the key to improve the effectiveness of the motion estimation.
Disclosure of Invention
The invention provides a joint rotation center optimization method, a storage medium and equipment based on motion capture point cloud, aiming at solving the problem that joint motion parameters calculated based on mark points are sensitive to sticking point errors and have large errors.
The joint rotation center optimization method based on motion capture point cloud comprises the following steps:
based on collected mark point data of an optimized joint and a first bone and a second bone which are respectively connected with the optimized joint in a relative static state and a relative motion state, the mark point data of the first bone in the relative motion state is superposed on the mark point data of the first bone in the relative static state, and a motion track of the mark point data is converted into the rotation motion of the first bone which keeps still and the second bone around the first bone through the optimized joint based on the corresponding rotation translation relation;
the second bone mark point of the current frame is superposed on the mark point corresponding to the second bone of the previous frame, and a rotation and translation matrix is determined
Figure 663901DEST_PATH_IMAGE001
And obtaining the coordinates of a point P of the rotation center; calculating the rotation center P of all adjacent two frames of rotation and translation matrixes in the complete gait cycle
Figure 594948DEST_PATH_IMAGE002
As an optimal target, converting the optimal target into an optimization problem, and further realizing the optimization of the joint rotation center; wherein, I is a unit matrix, and n is the frame number in the complete gait cycle.
Further, will
Figure 349277DEST_PATH_IMAGE002
The optimization problem that is the optimal target and is transformed into is as follows:
Figure 273240DEST_PATH_IMAGE003
Figure 796625DEST_PATH_IMAGE004
the joint optimization method comprises the following steps of A, calculating P _ x, P _ y and P _ z, wherein the P _ x, P _ y and P _ z are coordinate values of a P point of a rotation center, the P _ marker is a central position point of a mark point corresponding to an optimization joint, and the P _ marker _ x, P _ marker _ y and P _ marker _ z are coordinates of the P _ marker; the optimized range of offset, and offset _ x, offset _ y, and offset _ z are coordinates of the optimized range.
Further, the process of acquiring the coordinates of the point P of the rotation center is realized by numerical calculation.
Furthermore, the process of superposing the mark point data in the relative motion state of the first bone on the mark point data in the relative rest state is realized by using a mark point set superposition algorithm.
Further, the process of overlapping the second bone mark point of the current frame to the mark point corresponding to the second bone of the previous frame is realized by using a mark point set overlapping algorithm.
Preferably, the marked point set superposition algorithm iterates the closest point algorithm.
Preferably, the mark points are mark points obtained after the optical mark points arranged on the human body part corresponding to the optimized joint, the first bone connected with the optimized joint and the second bone respectively connected with the optimized joint are captured by the motion capture system.
Preferably, the number of the marking points of the optimized joint in the relative static state is at least two; the number of the marking points of the first bone connected with the optimized joint and the second bone connected with the optimized joint in the relative static state is at least three respectively.
A storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the motion capture point cloud based joint center of rotation optimization method.
The device comprises a processor and a memory, wherein the memory stores at least one instruction which is loaded and executed by the processor to realize the joint rotation center optimization method based on the motion capture point cloud.
Has the advantages that:
the invention provides a method for optimizing a joint rotation center, which aims at the problem that joint motion parameters calculated based on mark points are sensitive to sticking point errors. The method integrates static information and dynamic information, takes the static information as a reference, and eliminates the dependence of the calculation of the knee joint center on mark points inside and outside the knee joint by optimizing the joint rotation center in the dynamic process, thereby reducing the influence of a point pasting error on the calculation of joint motion parameters. From the results of fig. 2, it can be seen that the flexion, lateral flexion and rotation angles of the knee joint calculated by using the optimized rotation center as the joint center all change compared with the conventional method, which indicates that the calculation result is obviously affected by using different joint centers. As can be seen from the anatomy of the knee joint, the knee joint does not rotate about a single center during motion, but rather varies continuously. Therefore, in the conventional joint center calculated based on bony landmark points, the error between the model point (point on the bone) and the driving point (point acquired by motion capture) may vary greatly according to the bending angle of the joint. From the effect of fig. 3, it can be found that the optimized rotation center is used as a joint center driving muscle bone model, the deviation between the model point and the driving point is more uniform in the dynamic process, and the overall error is smaller. Therefore, the joint motion parameters calculated by adopting the optimized rotation center of the invention as the preset joint central point of the model can be closer to the real data.
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FIG. 1 is a schematic diagram comparing a traditional knee joint patch-based calculated joint center point with a center point calculated by the optimization method of the present invention;
FIG. 2 is a schematic view of knee joint movement angles of two different knee joint rotation center calculation methods;
FIG. 3 is a graph of the error between four model points and drive points for two different methods during a gait cycle;
FIG. 4 is a graph showing the comparison of the error between each model point and the driving point for two different methods during a gait cycle.
