CN112949084A - Force-feedback-free stress motion capture error correction method - Google Patents
Force-feedback-free stress motion capture error correction method Download PDFInfo
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Abstract
The invention discloses a force-free feedback stress action capture error correction method, which comprises the following steps: step 1: establishing a biomechanical model of the upper limb of the human body through AnyBody simulation software; step 2: obtaining theoretical action parameter change curves of the upper limbs of the human body under different loads through a biomechanics model of the upper limbs of the human body; and step 3: carrying out third-order polynomial fitting on the theoretical action parameter change curve to obtain a linear fitting function of corresponding action; and 4, step 4: and predicting and correcting corresponding motion capture data which is powerless to feed back under different loads through a linear fitting function. The invention can effectively reduce the motion capture error, and the accuracy can meet the accuracy requirement of most tasks.
Description
Technical Field
The invention relates to the technical field of mechanical engineering and human-machine engineering, in particular to a force-bearing action capture error correction method capable of realizing force-free feedback.
Background
The motion capture technology avoids complex human body modeling work, can generate highly vivid motion in real time, and is widely applied to the field of human body motion simulation. However, due to the current force feedback technology development level and the restriction of application cost, the motion capture system under the actual stress condition is generally established on the basis of powerless feedback, a real person cannot feel the real stress in the operation process, the influence of carrying, twisting, pushing and the like on the action of the virtual person cannot be effectively and accurately reflected in the motion capture process, and the result inevitably has errors compared with the real stress action. For specific application fields with high requirements on motion accuracy, such as virtual maintenance, virtual assembly, virtual surgery, etc., the motion errors may cause that subsequent human factor analysis and evaluation results are difficult to meet the use requirements.
For error correction of motion capture, researchers at home and abroad develop a series of research works based on a video method and a kinematics and dynamics method. Grochow et al propose a reverse kinematics method based on physical kinematics characteristics, which combines and applies a global nonlinear dimensionality reduction technology, a Gaussian implicit variable processing model (GPLVM) and a priori kinematics model, and is suitable for correcting similar small-scale human motion data but may not be suitable for large-scale heterogeneous data [2 ]; wolfgang Seemann et al propose a method of projecting observed position, velocity and acceleration on a corresponding constraint manifold to generate a new corrected trajectory, ensuring consistency of motion parameters [3 ]; liang Zhang and G Brunnet et al propose a hybrid method of real-time human motion capture using a simplified marker set and monocular video, perform pose estimation based on marker positions using an improved inverse motion solver, and refine and improve the pose based on video images [4 ]; qiu Shi Guang and the like propose that a virtual human real-time motion control model is established by adopting a dynamic constraint mode based on joint rotation information, a grey system theory is introduced to establish a motion compensation model, and online compensation of missing data is realized [5 ]; shi Xue and the like adopt Motion Builder software to carry out post-processing on the captured original data of the Motion, modify the human joints frame by referring to the actual video Motion, successfully correct the motions of the virtual human ankle joints and the fingers, form a special virtual human body by recording the basic postures of experimenters, and establish a convenient-to-use special Motion library [6 ]; zhang Hongbo et al propose a method of recognizing human body behavior using a Motion Difference Histogram (MDH) as a motion feature descriptor, correcting a motion estimation result by background motion estimation, and encoding a motion difference between a background and an object [7 ].
It can be found that most current motion capture error correction aims at the influences of sensor precision, environmental factors and the like, mainly solves the problem of human body self kinematics under the condition of non-force action, and has less error research on the feedback deficiency of external force. Aiming at the problems, a dynamic method is adopted to carry out human body modeling and action correction, the posture change rule of the human body upper limb joint under different external load conditions is summarized, and a corresponding action compensation correction method is explored.
Disclosure of Invention
The invention aims to provide a force-free feedback force action capturing error correction method to solve the problem that an existing force capturing system is built on the basis of force-free feedback and has an error inevitably.
In order to achieve the purpose, the invention provides the following technical scheme:
a force-free motion capture error correction method comprises the following steps:
step 1: establishing a biomechanical model of the upper limb of the human body through AnyBody simulation software;
and step 3: obtaining theoretical action parameter change curves of the upper limbs of the human body under different loads through a biomechanics model of the upper limbs of the human body;
and 4, step 4: carrying out third-order polynomial fitting on the theoretical action parameter change curve to obtain a linear fitting function of corresponding action;
and 5: and predicting and correcting corresponding motion capture data which is powerless to feed back under different loads through a linear fitting function.
Preferably, the biomechanical model of the human upper limb is established by AnyBody simulation software, comprising:
step 11: establishing a human upper limb structure model, wherein the human upper limb structure model comprises a shoulder joint, an elbow joint, a wrist joint and a lumbar vertebra joint;
step 12: based on the human upper limb structure model, human body and environment models are defined in an AnyScript script of AnyBody simulation software, so that a biomechanics model of the human upper limb is obtained.
