CN112949084B - Force action capturing error correction method based on weak feedback - Google Patents
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Abstract
The invention discloses a force action capturing error correction method for weak feedback, 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 a theoretical action parameter change curve of the upper limb of the human body under different loads through a biomechanical model of the upper limb of the human body; step 3: performing third-order polynomial fitting on the theoretical action parameter change curve to obtain a linear fitting function of the corresponding action; step 4: and predicting and correcting corresponding motion capture data which cannot be fed back under different loads through a linear fitting function. The invention can effectively reduce motion capture errors, and the accuracy of the motion capture errors can meet the accuracy requirements of most tasks.
Description
Technical Field
The invention relates to the technical fields of mechanical engineering and human engineering, in particular to a force action capturing error correction method with weak feedback.
Background
The motion capture technology avoids complex human modeling work, can generate highly realistic motions in real time, and is widely applied to the field of human motion simulation. However, due to the limitations of the current force feedback technology in the development level and application cost, the motion capture system under the actual stress condition is generally built on the basis of the weak feedback, the real person cannot feel the real stress in the operation process, the motion capture process cannot effectively and accurately reflect the influence of carrying, torsion, pushing and the like on the motion of the virtual person, and the result inevitably has errors compared with the real stress motion. For specific application fields such as virtual maintenance, virtual assembly, virtual surgery and the like with high requirements on action precision, the action errors can cause that the subsequent human 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 video methods and kinematic and dynamic methods. Grochow et al propose a reverse kinematics method based on physical kinematics features, which combines and applies a global nonlinear dimension reduction technique, a Gaussian implicit variable processing model (Gaussian process latent variable model, GPLVM) and a priori kinematics model, is suitable for correcting small-scale human motion data of the same type, but may not be suitable for heterogeneous data [2] with larger scale; wolfgang Seemann et al propose a method of generating a corrected new trajectory by projecting the observed position, velocity and acceleration on the corresponding constraint manifold, ensuring the consistency of the motion parameters [3]; liang Zhang and G Brunett et al propose a hybrid approach to real-time human motion capture using a reduced marker set and monocular video, using an improved inverse motion solver to estimate pose based on marker position, and refine and improve pose based on video images [4]; shian et al propose to set up a real-time motion control model of a virtual person based on joint rotation information by adopting a dynamic constraint mode, and introduce a gray system theory to set up an action compensation model so as to realize online compensation of missing data [5]; the method comprises the steps that Motion Builder software is adopted by Shi Xue and the like to carry out post-processing on Motion captured original data, actual video Motion is referred to modify human joints frame by frame, motions of virtual human ankle joints and fingers are successfully modified, a special virtual human body is formed by recording basic gestures of experimenters, and a special Motion library [6] convenient to use is built; zhang Hongbo et al propose a method of recognizing human behavior using a motion difference histogram (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 the current motion capture error correction is mostly aimed at the influence of sensor precision, environmental factors and the like, mainly solves the problem of the kinematics of the human body under the condition of non-force action, and has less error research caused by the loss of external force feedback. Aiming at the problems, the dynamic method is adopted to perform human modeling and motion correction, and the posture change rules of the joints of the upper limbs of the human body under different external load conditions are summarized, so that the corresponding motion compensation correction method is explored.
Disclosure of Invention
The invention aims to provide a force action capturing error correction method for weak feedback, so as to solve the problem that the existing capturing system is inevitably provided with errors on the basis of the weak feedback.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a force action capturing error correction method of weak feedback comprises the following steps:
step 1: establishing a biomechanical model of the upper limb of the human body through AnyBody simulation software;
step 3: obtaining a theoretical action parameter change curve of the upper limb of the human body under different loads through a biomechanical model of the upper limb of the human body;
step 4: performing third-order polynomial fitting on the theoretical action parameter change curve to obtain a linear fitting function of the corresponding action;
step 5: and predicting and correcting corresponding motion capture data which cannot be fed back under different loads through a linear fitting function.
Preferably, the establishment of the biomechanical model of the upper limb of the human body by AnyBody simulation software comprises:
step 11: establishing a human body upper limb structure model, wherein the human body upper limb structure model comprises shoulder joints, elbow joints, wrist joints and lumbar joints;
step 12: based on the structural model of the upper limb of the human body, defining the human body and the environmental model in an AnyScript script of Anybody simulation software, thereby obtaining a biomechanical model of the upper limb of the human body.