Detailed Description
The first specific implementation way is as follows:
the embodiment is a joint rotation center optimization method based on motion capture point cloud, the invention combines data of two states of static standing and dynamic walking at the same time, the joint rotation center is optimized, two bones connected with the optimized joint are respectively marked as a first bone and a second bone, taking a knee joint as an example for explanation, and the first bone and the second bone are respectively a thigh leg bone and a shank leg bone.
It should be noted that the present invention is not limited to the knee joint, but is applicable to other joints such as hip joint, shoulder joint, elbow joint, etc. In the case of an elbow joint, the first and second bones are the upper and lower arm bones, respectively.
A joint rotation center optimization method based on motion capture point cloud taking a knee joint as an example comprises the following steps:
and S1, pasting at least two optical mark points on the human body part corresponding to the first bone and the second bone.
In the present embodiment, for the knee joint, the parts of the human body corresponding to the first bone and the second bone are the thigh and the calf, and 4 optical markers are respectively adhered to the thigh and the calf, wherein the markers on the thigh are LTHI _ U, LTHI _ D, LTHI _ F, LTHI _ B, and the markers on the calf are LTIB _ U, LTIB _ D, LTIB _ F, LTIB _ B. 2 marking points LKNE _ I and LKNE _ O are respectively pasted on the inner side and the outer side of the knee joint, and are respectively shown in figure 1.
It should be noted that the present invention includes, but is not limited to, acquiring data of the first bone and the second bone in the relative static state and the relative motion state by pasting the optical mark points. Meanwhile, in the present embodiment, 4 optical mark points are respectively attached to the thigh and the calf, and actually, 3 optical mark points or more than 4 optical mark points may be attached.
And S2, respectively capturing the space coordinates of the mark points of the 10 optical mark points in the static standing and dynamic walking processes through a motion capture system. The four points LTHI _ U, LTHI _ D, LTHI _ F, LTHI _ B on the thigh in the dynamic process are overlapped to the four points LTHI _ U, LTHI _ D, LTHI _ F, LTHI _ B on the static thigh, a rotation and translation matrix T1 is constructed through a marking point set overlapping algorithm, so that the motion track of 10 points in the walking process is converted into that the thigh keeps still, and the four points LTIB _ U, LTIB _ D, LTIB _ F, LTIB _ B of the shank rotate around the thigh through the knee joint. The marked Point set superposition algorithm in the embodiment is an ICP (Iterative Closest Point) algorithm, but the marked Point set superposition algorithm may not be limited to the ICP algorithm.
S3, constructing a rotation and translation matrix T through ICP (inductively coupled plasma) and other algorithms current_frame Four points of the shank LTIB _ U, LTIB _ D, LTIB _ F, LTIB _ B of the current frame are overlapped to four points of the shank LTIB _ U, LTIB _ D, LTIB _ F, LTIB _ B of the previous frame. Theoretically, the rotation centers P of two adjacent images should be consistent withT current_frame* P = P, i.e. the point that remains unchanged before and after the rotation transformation. However, in practical application, when the change amplitude of two adjacent frames is small, T is current_frame The calculation error of (2) becomes large, and the calculated P point also deviates greatly. Therefore, the invention adopts another method to carry out optimization, namely, the coordinates of the point P of the rotation center are obtained by a numerical calculation method:
will T current_frame* P = P to (T) current_frame - I） * P =0, where I is the identity matrix; theoretically, the rotation center P should satisfy (T) current_frame - I） * P =0, considering the actual error, the invention calculates the rotation center P of all the adjacent two frames of rotation and translation matrixes in the complete gait cycle, and the rotation center P is used for calculating the rotation and translation matrixes of all the adjacent two frames
Figure 226469DEST_PATH_IMAGE005
As an optimal target; wherein, the point P is the rotation central point of the same joint needing to be optimized, and the coordinate of the point P is recorded as [ P _ x, P _ y, P _ z, 1%]. Therefore, the calculation method of P transforms the objective optimization problem, which can be calculated by a numerical optimization function (e.g., fmicon or minize) of MATLAB or Python. Considering the actual geometrical significance, the calculated rotation center point can not deviate from the knee joint mark point too far, and further the solution method of P is converted into an optimization problem with constraint conditions, namely:
Figure 468095DEST_PATH_IMAGE003
Figure 680902DEST_PATH_IMAGE004
the P _ marker is a midpoint between LKNE _ I and LKNE _ O, the offset is a designated optimization range, and the offset _ x, the offset _ y, and the offset _ z are coordinates of the optimization range, respectively.
