CN113598755B - Human body rhythm motion parametric representation analysis method based on fast Fourier transform - Google Patents
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Abstract
The invention discloses a human rhythm motion parameterization characterization analysis method based on fast Fourier transform, belonging to the field of motion analysis and comprising the following procedures: selecting a tested object to perform a data acquisition experiment, acquiring joint angle change data in human body rhythm movement, establishing a movement database and drawing a movement curve; decomposing each joint motion curve into a plurality of rhythm components with different frequencies through fast Fourier transform to extract the amplitude, frequency and phase parameters of each component; the extracted amplitude, frequency and phase parameters of each component are equivalent to space particle coordinates, and are mapped to the component coordinates of the midpoint in the three-dimensional space, so that the particle distribution representation of the human rhythm cooperative motion is formed; based on the fluid mechanics thought and the McCann translation theory, in the particle distribution corresponding to different groups of data with the same motion, the real-time and accurate analysis of the particle distribution rule corresponding to the human rhythm cooperative motion is realized by using the methods of geodesic connection, high-density sampling, parameter reconstruction comparison and error calculation.
Description
Technical Field
The invention relates to the field of human motion analysis, in particular to a human rhythm motion parametric representation analysis method based on fast Fourier transform.
Background
After a long evolutionary process, human beings have excellent rhythm cooperative motion capability, and the cooperative mechanism between joints and limbs has wide application prospects in the fields of gait analysis, motion guidance, motion intervention, rehabilitation evaluation and the like, however, the characterization and analysis of joint motion cooperation are difficult problems in related fields due to the problems of various types of human body motions, complex physiological structures and the like.
The joint coordination rules of walking, rope skipping and the like have the characteristics of high flexibility, good flexibility, high stability and the like, and the joint coordination rules of the joint coordination motions are taken as typical representatives in human basic motions and skill motions, so that the intrinsic joint coordination rules have high practical application values, but the problems of difficult feature extraction, difficult coordination characterization, difficult rule analysis and the like still exist in the current motion coordination research field. The current methods of motion time phase calculation, image feature extraction and the like can only simply analyze the characteristics of local joint coupling, motion period variability and the like, and still cannot realize accurate quantitative characterization and deep analysis of human motion cooperative features, so that an effective method capable of accurately characterizing human rhythm cooperative motion in real time and accurately analyzing the inherent change rule of the human rhythm cooperative motion is urgently needed for the research of human motion cooperative rules.
Disclosure of Invention
The invention provides a human rhythm motion parametric representation analysis method based on fast Fourier transform to solve the defects, and the method is based on the periodic characteristics of human rhythm motion, decomposes the rhythmic motion of various joints of a human body into amplitude, frequency and phase parameters by using the fast Fourier transform, maps the amplitude, the frequency and the phase parameters to the particle distribution in a human rhythm collaborative motion background space, realizes accurate analysis of change rules among different particle distributions by introducing the principle and the method in the manifold theory, and provides theoretical basis for further development in the fields of motion guidance, motion intervention, rehabilitation evaluation and the like.
In order to solve the technical problems, the invention adopts the technical scheme that: the human rhythm motion parametric characterization analysis method based on the fast Fourier transform comprises the following steps:
step S1: selecting a tested object to perform a data acquisition experiment, acquiring joint angle change data in human body rhythm movement, establishing a movement database and drawing a movement curve; the step S1 includes the steps of:
step S101: selecting a tested subject to carry out a motion data acquisition experiment, acquiring joint angle change data in human body rhythm motion and establishing a database;
step S102: extracting the motion data of each joint tested in the sagittal plane and respectively drawing the motion curve of each joint;
step S2: decomposing each joint motion curve into a plurality of rhythm components with different frequencies through fast Fourier transform, and extracting the amplitude, frequency and phase parameters of each component; the step S2 includes the steps of:
step S201: dividing the motion cycle in the human body rhythm motion data according to the human body rhythm motion periodic characteristics;
step S202: extracting angle motion cycle data of each joint according to the division result, decomposing the angle data of each joint into a plurality of rhythm components with different frequencies based on fast Fourier transform, and extracting the frequency, amplitude and phase parameters of each component;
the formula for the fast fourier transform can be expressed as:
wherein X (N) is an original signal, X (k) is a signal after fast Fourier transform, j is an imaginary unit, and N is the number of sampling points;
and step S3: the amplitude, frequency and phase parameters of each component extracted in the step S2 are equivalent to space particle coordinates and are mapped to component coordinates of a midpoint in a three-dimensional space, and a particle distribution representation of the human rhythm cooperative motion is formed;
and step S4: based on the fluid mechanics thought and the McCann translation theory, in the particle distribution corresponding to different groups of data with the same motion, the real-time and accurate analysis of the particle distribution rule corresponding to the human rhythm cooperative motion is realized by using the methods of geodesic connection, high-density sampling, parameter reconstruction comparison and error calculation.