Preferably, the defining the human body and environment model in the AnyScript script of the Anybody emulation software includes: and editing the skeleton size, inertia parameters, kinetic parameters and biological parameters of the human body.
Preferably, the defining the human body and environment model in the AnyScript script of the AnyBody simulation software according to the characteristics of the testers and the maintenance tasks 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 method for obtaining the theoretical action parameter change curve of the upper limb of the human body under different loads by using the biomechanical model of the upper limb of the human body comprises the following steps: anybody simulation software drives a human body to execute actions based on a muscle system of a Hill model of the Anybody simulation software, a change rule of joint angles is calculated according to Euler angle transformation among different coordinate systems, muscle force and moment are solved through a Lagrange method of forward or reverse dynamics, and a theoretical action parameter change curve is displayed after simulation is finished.
Preferably, the theoretical motion parameter change curve is a Qu Shenjiao degree elbow joint change curve with load.
By adopting the technical scheme, the invention has the following technical effects:
for carrying loads with different qualities, the correction action calculated based on the dynamic model is closer to reality than the capture result fed back by weakness, the action capture error can be effectively reduced, the accuracy can meet the accuracy requirements of most tasks, and the action capture error correction method is clear in physics, low in solving difficulty and suitable for wide popularization and application.
Drawings
FIG. 1 is a flowchart of a force action capturing error correction method with weak feedback according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a human upper limb structure model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing the relationship between the bending and stretching angle of the right elbow joint and the load mass according to the theoretical calculation provided by the embodiment of the invention;
FIG. 4 is a schematic diagram showing the relationship between the bending and stretching angle of the right elbow joint and the load mass according to the motion capture according to the embodiment of the present invention;
FIG. 5 is a schematic diagram showing the comparison between the corrected result and the actual captured result according to the embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and embodiments:
as shown in fig. 1, a force action capturing error correction method of force feedback comprises the following steps:
step 1: and establishing a biomechanical model of the upper limb of the human body through AnyBody simulation software.
Comprising the following steps:
step 11: establishing a human body upper limb structure model, wherein the human body upper limb structure model comprises shoulder joints, elbow joints, wrist joints and lumbar joints;
step 12: based on the structural model of the upper limb of the human body, defining the human body and the environmental model in an AnyScript script of Anybody simulation software, thereby obtaining a biomechanical model of the upper limb of the human body.
Preferably, the human body and environment model is defined in an AnyScript script of Anybody emulation software, including: and editing the skeleton size, inertia parameters, kinetic parameters and biological parameters of the human body.
Specifically, a human body upper limb structure model is established, specifically: the human body measurement model and the biomechanical model are integrated, the main movable joints of the human body are simplified into 11 joints including collarbone, shoulder, elbow, wrist, hip, thoracic vertebra and lumbar vertebra, and a simplified structure model of the upper half of the human body is established; on the basis, the cervical vertebra, the left and right clavicle joints are ignored, the thoracic vertebra and the lumbar vertebra joints are combined, a further simplified model is obtained, as shown in figure 2, and the simplified upper limb structure model of the human body comprises shoulder joints, elbow joints, wrist joints and lumbar vertebra joints.
Based on the upper limb structure model of the human body, defining a human body and an environment model in an AnyScript script of Anybody simulation software according to the characteristics of a tester and a maintenance task, and editing skeleton size and inertia parameters, action types, action paths, task duration and external stress states of the human body, so as to obtain a biomechanical model of the upper limb of the human body. AnyBody software is modeling simulation analysis software based on ergonomics and biomechanics, and can calculate biomechanics response characteristics of a human body to the environment. AnyBody establishes a complete human skeletal muscle system based on anatomy and Hill models, solves dynamic parameters such as muscle force, moment and the like based on Lagrange method, and obtains extensive verification and acceptance internationally by using a muscle recruitment optimization criterion of algorithm biomechanics and Lagrange dynamics deduction result. AnyBody can obtain static and dynamic kinematic, dynamic and biomechanical parameter indexes of all skeletal muscle system models including bones, muscles and joints in a single simulation, and can be intuitively displayed through a visual model.
Step 2: obtaining a theoretical action parameter change curve of the upper limb of the human body under different loads through a biomechanical model of the upper limb of the human body;
specifically, the method comprises the following steps: anybody simulation software drives a human body to execute actions based on a muscle system of a Hill model of the Anybody simulation software, a change rule of joint angles is calculated according to Euler angle transformation among different coordinate systems, muscle force and moment are solved through a Lagrange method of forward or reverse dynamics, and a theoretical action parameter change curve is displayed after simulation is finished.
