CN112842261B - Intelligent evaluation system for three-dimensional spontaneous movement of infant based on complex network - Google Patents

Intelligent evaluation system for three-dimensional spontaneous movement of infant based on complex network Download PDF

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CN112842261B
CN112842261B CN202011616536.8A CN202011616536A CN112842261B CN 112842261 B CN112842261 B CN 112842261B CN 202011616536 A CN202011616536 A CN 202011616536A CN 112842261 B CN112842261 B CN 112842261B
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徐光华
吴庆强
魏帆
张四聪
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Xian Jiaotong University
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Abstract

A baby three-dimensional spontaneous movement intelligent evaluation system based on a complex network comprises a three-dimensional somatosensory information input module, a movement information feature extraction module, a computer with a complex network analysis module and a movement quality output module; the output of the three-dimensional motion sensing information input module is connected with the input of the motion information characteristic extraction module, the output of the motion characteristic extraction module is connected with the input of a computer with a complex network analysis module, and the output of the computer with the complex network analysis module is connected with the input of the motion quality output module; the system can comprehensively reflect the movement characteristics of the baby, performs targeted complexity characteristic evaluation on the spontaneous movement of the baby, and meets the large-scale and intelligent popularization and application of the baby cerebral palsy screening.

Description

Intelligent evaluation system for three-dimensional spontaneous movement of infant based on complex network
Technical Field
The invention belongs to the technical field of human motion assessment, and particularly relates to an intelligent infant three-dimensional spontaneous motion assessment system based on a complex network.
Background
A professional doctor can analyze movement characteristics through clinical observation of spontaneous movement of the infant to realize early prediction of neurodevelopmental disorders such as cerebral palsy and the like, and can promote nerve remodeling through intervention treatment in a brain development strengthening process, so that negative effects of the cerebral palsy on the infant are reduced, but the training period of the early diagnosis mode is long and the efficiency is low. In order to improve the early screening efficiency of the infantile cerebral palsy, a computer and a visual sensor can be adopted to assist a doctor to finish the observation and analysis process, and powerful technical support is provided for early screening and rehabilitation training of the cerebral palsy.
At present, with the rapid development of somatosensory technology, the digital evaluation of human body movement also becomes a research hotspot. The existing human motion digital assessment mainly comprises two main categories of identification evaluation of specific actions and clinical assessment of typical actions. The standardized evaluation of specific actions can only be applied to conscious exercise learning, such as evaluation of execution degree of standard actions of sports, dancing, rehabilitation training and the like, and the motion evaluation method is not suitable for evaluation of the motion of the infant in consideration of the involuntary motion of the infant; the characteristic evaluation of typical movement uses more traditional time-frequency domain analysis, is mainly used for dyskinesia evaluation caused by medical diseases, can describe the dynamic characteristics of movement, but does not evaluate the nonlinear characteristics of spontaneous movement of infants.
However, the computer system is used for assisting a professional physician to perform early screening of cerebral palsy, and the core problem is to digitally represent the infant movement and extract and evaluate corresponding characteristics. However, the existing movement evaluation multiple bases on the statistical distribution of movement for early prediction of infantile cerebral palsy cannot consider the change process of the movement per se on the time sequence, can not comprehensively and essentially reflect the characteristics of the whole body movement, and then has low prediction accuracy on the neural development state.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide the infant three-dimensional spontaneous movement intelligent evaluation system based on the complex network, which can comprehensively reflect the movement characteristics of the infant, carry out targeted complexity characteristic evaluation on the spontaneous movement of the infant and meet the large-scale and intelligent popularization and application of the infant cerebral palsy screening.
In order to achieve the purpose, the invention adopts the technical scheme that:
a baby three-dimensional spontaneous movement intelligent evaluation system based on a complex network comprises a three-dimensional somatosensory information input module, a movement information feature extraction module, a computer with a complex network analysis module and a movement quality output module; the output of the three-dimensional motion sensing information input module is connected with the input of the motion information characteristic extraction module, the output of the motion characteristic extraction module is connected with the input of a computer with a complex network analysis module, and the output of the computer with the complex network analysis module is connected with the input of the motion quality output module.
The three-dimensional somatosensory information input module realizes acquisition of three-dimensional somatosensory information, wherein the three-dimensional somatosensory information is human skeleton three-dimensional coordinates output by Kinect equipment, namely three-dimensional key point information of a human body.
The motion information characteristic extraction module comprises a human body structure vector representation module and a human body motion characteristic representation module, the human body structure vector representation module represents three-dimensional key point information output by the three-dimensional body feeling information input module as human body structure vectors, and the human body motion characteristic representation module realizes key point angle calculation between the human body structure vectors.
