# Complexity Graph-Based Multilabel Classification Method of Human Action in Rope Skipping Scene

Authors: Yichen Wang; Yi Wang; Zhimin Zhang
Journal: Security and Communication Networks
 (2022)

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## Abstract

Aiming at the problem of insufficient accuracy of multilabel classification of human action at present, a multilabel classification method of human action in the rope skipping scene is proposed. It realizes feature recognition and classification by collecting human action features in the scene of skipping rope movement and uses RNN to optimize the human action feature recognition algorithm. On the basis of feature recognition, the characteristics of human movement in the rope skipping scene are classified, the confidence map of the key point position is obtained by using the Gaussian modeling method, and the action multilabel classification is realized. Finally, experiments show that the multilabel classification method of human action in rope skipping scene has high accuracy and fully meets the research requirements.


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## Body

## 1. Introduction

With the development and application of computer technology, artificial intelligence technology has been widely used in sports training. As a whole-body sport, rope skipping plays a more and more important role in daily life and has been included in the project of middle school entrance examination [1]. Currently, a shortage of skilled physical education instructors in rope skipping instruction results in poor student training efficiency: rope skipping monitoring equipment is bulky and costly, and the cost and energy of manual statistics are excessive. How to assess motions in the rope skipping process automatically and rapidly, and provide appropriate advice and training plans, has become the key to improving the score on the rope skipping examination [2]. Existing vision-based human motion detection and analysis algorithms are sophisticated, insecure, and computationally intensive. Furthermore, owing to a scarcity of qualified human motion analyzers, further study into human motion analysis and motion quality assessment is needed. As a result, it is critical for motion analysis and motion quality assessment in the human motion process to develop a motion analysis technique with high resilience and a consistent time cost [3, 4]. Aiming at the problem of motion analysis in the rope skipping process, firstly, the coordinates of key points in the rope skipping process are obtained through a 2D attitude estimation algorithm, and the coordinates are preprocessed to obtain a robust data sequence. Secondly, the motion analysis problem in the rope skipping process is transformed into a multilabel classification problem, and a new multilabel classification model alstm-lstm is proposed. Finally, a motion analysis method in rope skipping based on the mobile phone is designed and developed.


## 2. Multilabel Classification of Human Actions in Moving Scenes

### 2.1. Human Motion Feature Recognition in Moving Scene

In the field of motion analysis and recognition based on computer vision, the single-label classification method aims to solve the problem that examples only belong to one category. Different labels are completely independent and not related to each other [5]. However, in practical application, human limb movements may need many aspects of analysis, and there are often multiple labels in one frame of the image, such as simultaneous analysis of upper limb movements and lower limb movements in one action. In the multilabel classification problem, labels are not completely independent, and there are some dependencies or mutual exclusions. In the multilabel classification task, the number of labels is relatively large, which makes the relationship between categories very complex and difficult to describe [6]. Therefore, compared with the traditional single-label classification task, multilabel classification is more complex. Figure 1 shows the difference between single-label classification and multilabel classification.Figure 1
Example of single label and multilabel.Multilabel comprehensive recognition technique is a multilabel learning process-based comprehensive learning approach. A kind of supervised learning issue is multilabel learning. Traditional classification learning algorithms presume that each sample has just one label and interpret the data from a single perspective [7]. Each sample may be connected with many category labels in real-world applications. The strategies used to solve the multilabel classification issue may be classified into two categories: problem transformation and algorithm modification. The multilabel classification problem is generally transformed into alternative learning situations using problem transformation methods, such as changing the multilabel classification issue into numerous binary classification problems [8]. The strategy based on algorithm adaptation entails adapting a current popular algorithm in the classification process to handle multilabel data. With the advancement of deep learning in recent years, several researchers have adapted CNN, RNN, and other deep learning algorithms for use in multilabel classification [9]. Changing the output layer of a neural network is a common modification method. Figure 2 depicts the multilabel learning strategy.Figure 2
Multilabel learning method.The relevant knowledge of RNN is summarized. RNN is a deep neural network that uses context state to mine time series information in data. Compared with convolutional neural network, RNN combines the state of the current model to calculate the output at each time. The structure of the pure RNN model has long-term dependence and may lose the ability to learn remote information [10]. The emergence of long-term a and b short-term memory network Wi successfully solves the problem of gradient disappearance. It is the most popular RNN network, which is widely used in speech recognition, natural language processing, video description, and behavior recognition. The input of the LSTM includes the network input γ of the current time, the LSTM output a of the last time, and the storage unit h of the previous time, and the output x includes the output λ of the current time and the storage unit θ of the current time. It uses input gate and forgetting gate to control the storage unit and combined with output gate to describe the long-distance dependence more effectively. The input gate, forgetting gate, and output gate are calculated as follows:(1)ct=aiWi⋅hr−1,xr+bi−γ,ft=afWf⋅ht−1,xr+bf−λor=aoWo⋅ht−1,xt+bo−θ,,where i,f,o represent the connection weight moments of input gate, forgetting gate, and output gate, respectively, h and p and ilr are the offset of input forgetting gate and output gate, respectively. The output of LSTM is jointly calculated by the memory unit and output gate(2)c¯i=tanctWc⋅ht−1+xl−p,d=ft×cr−1+ilr,hz=or×tanhci−p,where V0 is the candidate state at time t, W is the weight matrix of the candidate state, cr−1 is the candidate bias, n is the storage unit at time t, and k is the final output at time u. If the tag is located on the conveyor belt and assuming that the tag moves in a uniform straight line relative to the antenna in every very short time, the tag movement speed at all times can be calculated. The relative motion speed between the tag and the antenna is(3)Vr=V0cosθ−kn+t−u,where(4)cosθ=c¯id+hzhz2+kv2−1.In order to obtain the coordinate information of human key points in human posture estimation based on multilabel classification, the Gaussian modeling method is used to obtain the confidence map of keypoint position, and the confidence map is used to represent the key points, with the value in the confidence map expressed as the probability of a key point position. The key point location confidence graph may be written as(5)Cj,k=expVr−p−xj,kδ2−k,Cjp=WmaxSj,kp−lz,wherexj,k represents the joint points of the human body, l represents the k-th target person in the image, z represents the coordinates of the predicted person in the image, and W and ∈ r represent the specific coordinate position of the j-th joint point of the k-th target person, δ. It is a minimum value, which can ensure that it can have certain feasibility in the training process of the model.