Preferably, the theoretical motion parameter change curve is a Qu Shenjiao degree elbow joint change curve along with load change.
Specifically, the change relation of the angle of the right elbow joint along with the weight of the box body is selected as a study object, a space rectangular coordinate system is established by taking the midpoint of the connecting line of the two feet as an origin, and the front direction, the right direction and the upper direction of the human body are respectively positive directions of x, y and z axes. The external dimension of the carrying weight is 0.4 multiplied by 0.2m, and the centroid is positioned at the center of the geometric body. The initial position of the mass center of the weight is set to be (0.5, -0.2,1.3), and the moving direction is horizontal to the right and approximately uniform, and the unit is m. The carrying duration is 1s, and the transverse displacement distance of the mass center of the weight is 0.4m.
The carrying load values are calculated by dividing into six cases of m=0, m=1 kg, m=2.5 kg, m=5 kg, m=10 kg and m=20 kg. The external force applied to the hands is F=mg/2 (g= -9.81m/s 2), and the direction is always vertical downwards. In the action process, the palm center is always kept close to the carried object, so that the posture change of the upper limb caused by the angle change of the wrist joint can be ignored approximately. According to MATLAB-based human body upper limb movement analysis and simulation, the calculation method of each main kinetic parameter of the upper human body is shown in table 1.
Table 1 calculation formula (unit: m, kg) of human upper limb dynamics parameters notes: H. w is height and weight respectively
| Length of | Center of gravity position | Weight of (E) | Radius of rotation | Moment of inertia | |
| Forearm (forearm) | L 1 ＝0.157H | O 1 ＝0.43L 1 | W 1 ＝0.018W | R 1 ＝0.526L 3 | I 1 ＝W 1 ×R 1 2 |
| Upper arm | L 2 ＝0.172H | O 2 ＝0.436L 2 | W 2 ＝0.0375W | R 2 ＝0.542L 3 | I 2 ＝W 2 ×R 2 2 |
| Trunk body | L 3 ＝0.3H | O 3 ＝0.66L 3 | W 3 ＝0.5804W | R 3 ＝0.837L 3 | I 3 ＝W 3 ×R 3 2 |
In this example, the height of the experimenter was h=1.80 m, and the weight w=70 kg. The corresponding parameters of the upper limb are calculated as follows:
to simplify the calculation process, it is assumed that the lower limb portion of the human body remains stationary while ignoring the effects of the clavicle joint. And determining the motion of the self coordinate system of each body segment relative to the base coordinate system by adopting coordinate transformation and a rotation matrix. The initial rotation angle and the constraint interval of each main joint required for calculation relative to the origin of the own coordinate system in the static state are shown in table 2.
TABLE 2 initial values of angles of the main joints and constraint intervals (units: °)
Inputting the dynamic parameters of the upper limbs of the human body, the initial rotation angle of the origin of the coordinate system of each main joint in a relative static state and the constraint interval into AnyBody simulation software to obtain a change curve of the bending and stretching angle of the right elbow joint along with the load mass, dividing the carrying action in the change curve 1s into 16 steps, recording theoretical calculation results of the change of the joint posture along with the load value in different action stages ( steps 1,4,7, 10, 13 and 16), and drawing a scatter diagram as shown in figure 3.
The Perception Neuron inertial motion capturing system is adopted to carry out true human motion capturing experiments and verification, horizontal conveying motions of experimenters are captured according to the same load groups, the conveying motions in 1s are divided into 16 steps, the posture angle data of the right elbow joint in the corresponding frame obtained through multiple motion capturing experiments are recorded, and a curve is drawn as shown in fig. 4.
The joint Qu Shenjiao degree track curve obtained by the motion capture experiment and theoretical calculation is observed respectively, and the following common law can be found:
along with the increase of the weight of the carrying load, the bending and stretching angles of the right elbow joint show a decreasing trend at different stages; when the load value is large (10 kg and 20kg groups), the deflection angle of the right elbow joint is generally more than 10 degrees compared with the deflection of the non-load state in multiple stages of carrying action, the maximum deflection angle can reach 30-40 degrees, the influence caused by the deflection cannot be ignored, and reasonable correction is needed; the calculation result and the actual motion capture result obtained according to the dynamics model have similar change rules under different loads, and the calculation result and the actual motion capture result further approach to be matched with each other along with the increase of the loads.