The computer with the complex network analysis module comprises an association dimension analysis module and a small-world network reconstruction module; the correlation dimension analysis module analyzes the key point angle features of the input human body structure and calculates the correlation dimension of the input feature sequence; and the small-world network reconstruction module analyzes the small-world network characteristics of the key point angle characteristics of the input human body structure.
The motion quality output module comprises a comprehensive motion quality generation module and an average comprehensive evaluation index output module; the comprehensive motion quality generation module combines the output of the correlation dimension analysis module and the output of the small-world network reconstruction module to generate a final comprehensive evaluation index; the average comprehensive evaluation index output module repeatedly calculates the comprehensive evaluation index of the computer with the complex network analysis module and outputs the final average comprehensive evaluation index.
The invention has the following beneficial effects:
(1) the system of the invention fully considers the variability of the motion in time, and can evaluate the spontaneous motion quality of the infant in terms of complexity in a targeted manner.
(2) The system has high efficiency, avoids the influence of subjective factors in the evaluation of the spontaneous movement of the infant, and realizes the objective quantitative evaluation of the spontaneous movement quality of the infant.
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FIG. 1 is a block diagram of the present invention.
FIG. 2 shows the position of the joint point of the human body according to the present invention.
FIG. 3 is a diagram showing the comparison result of solving the correlation dimension by simulating spontaneous movement of a normal infant (complex movement) and a cerebral palsy infant (simple movement), and (a) is a diagram showing the result of the right shoulders of the two infants; panel (b) is a schematic representation of the right wrist results for both infants.
FIG. 4 is a schematic diagram of a correlation coefficient matrix for simulating solution of a group of simple movements and a group of complex movements of normal and cerebral paralyzed infants, wherein (a) is a normal movement correlation matrix; graph (b) an abnormal motion correlation matrix.
FIG. 5 is a schematic diagram of a network structure obtained by reconstructing a group of simple movements and a group of complex movements by simulating normal and cerebral palsy infants, wherein (a) is a normal movement network structure; graph (b) abnormal motion network structure.
FIG. 6 is a diagram of the combined indicators of normal and abnormal infants in the group of normal and cerebral palsy infants 12.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
Referring to fig. 1, an intelligent evaluation system for three-dimensional spontaneous movement of an infant based on a complex network comprises a three-dimensional somatosensory information input module, a movement information feature extraction module, a computer with a complex network analysis module and a movement quality output module; the output of the three-dimensional motion sensing information input module is connected with the input of the motion information characteristic extraction module, the output of the motion characteristic extraction module is connected with the input of a computer with a complex network analysis module, and the output of the computer with the complex network analysis module is connected with the input of the motion quality output module.
The three-dimensional somatosensory information input module realizes acquisition of three-dimensional somatosensory information, wherein the three-dimensional somatosensory information is human skeleton three-dimensional coordinates output by Kinect equipment, namely three-dimensional key point information of a human body.
The motion information feature extraction module comprises a human body structure vector representation module and a human body motion feature representation module, wherein the human body structure vector representation module represents the three-dimensional key point information input by the three-dimensional body feeling information input module into a human body structure vector; the human motion characteristic representation module realizes the calculation of the key point angle between the human structure vectors.
Different human body structure vectors are constructed in the human body structure vector representation module according to the three-dimensional coordinates of the human body joint points output by the three-dimensional somatosensory information input module;
taking 17 human body structure vectors as an example, the following structure vectors can be constructed: neck → head vector
Figure GDA0003288310020000051
Neck → right shoulder vector
Figure GDA0003288310020000052
Right shoulder → right elbow vector
Figure GDA0003288310020000053
Right elbow → right wrist vector
Figure GDA0003288310020000054
Neck → left shoulder vector
Figure GDA0003288310020000055
Left shoulder → left elbow vector
Figure GDA0003288310020000056
Left elbow → left wrist vector
Figure GDA0003288310020000057
Neck → right hip vector
Figure GDA0003288310020000058
Right hip → right knee vector
Figure GDA0003288310020000059
Right Knee → Right ankle vector
Figure GDA00032883100200000510
Neck → left hip vector
Figure GDA00032883100200000511
Left hip → left knee vector
Figure GDA00032883100200000512
Left knee → left ankle vector
Figure GDA00032883100200000513
Neck → right wrist vector
Figure GDA00032883100200000514
Neck → left wrist vector
Figure GDA00032883100200000515
Right hip → right ankle vector
Figure GDA00032883100200000516
Left hip → left ankle vector
Figure GDA00032883100200000517
Taking the right upper body structure vector as an example, the calculation method of the right upper body structure vector is as follows:
Figure GDA00032883100200000518
in the formula: (x)Part,yPart,zPart) Three-dimensional coordinates of corresponding nodes of the body are respectively shown, wherein Part represents a corresponding key point of the human body, H is the head, N is the neck, SR is the right shoulder, ER is the right elbow, and WR is the right wrist, as shown in FIG. 2.