The technical scheme of the invention is further improved as follows: step S3 includes the following steps:
step S301: useless joint data which have no influence or only have slight influence on the motion process are removed by using methods of amplitude discrimination and joint weight calculation, and joint data which play a main role in the motion process are reserved;
step S302: the rhythm component parameters after the tested joint curve is decomposed are equivalent to coordinate components, each component parameter is mapped into a three-dimensional space and is subjected to normalization processing, and a particle distribution representation of human rhythm cooperative motion is formed, wherein the formula is as follows:
θ i ＝[2*[θ i -min(θ i )]/[max(θ i )-min(θ i )]]-1, wherein θ i Representing the joint angle at time i in one gait cycle, the normalization process scales all data to [ -1,1]And (4) the following steps.
The technical scheme of the invention is further improved as follows: step S4 includes the following steps:
step S401: based on the fluid mechanics thought and the McCann translation theory, in the particle distribution corresponding to different groups of data, according to the magnitude sequence of the frequency parameters, the geodesic lines are used for connecting the component particles with the same frequency magnitude sequence;
step S402: and performing high-density equal sampling on all the connecting lines, reconstructing joint angle period data through parameters corresponding to sampling point coordinates, and finally realizing real-time accurate analysis of the distribution change rule of the nodal-law cooperative motion particles of different individuals and different motions by using modes of contrast verification and error calculation.
The technical scheme of the invention is further improved as follows: the joint angle processing and analyzing steps in step S1, step S2, step S3, or step S4 may be applied to data of joint angular velocity and angular acceleration calculated based on the joint angle.
Due to the adoption of the technical scheme, the invention has the technical progress that:
the method decomposes rhythmic motion of a plurality of joints of a human body into amplitude, frequency and phase parameters by utilizing fast Fourier transform, maps the amplitude, frequency and phase parameters to particle distribution in a background space of body rhythm collaborative motion, and realizes accurate analysis of change rules among different particle distributions by introducing principles and methods in a popular theory, thereby realizing accurate quantitative characterization of the body rhythm motion.
The method realizes accurate representation of the cooperative characteristic of the human body rhythm motion, realizes parameterization of the human body joint rhythm motion by utilizing fast Fourier transform and mapping the parameterization into particle distribution in space based on the periodic characteristic of the human body rhythm motion, further analyzes the cooperative characteristic of the human body rhythm motion by analyzing the distribution rule of the particles, and further is applied to human body rehabilitation evaluation.
The invention realizes the accurate real-time analysis of the human rhythm motion rule, further analyzes the difference of the same motion with different proficiency degrees by the real-time particle processing and analysis of the human rhythm motion joint angle through the fast Fourier transform, and can give real-time guidance in the learning of a certain motion.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description in the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts;
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a three-dimensional map of joint particles of the present invention;
FIG. 3 is a schematic view of a joint angle particle map of the present invention;
FIG. 4 is a schematic view of particle analysis according to the present invention;
Detailed Description
The present invention will be described in further detail with reference to the following examples:
the human rhythm movement parameterization characterization analysis method based on fast Fourier transform, as shown in fig. 1 to 4, comprises the following steps:
step S1: selecting a tested object to perform an experiment, collecting the human rhythm movement joint angle data, establishing a database, drawing a movement curve, recording and storing basic information of a target object, including name, gender, age, height, weight, the name of a done action and the like.