Based on the above, it can be considered that the dynamics calculation result is accurate enough, that is, the motion capture result under the condition of weak feedback is corrected, so that the dynamics calculation result can replace the motion capture result under the actual stress state.
Step 3: performing third-order polynomial fitting on the theoretical action parameter change curve to obtain a linear fitting function of the corresponding action;
step 4: and predicting and correcting corresponding motion capture data which cannot be fed 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 fitted by using a third-order polynomial, so that a linear fitting function corresponding to each curve is obtained as follows:
according to the result obtained by the formula, the flexion and extension angles of each stage of the right elbow joint corresponding to different loads can be predicted and corrected.
The correction method is respectively verified by taking m=7.5kg and m=15kg as examples, and the correction method is substituted into the formula (1), so that the right elbow joint flexion and extension angles at different stages are calculated as follows:
the real person motion was captured with the transport load set to 7.5kg and 15kg, and the captured file was saved and the corresponding data was recorded as shown in table 3.
Table 3 m=7.5 kg and m=15 kg corresponding right elbow joint Qu Shenjiao degree capturing results
According to the formula (2), the formula (3) and the corresponding actual motion capture data, a scatter diagram is drawn as shown in fig. 5. The NF-CAP is an unloaded motion capture result, and CAP and COR respectively represent an actual capture result and a correction result based on dynamic calculation.
Comparing the actual capturing result with the theoretical correction result, wherein the standard deviation corresponding to the actual capturing result is s 1 = 3.138 (m=7.5 kg) and s 2 ＝2.48(m＝15kg)。
Defining the relative error of the motion as:
in the formula (4), θ i And (3) capturing results (i=1, 4,7, 10, 13 and 16) for actual stressed actions at a certain moment, wherein thetai is a fitting correction result based on corresponding dynamic calculation at the same moment. If lambda is<0.05, the relative error is considered to be sufficiently small, i.e., the fitting correction result is sufficiently accurate.
Substituting the data gives relative errors λ1=0.0333 (m=7.5 kg) and λ2= 0.0368 (m=15 kg), respectively.
In summary, for carrying loads with different masses, the correction actions based on dynamic calculation are closer to the actual actions than the capturing results of the weak feedback, that is, the action capturing errors can be effectively reduced by adopting a dynamic correction method; the correction result based on dynamic calculation approximately falls on the corresponding actual capture action curve, the maximum angle error at a single moment is not more than 6 degrees, and the accuracy can meet the accuracy requirements of most tasks.
The foregoing is merely exemplary of the present invention, and specific technical solutions and/or features that are well known in the art have not been described in detail herein. It should be noted that, for those skilled in the art, several variations and modifications can be made without departing from the technical solution of the present invention, and these should also be regarded as the protection scope of the present invention, which does not affect the effect of the implementation of the present invention and the practical applicability of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (1)
1. A force action capturing error correction method of weak feedback 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 a Qu Shenjiao-degree change curve of elbow joints of the upper limbs of the human body under different loads along with the change of the loads through a biomechanical model of the upper limbs of the human body;
step 3: performing third-order polynomial fitting on a Qu Shenjiao-degree change curve of the elbow joint along with load change to obtain a linear fitting function of corresponding actions;
step 4: predicting and correcting corresponding motion capture data which are fed back in a weak mode under different loads through a linear fitting function;
establishing a biomechanical model of the upper limb of the human body through AnyBody simulation software, wherein the biomechanical model comprises the following steps:
step 11: establishing a human body upper limb structure model, wherein the human body upper limb structure model comprises shoulder joints, elbow joints, wrist joints and lumbar joints;
step 12: based on the structural model of the upper limb of the human body, defining a human body and an environment model in an AnyScript script of AnyBody simulation software so as to obtain a biomechanical model of the upper limb of the human body, wherein the human body and the environment model are defined in the AnyScript script of the AnyBody simulation software, and the method comprises the following steps of: editing skeleton size, inertia parameters, kinetic parameters and biological parameters of a human body;
the method for obtaining the Qu Shenjiao degree change curve of the elbow joint of the upper limb of the human body under different loads along with the change of the loads through the biomechanical model of the upper limb of the human body comprises the following steps: anybody simulation software drives a human body to execute actions based on a muscle system of a Hill model of the Anybody simulation software, a change rule of joint angles is calculated according to Euler angle transformation among different coordinate systems, muscle force and moment are solved through a Lagrangian method of forward or reverse dynamics, and a change curve of Qu Shenjiao degrees of elbow joints along with load change is displayed after simulation is finished.
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