The human motion characteristic representation module takes the similarity of human structures into consideration, extracts human joint angles from the human structure vectors output by the human structure vector representation module, and digitally represents the human motion to obtain angle motion characteristic sequences with different dimensions;
taking the 14-dimensional as an example,
θi＝[θNR,θNL,θSL,θEL,θWL,θSR,θER,θWR,θHL,θKL,θAL,θHR,θKR,θAR]，
wherein theta isiRepresenting the ith key point angle, NR being the right neck, NL being the left neck, SL being the left shoulder, EL being the left elbow, WL being the left wrist, SR being the right shoulder, ER being the right elbow, WR being the right wrist, HL being the left hip, KL being the left knee, AL being the left ankle, HR being the right hip, KR being the right knee, AR being the right ankle; theta is the angle characteristic of the corresponding joint, and the angle theta of the right shoulder jointSRAnd right elbow joint angle thetaERThe calculation method is as follows:
Figure GDA0003288310020000061
other joint angles are obtained in the same way.
The computer with the complex network analysis module comprises an association dimension analysis module and a small-world network reconstruction module; the correlation dimension analysis module analyzes the key point angle features of the input human body structure and calculates the correlation dimension of the input feature sequence; and the small-world network reconstruction module analyzes the small-world network characteristics of the key point angle characteristics of the input human body structure.
The correlation dimension analysis module adopts a nonlinear system correlation theory to calculate the nonlinear complexity of each dimension motion characteristic sequence output by each human motion characteristic representation module, and selects the time delay tau at the moment as the time delay of the sequence when the first local minimum value of the mutual information quantity appears along with the increase of the time delay tau; meanwhile, as the embedding dimension m increases, when the pseudo-neighbor rate in the phase space decreases to 0, m at this time is selected as the embedding dimension of the sequence; after determining the embedding dimension m and the time delay τ, the correlation dimension D is solved2：
Figure GDA0003288310020000062
Wherein D is2For a sequence of features { theta }iThe GP correlation dimension of, C (r) represents the ratio of the number of point pairs with a distance less than r in the feature phase space to all possible number of point pairsTo 14-dimensional correlation dimension information D2. FIG. 3 shows a comparison of the correlation dimension solving effects of the angular feature sequences of the right shoulder joint and the right wrist joint of 30 different groups of infants in different motion states; as can be seen from the figure, different motion patterns have different embedding dimensions, complex motions have a higher embedding dimension, while simple motions have a lower embedding dimension.
The small world network reconstruction module analyzes the small world network characteristics of the key point angle characteristics of the input human body structure; taking the synchronicity of the motion of each limb of different human bodies into consideration, and solving the Spearman correlation coefficient among the key point angle characteristic dimensional motion characteristic sequences of the human body structure:
Figure GDA0003288310020000071
wherein for the original data { theta }iAnd { theta }jRecording theta 'according to the sequence from big to small'iAnd θ'jIs the raw data thetaiAnd thetajLocation in the sorted list is named θ'iAnd thetaj' is the raw data thetaiAnd thetajRank of (2), rank difference ht＝θ'i-θ'j(ii) a N is data { thetaiLength of time series; rhos(θi,θj) Spearman's correlation coefficient representing the i and j dimensions in the angular features; forming a correlation coefficient matrix for describing the whole body movement on the basis of the physical connectivity of each node in the human anatomy distribution, and simultaneously considering the joint transmission property of each limb, connecting edges of the connected joints in the anatomy and the non-adjacent joints on the single limb to construct an initial network; and then, taking the absolute values of the elements in the Spearman correlation matrix as the probability of adding new connecting edges between nodes, and reconstructing the initial network aiming at the specific motion sequence to obtain the characteristics of the small-world network under different human motion modes. Fig. 4 (a) and (b) show the correlation coefficient matrix solved for a complex set of motions and a simple set of motions, respectively, and it can be seen that the correlation between the key points of the complex motion in (a) is weaker than that of the simple motion in (b); in FIG. 5(a) And (b) respectively showing a network structure obtained by reconstructing a group of complex motions and a group of simple motions, wherein the connection of the small-world network of the complex motions in (a) is relatively simple, and the relevance between the nodes of the small-world network is strong due to the strong relevance between key points in the simple motions in (b).