Preferably, the human body and environment model is defined in an AnyScript script of AnyBody simulation software, including: and editing the skeleton size, inertia parameters, kinetic parameters and biological parameters of the human body.
Preferably, the defining human body and environment models in the AnyScript script of AnyBody simulation software according to the characteristics of the experimenter and the maintenance task includes: and editing the skeleton size and inertia parameters, the action type, the action path, the task duration and the external stress state of the human body.
Preferably, the obtaining of the change curve of the theoretical motion parameters of the upper limb of the human body under different loads through the biomechanical model of the upper limb of the human body comprises: the AnyBody simulation software drives a human body to execute actions based on a muscle system of a Hill model of the AnyBody simulation software, calculates the change rule of joint angles according to Euler angle transformation between different coordinate systems, solves the muscle force and moment by a forward or reverse dynamic Lagrangian method, and displays a theoretical action parameter change curve after the simulation is finished.
Preferably, the theoretical motion parameter variation curve is a variation curve of the flexion-extension angle of the elbow joint along with the change of the load.
By adopting the technical scheme, the invention has the following technical effects:
for the carrying loads with different masses, the correction action based on the dynamic model calculation is closer to the reality than the capturing result fed back without force, the action capturing error can be effectively reduced, the accuracy can meet the precision requirement of most tasks, and the action capturing error correction method is physically clear, low in solving difficulty and low in popularization and application, and is suitable for wide popularization and application.
Drawings
FIG. 1 is a flowchart of a force feedback-free method for correcting an error in a force capture according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a model of a human upper limb structure according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the relationship between the flexion-extension angle of the right elbow joint and the variation of the load mass according to the theoretical calculation provided by the embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the variation of the flexion-extension angle of the right elbow joint with the load mass according to the motion capture provided by the embodiment of the present invention;
fig. 5 is a schematic diagram illustrating comparison between the correction result and the actual capture result according to the embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and embodiments:
as shown in fig. 1, a method for correcting force-less motion capture errors without force feedback includes:
step 1: and establishing a biomechanical model of the upper limb of the human body through AnyBody simulation software.
The method comprises the following steps:
step 11: establishing a human upper limb structure model, wherein the human upper limb structure model comprises a shoulder joint, an elbow joint, a wrist joint and a lumbar vertebra joint;
step 12: based on the human upper limb structure model, human body and environment models are defined in an AnyScript script of AnyBody simulation software, so that a biomechanics model of the human upper limb is obtained.
Preferably, the human body and environment models are defined in an AnyScript script of AnyBody simulation software, and comprise: and editing the skeleton size, inertia parameters, kinetic parameters and biological parameters of the human body.
Specifically, establishing a human upper limb structure model specifically comprises: integrating a human body measurement model and a biomechanics model, simplifying main movable joints of a human body into 11 joints including a clavicle, a shoulder, an elbow, a wrist, a hip, a thoracic vertebra and a lumbar vertebra, and establishing a simplified structure model of the upper half of the human body; on the basis, the cervical vertebra, the left and right clavicle joints are ignored, and the thoracic vertebra and the lumbar vertebra joints are combined to obtain a further simplified model as shown in fig. 2, wherein the simplified upper limb structure model of the human body comprises shoulder joints, elbow joints, wrist joints and lumbar vertebra joints.
Based on a human upper limb structure model, according to characteristics of a tester and a maintenance task, a human body and an environment model are defined in an AnyScript script of AnyBody simulation software, and skeleton size, inertia parameters, action types, action paths, task duration and external stress states of the human body are edited, so that a biomechanics model of the human upper limb is obtained. The AnyBody software is modeling simulation analysis software based on ergonomics and biomechanics, and can calculate the biomechanics response characteristics of a human body to the environment. An integral human skeletal muscle system is established by AnyBody based on anatomy and Hill model, dynamic parameters such as muscle force and moment are solved based on Lagrange method, and the muscle recruitment optimization criterion of the algorithm biomechanics and the Lagrange dynamics derivation result are internationally verified and approved. The AnyBody can obtain the static and dynamic kinematic, dynamic and biomechanical parameter indexes of all skeletal muscle system models including bones, muscles and joints in single simulation, and visually displays the parameters through a visual model.
Step 2: obtaining theoretical action parameter change curves of the upper limbs of the human body under different loads through a biomechanics model of the upper limbs of the human body;
specifically, the method comprises the following steps: the AnyBody simulation software drives a human body to execute actions based on a muscle system of a Hill model of the AnyBody simulation software, calculates the change rule of joint angles according to Euler angle transformation between different coordinate systems, solves the muscle force and moment by a forward or reverse dynamic Lagrangian method, and displays a theoretical action parameter change curve after the simulation is finished.