The step S1 specifically includes the following steps: a is selected as a beginner, B is selected as an athlete to carry out rope skipping experiments by two persons, two tested data are collected by adopting a high-precision motion capture instrument, a database is established, then the motion data of each joint in the sagittal plane is extracted, and the motion curve of each joint is respectively drawn by B.
Step S2: and decomposing each joint motion curve into a plurality of rhythm components with different frequencies through fast Fourier transform, and extracting the amplitude, frequency and phase parameters of each component.
The step S2 specifically includes the following steps: dividing the motion cycle in the human body rhythm motion data according to the periodic characteristics of the human body rhythm motion; and carrying out fast Fourier transform on the acquired joint angle change data, decomposing the motion curve of each joint into a plurality of rhythm components with different frequencies, and extracting the amplitude, frequency and phase parameters of each component.
The formula for the fast fourier transform can be expressed as:
wherein X (N) is the original signal, X (k) is the signal after fast Fourier transform, j is the imaginary unit, and N is the number of sampling points.
And step S3: the amplitude, frequency and phase parameters extracted from each component in the step S2 are equivalent to space particle coordinates and are mapped to component coordinates of a midpoint in a three-dimensional space, and a particle distribution representation of the human rhythm cooperative motion is formed;
step S3 specifically includes the following steps: useless joint data which have little or no influence on the motion process are removed by methods of amplitude discrimination, joint weight calculation and the like, and joint data which play a main role in the motion process are reserved; and (3) the joint component parameters after the tested joint curve is decomposed are equivalent to coordinate components, the coordinate components are mapped into a three-dimensional surface and normalization processing is carried out, and the formula is as follows:
θ i ＝[2*[θ i -min(θ i )]/[max(θ i )-min(θ i )]]-1, wherein θ i Representing the joint angle at time i in one gait cycle, the normalization process scales all data to [ -1,1]And (4) the following steps.
And step S4: based on the fluid mechanics thought and the McCann translation theory, in the particle distribution corresponding to different groups of data with the same motion, the real-time and accurate analysis of the particle distribution rule corresponding to the human rhythm cooperative motion is realized by utilizing the methods of geodesic connection, high-density sampling, parameter reconstruction comparison, error calculation and the like.
Step S4 specifically includes the following steps: based on the fluid mechanics thought and the McCann translation theory, in A, B two groups of corresponding particle distributions, according to the magnitude sequence of the frequency parameters, the geodesic lines are used for connecting the component particles with the same frequency magnitude sequence; sampling all connecting lines by more than 10 equal divisions, reconstructing a curve by the corresponding parameters of each equal division point coordinate, representing one of more than 10 components of each joint, obtaining a plurality of equal division points, observing and analyzing the difference of A and B persons in the rope skipping movement by means of comparison verification, error calculation and the like, and further carrying out real-time and accurate analysis guidance on the learning of the rope skipping movement.