The motion quality output module comprises a comprehensive motion quality generation module and an average comprehensive evaluation index output module; the comprehensive motion quality generation module combines the output of the correlation dimension analysis module and the output of the small-world network reconstruction module to generate a final comprehensive evaluation index; the average comprehensive evaluation index output module repeatedly calculates the comprehensive evaluation index of the computer with the complex network analysis module and outputs the final average comprehensive evaluation index.
The comprehensive motion quality generation module takes the correlation dimension output by the correlation dimension analysis module and the small-world network characteristics output by the small-world network reconstruction module as the input of the network model, and solves the whole body motion complexity S under the network statei：
Figure GDA0003288310020000081
Wherein D2iCorrelation dimension calculated for i times, CiAnd C0Respectively, the cluster coefficient of the i-th reconstructed network and the cluster coefficient of the initial network, LiAnd L0The average path length of the i-th reconstructed and the average path length of the initial network, respectively.
The average comprehensive evaluation index output module repeats the comprehensive motion quality generation module for k times, and averages the comprehensive indexes to obtain a complexity comprehensive index describing the motion of the sample:
Figure GDA0003288310020000082
fig. 6 shows the comprehensive indexes of 12 groups of normal and abnormal infants, and it can be seen from the figure that the positive sample, i.e. the abnormal sample, can be effectively screened out through the complexity comprehensive index S of 12 groups of samples.

Claims (6)

1. A baby three-dimensional spontaneous motion intelligent evaluation system based on a complex network is characterized in that: the motion quality analysis system comprises a three-dimensional somatosensory information input module, a motion information feature extraction module, a computer with a complex network analysis module and a motion quality output module; the output of the three-dimensional motion sensing information input module is connected with the input of the motion information characteristic extraction module, the output of the motion characteristic extraction module is connected with the input of a computer with a complex network analysis module, and the output of the computer with the complex network analysis module is connected with the input of the motion quality output module;
the three-dimensional somatosensory information input module realizes acquisition of three-dimensional somatosensory information, wherein the three-dimensional somatosensory information is three-dimensional coordinates of human skeleton output by Kinect equipment, namely three-dimensional key point information of a human body;
the motion information characteristic extraction module comprises a human body structure vector representation module and a human body motion characteristic representation module, the human body structure vector representation module represents three-dimensional key point information output by the three-dimensional body feeling information input module as human body structure vectors, and the human body motion characteristic representation module realizes the calculation of key point angles among the human body structure vectors;
the computer with the complex network analysis module comprises an association dimension analysis module and a small-world network reconstruction module; the correlation dimension analysis module analyzes the key point angle features of the input human body structure and calculates the correlation dimension of the input feature sequence; the small world network reconstruction module analyzes the small world network characteristics of the key point angle characteristics of the input human body structure;
the small world network reconstruction module analyzes the small world network characteristics of the key point angle characteristics of the input human body structure; taking the synchronicity of the motion of each limb of different human bodies into consideration, and solving the Spearman correlation coefficient among the key point angle characteristic dimensional motion characteristic sequences of the human body structure:
Figure FDA0003288310010000021
wherein for the original data { theta }iAnd { theta }jRecording theta 'according to the sequence from big to small'iAnd θ'jIs the raw data thetaiAnd thetajLocation in the sorted list is named θ'iAnd θ'jIs the raw data thetaiAnd thetajRank of (2), rank difference ht＝θ′i-θ′j(ii) a N is data { thetaiLength of time series; rhos(θi，θj) Spearman's correlation coefficient representing the i and j dimensions in the angular features; forming a correlation coefficient matrix for describing the whole body movement on the basis of the physical connectivity of each node in the human anatomy distribution, and simultaneously considering the joint transmission property of each limb, connecting edges of the connected joints in the anatomy and the non-adjacent joints on the single limb to construct an initial network; and then, taking the absolute values of the elements in the Spearman correlation matrix as the probability of adding new connecting edges between nodes, and reconstructing the initial network aiming at the specific motion sequence to obtain the characteristics of the small-world network under different human motion modes.