Preferably, the theoretical motion parameter variation curve is a variation curve of the flexion-extension angle of the elbow joint along with the change of the load.
Specifically, the change relation of the angle of the right elbow joint along with the weight of the box body is selected as a research object, a space rectangular coordinate system is established by taking the midpoint of a double-foot connecting line as an original point, and the front direction, the right direction and the upper direction of the human body are respectively the positive directions of an x axis, a y axis and a z axis. The external dimension of the carried heavy object is 0.4 multiplied by 0.2m, and the mass center of the carried heavy object is positioned at the center of the geometric solid. Setting the initial position of the mass center of the weight as (0.5, -0.2, 1.3), and the moving direction is horizontally towards the right and approximately at a constant speed, and the unit is m. The carrying duration is 1s, and the transverse displacement distance of the mass center of the weight is 0.4 m.
The calculation was performed in six cases, i.e., a conveying load value m of 0, a conveying load value m of 1kg, a conveying load value m of 2.5kg, a conveying load value m of 5kg, a conveying load value m of 10kg, and a conveying load value m of 20 kg. The external force applied to the hand is F-mg/2 (g-9.81 m/s2), and the direction is always vertical downwards. In the course of action, the palm always keeps close to the object to be transported, so that the posture change of the upper limb caused by the angle change of the wrist joint can be approximately ignored. According to MATLAB-based human upper limb movement analysis and simulation, the calculation method of each main kinetic parameter of the upper half of the human body is shown in Table 1.
Table 1 human upper limb kinetic parameter calculation formula (unit: m, kg): H. w is height and weight respectively
| Length of | Position of center of gravity | Weight (D) | Radius of rotation | Moment of inertia | |
| Front arm | L1＝0.157H | O1＝0.43L1 | W1＝0.018W | R1＝0.526L3 | I1＝W1×R1 2 |
| Upper arm | L2＝0.172H | O2＝0.436L2 | W2＝0.0375W | R2＝0.542L3 | I2＝W2×R2 2 |
| Trunk | L3＝0.3H | O3＝0.66L3 | W3＝0.5804W | R3＝0.837L3 | I3＝W3×R3 2 |
In this example, the height H of the subject was 1.80m, and the weight W was 70 kg. The parameters of the corresponding upper limb obtained by calculation are as follows:
to simplify the calculation process, it is assumed that the lower limb portion of the human body remains stationary while ignoring the influence of the clavicular joint. And determining the motion of the coordinate system of each body joint relative to the base coordinate system by adopting coordinate transformation and a rotation matrix. The initial rotation angles and constraint intervals of the main joints relative to the origin of the coordinate system of the main joints in the static state are shown in table 2.
TABLE 2 initial Angle values and constraint intervals (units: °) of the Main joints
Inputting the dynamic parameters of the upper limbs of the human body, the initial rotation angles and the constraint intervals of the origin points of the coordinate systems of the main joints in the relative static state into AnyBody simulation software to obtain a change curve of the flexion and extension angles of the right elbow joints along with the load mass, dividing the conveying action in a change curve 1s into 16 steps, recording theoretical calculation results of the joint postures along with the load value change in different action stages (the 1 st, 4 th, 7 th, 10 th, 13 th and 16 th steps), and drawing a scatter diagram as shown in figure 3.
The human motion capture experiment and verification are carried out by adopting a Perception Neuron inertial motion capture system, the horizontal carrying motion of the experimenter is captured according to the same load group, the carrying motion within 1s is also divided into 16 steps, the posture angle data of the right elbow joint in the corresponding frame obtained by a plurality of times of motion capture experiments is recorded, and a curve is drawn as shown in figure 4.
By observing the joint flexion and extension angle trajectory curves obtained by the action capture experiment and theoretical calculation respectively, the following common laws can be found:
with the increase of the weight of the carrying load, the flexion and extension angles of the right elbow joint show a descending trend in different stages; when the load value is large (10kg and 20kg groups), the deviation of the flexion-extension angle of the right elbow joint compared with the unloaded state generally exceeds 10 degrees and can reach 30-40 degrees at most in multiple stages of the carrying action, the influence caused by the deviation cannot be ignored, and the right elbow joint needs to be reasonably corrected; the calculation result obtained according to the dynamic model and the actual motion capture result have similar change rules under different loads, and the calculation result and the actual motion capture result are further close to be matched with each other along with the increase of the loads.
Based on this, it can be considered that the dynamics calculation result is accurate enough, that is, the motion capture result under the condition of powerless feedback can be replaced by the motion capture result under the actual stress state by correcting the motion capture result under the condition of powerless feedback.