The embodiment embodies the joint angle data equivalent to the essential rule of human motion, parametrizes the human rhythm motion by deeply analyzing the stage characteristics of the rhythmic motion and utilizing a fast Fourier transform method, so that the characteristics of personal difference, cooperativity and the like of the human rhythm motion which are difficult to describe originally can be accurately extracted in real time and subjected to quantitative analysis.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (3)
1. The human rhythm motion parametric characterization analysis method based on fast Fourier transform is characterized by comprising the following steps: the method comprises the following steps:
step S1: selecting a tested object to perform a data acquisition experiment, acquiring joint angle change data in human body rhythm movement, establishing a movement database and drawing a movement curve; the step S1 includes the steps of:
step S101: selecting a tested subject to carry out a motion data acquisition experiment, acquiring joint angle change data in human body rhythm motion and establishing a database;
step S102: extracting the motion data of each joint tested in a sagittal plane and respectively drawing the motion curve of each joint;
step S2: decomposing each joint motion curve into a plurality of rhythm components with different frequencies through fast Fourier transform, and extracting the amplitude, frequency and phase parameters of each component; the step S2 includes the steps of:
step S201: dividing the motion cycle in the human body rhythm motion data according to the human body rhythm motion periodic characteristics;
step S202: extracting angle motion cycle data of each joint according to the division result, decomposing the angle data of each joint into a plurality of rhythm components with different frequencies based on fast Fourier transform, and extracting the frequency, amplitude and phase parameters of each component;
the formula for the fast fourier transform is expressed as:
wherein X (N) is an original signal, X (k) is a signal after fast Fourier transform, j is an imaginary unit, and N is the number of sampling points;
and step S3: the amplitude, frequency and phase parameters of each component extracted in the step S2 are equivalent to space particle coordinates and are mapped to component coordinates of a midpoint in a three-dimensional space, and a particle distribution representation of the human rhythm cooperative motion is formed;
and step S4: based on the fluid mechanics thought and the McCann translation theory, in the particle distribution corresponding to different groups of data with the same motion, the real-time and accurate analysis of the particle distribution rule corresponding to the human rhythm cooperative motion is realized by using the methods of geodesic connection, high-density sampling, parameter reconstruction comparison and error calculation; the step S4 includes the steps of:
step S401: based on the fluid mechanics thought and the McCann translation theory, in the particle distribution corresponding to different groups of data, according to the magnitude sequence of the frequency parameters, utilizing the geodesic line to connect the component particles with the same frequency magnitude sequence;
step S402: and performing high-density equal sampling on all the connecting lines, reconstructing joint angle period data through parameters corresponding to sampling point coordinates, and finally realizing real-time accurate analysis of the distribution change rule of the nodal-law cooperative motion particles of different individuals and different motions by using modes of contrast verification and error calculation.
2. The fast fourier transform-based human rhythm motion parameterization characterization and analysis method of claim 1, wherein: the step S3 includes the steps of:
step S301: useless joint data which have no influence or only have slight influence on the motion process are removed by using methods of amplitude discrimination and joint weight calculation, and joint data which play a main role in the motion process are reserved;
step S302: the rhythm component parameters after the tested joint curve is decomposed are equivalent to coordinate components, each component parameter is mapped into a three-dimensional space and is subjected to normalization processing, and a particle distribution representation of human rhythm cooperative motion is formed, wherein the formula is as follows:
θ i ＝[2*[θ i -min(θ i )]/[max(θ i )-min(θ i )]]-1, wherein θ i Representing the joint angle at time i in one gait cycle, the normalization process scales all data to [ -1,1]And (4) the following steps.
3. The fast fourier transform-based human rhythm motion parameterization characterization and analysis method of claim 1, wherein: the joint angle processing and analyzing steps in step S1, step S2, step S3, and step S4 may be applied to data of joint angular velocity and angular acceleration calculated based on the joint angle.
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| CN111950383A (en) * | 2020-07-21 | 2020-11-17 | 燕山大学 | Joint angle-based rhythm and motion collaborative analysis method |
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| CN109062051B (en) * | 2018-08-28 | 2021-07-23 | 苏州艾利特机器人有限公司 | Method for improving robot dynamics parameter identification precision |
| US11694360B2 (en) * | 2019-11-06 | 2023-07-04 | Ssam Sports, Inc. | Calibrating 3D motion capture system for skeletal alignment using x-ray data |
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| CN102955889A (en) * | 2011-08-29 | 2013-03-06 | 中国科学院力学研究所 | Pulse wave reconstruction method for extracting time domain feature points |
| CN109191448A (en) * | 2018-09-03 | 2019-01-11 | 重庆大学 | The transformer fault identification technology with Digital Image Processing is drawn based on three-dimensional coordinate |
| CN111685772A (en) * | 2020-05-29 | 2020-09-22 | 清华大学 | Exoskeleton robot measurement system, walking gait modeling analysis method and equipment |
| CN111950383A (en) * | 2020-07-21 | 2020-11-17 | 燕山大学 | Joint angle-based rhythm and motion collaborative analysis method |
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