2. The intelligent evaluation system for three-dimensional spontaneous infant movement based on complex network as claimed in claim 1, wherein: different human body structure vectors are constructed in the human body structure vector representation module according to the three-dimensional coordinates of the human body joint points output by the three-dimensional somatosensory information input module; adopting 17 human body structure vectors to construct the following structure vectors: neck → head vector
Figure FDA0003288310010000022
Neck → right shoulder vector
Figure FDA0003288310010000023
Right shoulder → right elbow vector
Figure FDA0003288310010000024
Elbow →Right wrist vector
Figure FDA0003288310010000025
Neck → left shoulder vector
Figure FDA0003288310010000026
Left shoulder → left elbow vector
Figure FDA0003288310010000027
Left elbow → left wrist vector
Figure FDA0003288310010000028
Neck → right hip vector
Figure FDA0003288310010000029
Right hip → right knee vector
Figure FDA00032883100100000210
Right Knee → Right ankle vector
Figure FDA00032883100100000211
Neck → left hip vector
Figure FDA00032883100100000212
Left hip → left knee vector
Figure FDA00032883100100000213
Left knee → left ankle vector
Figure FDA00032883100100000214
Neck → right wrist vector
Figure FDA00032883100100000215
Neck → left wrist vector
Figure FDA00032883100100000216
Right hip → right ankle vector
Figure FDA00032883100100000217
Left hip → left ankle vector
Figure FDA00032883100100000218
The calculation method of the right upper body structure vector is as follows:
Figure FDA0003288310010000031
in the formula: (x)Part,yPart,zPart) And three-dimensional coordinates of corresponding nodes of the body respectively, wherein Part represents a corresponding key point of the human body, H is the head, N is the neck, SR is the right shoulder, ER is the right elbow, and WR is the right wrist.
3. The intelligent evaluation system for baby three-dimensional spontaneous motion based on complex network as claimed in claim 2, wherein: the human motion characteristic representation module takes the similarity of human structures into consideration, extracts human joint angles from the human structure vectors output by the human structure vector representation module, and digitally represents the human motion to obtain angle motion characteristic sequences with different dimensions; by adopting the method of 14-dimensional,
θi＝[θNR,θNL,θSL,θEL,θWL,θSR,θER,θWR,θHL,θKL,θAL,θHR,θKR,θAR]，
wherein theta isiRepresenting the ith key point angle, NR being the right neck, NL being the left neck, SL being the left shoulder, EL being the left elbow, WL being the left wrist, SR being the right shoulder, ER being the right elbow, WR being the right wrist, HL being the left hip, KL being the left knee, AL being the left ankle, HR being the right hip, KR being the right knee, AR being the right ankle; theta is the angle characteristic of the corresponding joint, and the angle theta of the right shoulder jointSRAnd right elbow joint angle thetaERThe calculation method is as follows:
Figure FDA0003288310010000032
other joint angles are obtained in the same way.
4. The intelligent evaluation system for three-dimensional spontaneous infant movement based on complex network as claimed in claim 1, wherein: the correlation dimension analysis module adopts a nonlinear system correlation theory to calculate the nonlinear complexity of each dimension motion characteristic sequence output by each human motion characteristic representation module, and selects the time delay tau at the moment as the time delay of the sequence when the first local minimum value of the mutual information quantity appears along with the increase of the time delay tau; meanwhile, as the embedding dimension m increases, when the pseudo-neighbor rate in the phase space decreases to 0, m at this time is selected as the embedding dimension of the sequence; after determining the embedding dimension m and the time delay τ, the correlation dimension D is solved2：
Figure FDA0003288310010000041
Wherein D is2For a sequence of features { theta }iThe GP correlation dimension of C (r) represents the proportion of the number of the point pairs with the distance less than r in the characteristic phase space to all possible point pairs, and 14-dimensional correlation dimension information D is obtained2。
5. The intelligent evaluation system for three-dimensional spontaneous infant movement based on complex network as claimed in claim 1, wherein: the motion quality output module comprises a comprehensive motion quality generation module and an average comprehensive evaluation index output module; the comprehensive motion quality generation module combines the output of the correlation dimension analysis module and the output of the small-world network reconstruction module to generate a final comprehensive evaluation index; the average comprehensive evaluation index output module repeatedly calculates the comprehensive evaluation index of the computer with the complex network analysis module and outputs the final average comprehensive evaluation index.
6. The intelligent evaluation system for baby three-dimensional spontaneous motion based on complex network as claimed in claim 5, wherein: the comprehensive motion quality generation module takes the correlation dimension output by the correlation dimension analysis module and the small-world network characteristics output by the small-world network reconstruction module as the input of the network model, and solves the whole body motion complexity S under the network statei：
Figure FDA0003288310010000051
Wherein D2iCorrelation dimension calculated for i times, CiAnd C0Respectively, the cluster coefficient of the i-th reconstructed network and the cluster coefficient of the initial network, LiAnd L0The average path length of the ith reconstructed network and the average path length of the initial network are respectively;
the average comprehensive evaluation index output module repeats the comprehensive motion quality generation module for k times, and averages the comprehensive indexes to obtain a complexity comprehensive index describing the motion of the sample:
Figure FDA0003288310010000052
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