And step 3: carrying out third-order polynomial fitting on the theoretical action parameter change curve to obtain a linear fitting function of corresponding action;
and 4, step 4: and predicting and correcting corresponding motion capture data which is powerless to feed back under different loads through a linear fitting function.
Specifically, a plurality of groups of curves obtained through theoretical calculation in fig. 3 are observed, and a third-order polynomial is used to fit the curves, so that linear fitting functions corresponding to the curves are obtained as follows:
according to the results obtained by the above formula, the flexion and extension angles of the right elbow joint corresponding to different loads at each stage can be predicted and corrected.
The correction method is verified by taking m as 7.5kg and m as 15kg as examples respectively, formula (1) is replaced, and the flexion and extension angles of the right elbow joint at different stages are calculated as follows:
real-person motion capture was performed by setting the carrying loads to 7.5kg and 15kg, and the captured files were saved and the corresponding data were recorded as shown in table 3.
Table 3 right elbow flexion and extension angle capture results corresponding to m-7.5 kg and m-15 kg
A scatter diagram is drawn based on the equations (2) and (3) and the corresponding actual motion capture data, as shown in fig. 5. Wherein NF-CAP is the unloaded motion capture result, and CAP and COR respectively represent the actual capture result and the correction result based on kinetic calculation.
Comparing the actual captured result with the theoretical corrected result, the corresponding standard deviation is s13.138(m 7.5kg) and s2＝2.48(m＝15kg)。
Defining the relative error of the action as:
in the formula (4), θiAnd theta i is a fitting correction result based on dynamic calculation corresponding to the actual stress motion capture result (i is 1, 4, 7, 10, 13 and 16) at a certain moment. If λ<0.05, the relative error is considered to be small enough, i.e. the fitting correction result has sufficient accuracy.
The data were substituted to obtain relative errors λ 1 ═ 0.0333(m ═ 7.5kg) and λ 2 ═ 0.0368(m ═ 15kg), respectively.
In summary, for the carrying loads with different masses, the correction actions based on the dynamic calculation are closer to the actual actions than the capturing results fed back without force, that is, the action capturing errors can be effectively reduced by adopting the dynamic correction method; the correction result based on the dynamic calculation approximately falls on the corresponding actual capturing action curve, the maximum angle error at a single moment does not exceed 6 degrees, and the accuracy of the correction result can meet the accuracy requirement of most tasks.
The foregoing is merely an example of the present invention and common general knowledge in the art of designing and/or characterizing particular aspects and/or features is not described in any greater detail herein. It should be noted that, for those skilled in the art, without departing from the technical solution of the present invention, several variations and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (5)
1. A force-free motion capture error correction method is characterized by comprising the following steps:
step 1: establishing a biomechanical model of the upper limb of the human body through AnyBody simulation software;
step 2: obtaining theoretical action parameter change curves of the upper limbs of the human body under different loads through a biomechanics model of the upper limbs of the human body;
and step 3: carrying out third-order polynomial fitting on the theoretical action parameter change curve to obtain a linear fitting function of corresponding action;
and 4, step 4: and predicting and correcting corresponding motion capture data which is powerless to feed back under different loads through a linear fitting function.
2. The force-free motion capture error correction method of claim 1, wherein the establishing of the biomechanical model of the upper limb of the human body by the AnyBody simulation software comprises:
step 11: establishing a human upper limb structure model, wherein the human upper limb structure model comprises a shoulder joint, an elbow joint, a wrist joint and a lumbar vertebra joint;
step 12: based on the human upper limb structure model, human body and environment models are defined in an AnyScript script of AnyBody simulation software, so that a biomechanics model of the human upper limb is obtained.
3. The force-feedback-free stress motion capture error correction method of claim 2, wherein the definition of the human body and environment model in the AnyScript of AnyBody simulation software comprises: and editing the skeleton size, inertia parameters, kinetic parameters and biological parameters of the human body.
4. The method for correcting capturing errors of force-free motion according to claim 3, wherein the obtaining of the change curve of the theoretical motion parameters of the upper limbs of the human body under different loads through the biomechanical model of the upper limbs of the human body comprises: the AnyBody simulation software drives a human body to execute actions based on a muscle system of a Hill model of the AnyBody simulation software, calculates the change rule of joint angles according to Euler angle transformation between different coordinate systems, solves the muscle force and moment by a forward or reverse dynamic Lagrangian method, and displays a theoretical action parameter change curve after the simulation is finished.
5. The force-feedback-free force capture error correction method as claimed in any one of claims 1 to 4, wherein the theoretical motion parameter variation curve is a variation curve of the flexion-extension angle of the elbow joint with the load.
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