In this embodiment, according to the mark point pasting method shown in fig. 1, taking a knee joint as an example, the knee joint pastes 2 mark points on the medial and lateral condyles of a bone, and pastes 4 mark points on the middle of a thigh and the middle of a calf, respectively. And respectively capturing the space coordinate tracks of the 10 points in the standing calibration posture and the walking process through a MotionAnalysis motion capture system. The rotation center coordinate of the knee joint is calculated by the rotation center optimization method. Setting the optimized rotation center coordinate as the rotation center of the musculoskeletal model knee joint, and driving the thigh and the calf to move by using the 4 mark points of the thigh and the 4 mark points of the calf respectively, thereby calculating the movement angle of the knee joint in the walking process, as shown in fig. 3 and 4.
The second embodiment is as follows:
the present embodiment is a storage medium having at least one instruction stored therein, which is loaded and executed by a processor to implement the method for optimizing a joint rotation center based on motion capture point cloud.
It should be understood that any method described herein, including any methods described herein, may correspondingly be provided as a computer program product, software, or computerized method, which may include a non-transitory machine-readable medium having stored thereon instructions, which may be used to program a computer system, or other electronic device, to perform a process. Storage media may include, but is not limited to, magnetic storage media, optical storage media; a magneto-optical storage medium comprising: read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM), and flash memory layers; or other type of media suitable for storing electronic instructions.
The third concrete implementation mode:
the embodiment is a joint rotation center optimization device based on motion capture point cloud, the device comprises a processor and a memory, it should be understood that any device comprising the processor and the memory described in the present invention may also comprise other units and modules which perform display, interaction, processing, control and other functions through signals or instructions;
the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the motion capture point cloud based joint center of rotation optimization method.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it is therefore intended that all such changes and modifications be considered as within the spirit and scope of the appended claims.

Claims (10)

1. The joint rotation center optimization method based on motion capture point cloud is characterized by comprising the following steps:
based on the collected mark point data of the optimized joint and the first bone and the second bone which are respectively connected with the optimized joint in the relative static state and the relative motion state, the mark point data of the first bone in the relative motion state is superposed on the mark point data in the relative static state, and the motion track of the mark point data is converted into the rotation motion of the first bone which is kept still and the second bone around the first bone through the optimized joint based on the corresponding rotation translation relation;
the second bone mark point of the current frame is superposed on the mark point corresponding to the second bone of the previous frame, and a rotation and translation matrix T is determined current_frame And obtaining the coordinate of a point P of the rotation center; calculating the rotation center P of all the two adjacent frames of the rotation and translation matrix in the complete gait cycle
Figure DEST_PATH_IMAGE001
As an optimal target, converting the optimal target into an optimization problem, and further realizing the optimization of the joint rotation center; where I is the identity matrix and n is the number of frames in the complete gait cycle.
2. The motion capture point cloud based joint rotation center optimization method of claim 1, wherein the motion capture point cloud is to be used
Figure 691113DEST_PATH_IMAGE002
The optimization problem that is the optimal target and is transformed into is as follows:
Figure DEST_PATH_IMAGE003
the joint optimization method comprises the following steps of A, calculating P _ x, P _ y and P _ z, wherein the P _ x, P _ y and P _ z are coordinate values of a P point of a rotation center, the P _ marker is a central position point of a mark point corresponding to an optimization joint, and the P _ marker _ x, P _ marker _ y and P _ marker _ z are coordinates of the P _ marker; offset is an optimized range, and offset _ x, offset _ y, and offset _ z are coordinates of the optimized range, respectively.
3. The method for optimizing the joint rotation center based on motion capture point cloud of claim 2, wherein the process of obtaining the coordinates of the point P of the rotation center is realized by numerical calculation.
4. The method of claim 3, wherein the registration of the marker point data in the relative motion state of the first bone to the marker point data in the relative rest state is performed by using a marker point set registration algorithm.
5. The method of claim 4, wherein the process of overlapping the second bone marker point of the current frame onto the marker point corresponding to the second bone of the previous frame is implemented by using a marker point set overlapping algorithm.
6. The motion capture point cloud based joint rotation center optimization method of claim 5, wherein the marker point set coincidence algorithm is an iterative closest point algorithm.
7. The method as claimed in any one of claims 1 to 6, wherein the markers are optical markers disposed on the body part corresponding to the optimized joint, the first bone connected to the optimized joint, and the second bone connected to the optimized joint, respectively, captured by the motion capture system.
8. The method of claim 7, wherein the number of markers for optimizing the joint in a relatively static state is at least two; the number of the marking points of the first bone connected with the optimized joint and the second bone connected with the optimized joint in the relative static state is at least three respectively.
9. A storage medium having stored therein at least one instruction loaded and executed by a processor to implement a motion capture point cloud based joint rotation center optimization method according to one of claims 1 to 8.
10. Motion capture point cloud based joint rotation center optimization apparatus, comprising a processor and a memory, wherein the memory has stored therein at least one instruction, which is loaded and executed by the processor to implement the motion capture point cloud based joint rotation center optimization method according to one of claims 1 to 8.
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