### 2.2. Human Motion Feature Classification Algorithm in Moving Scene

The main difference between multilabel learning and traditional learning is that the increase in the number of labels makes the output space grow exponentially, and the computational complexity increases significantly [11]. There are three common processing methods for multilabel learning process. The simplest and direct method is to ignore the internal relationship between multiple labels and transform multilabel learning into multiple traditional single-label learning processes [12]. Let x represent the sample space, xi represent yj possible labels. For multilabel learning, it is to learn a function E from sample labels. If the multilabel learning process is transformed into Q single-label learning, the probability of label y obtained for each learning of sample x is(6)Pi=EQ,xi|yj∈hxi,1≤j≤k.The movement speed of the tag relative to the antenna causes the Doppler shift in the tag echo signal. The frequency of the echo signal is higher than the original signal when the tag is near to the antenna [13]. The larger the frequency shift, the quicker the tag travels away from the antenna; when the tag is far away from the antenna, the echo signal has a lower frequency than the original signal. The larger the frequency shift, the quicker the tag travels relative to the antenna [14]. The frequency difference between the antenna’s broadcast and received signals may then be determined as follows:(7)fd=VrPic⋅f0−Cj,k+Cjp.According to the formula, the real-time Doppler is(8)fdpt=Vxpt⋅1xp2t+yp2t+Pi⋅VrPic⋅f0.Combining all the learning results, we can get a set of multiple tagsxpt, and the total learning method is a set of single learning functions ypt, where(9)vypt=h1xi,h2xi,⋯,hqxi.Document classification, information retrieval, and bioinformatics are just a few of the disciplines where multilabel classification learning is applied. However, the approach of multilabel learning has not been thoroughly investigated and utilised in the area of picture and target recognition [15]. A single-label identification technique for targets is the standard target recognition approach. A target can only have one label in the same recognition algorithm, which only explains and characterises the target from one perspective [16]. For example, via the detection and identification of targets in image data, we may gain extremely comprehensive classification results for the appearance of the target in various top-level target recognition algorithms based on convolutional neural networks.


### 2.3. Implementation of Human Action Multilabel Classification in Moving Scene

The label’s location and motion state will be dynamically shown by the scene module, as illustrated in Figure3, which is an image of the module’s real-time scene [17]. The simulation approach also calculates each tag’s multidimensional information, adds suitable noise, and outputs the simulated multidimensional data using the reader’s R selective reading concept.Figure 3
Method composition block diagram of human motion scene platform.In order to clearly and accurately describe the data requirements of users, it is usually necessary to establish a conceptual model of data. The conceptual structure design of the database is the most important part of the whole database design. The purpose of conceptual structure design is to produce the conceptual structure of data. The data entities involved in the method include user entity, data entity, and message entity [18]. The conceptual design of the database is to find out the relationship between data and model the data with the management method of the database, so as to obtain the conceptual model of data. E-R diagram is a conceptual model, through which the relationship between data can be clearly displayed. In the form of an E-R diagram, the following depicts the entity structure and connection between entities: user name, user password, and user authority are the three entity properties of users. The primary key is set to the user ID, and the user authority is configured to differentiate between regular and administrator users [19]. Its major business is to give consumers with limb posture correction services while shaking their feet and jumping rope as an intelligent analysis technique of rope skipping at the end of artificial intelligence mobile phone. Users who want to assess their posture during rope skiing should use their mobile phone to log in, examine information about the successful admission test, upload their own rope skiing video, and get the final analysis results [20]. The method’s core business process is separated into six following elements, as illustrated in Figure 4: user login business process, model upload business process, user management business process, message publishing business process, video upload business process, and analysis result in watching the business process.Figure 4
Function module of intelligent rope skipping teaching method.Human motion analysis only focuses on the overall performance, is not specific to various limb movements, and cannot work effectively in a complex environment [21]. In order to effectively solve this problem, the motion analysis algorithm designed in this paper is based on this method. Because this method has strong generalization ability and universality, in the selection of this method, we use the network model of CNN structure and the LSTM network joint model with an attention mechanism to obtain robustness data through CNN and analyze the standard of limb movement through the joint LSTM model [22]. Figure 5 gives the specific flow chart. According to the flow chart, it can be clearly seen that the analysis of forward shaking double foot jump mainly includes two modules: one module is used to obtain the key point information of the human body, and the other module is to mathematically model the obtained key point information and establish a multilabel classification network model.Figure 5
Design flow of rope skipping action analysis model.To assess athletes’ limb motions during forward shaking two-foot rope skipping, the video stream data are first converted into human key point coordinate data using an enhanced multilabel classification network model, and then, a time series containing human key point coordinates is created [23]. In the process of robust rope skipping, the data are then preprocessed to acquire the limb key point coordinate data. Because the coordinates of the key locations of body motions are a time series with particular connections, this approach is eventually applied to the analysis of body movements using the algorithm transformation method in the multilabel classification algorithm [24]. According to the aforementioned analysis, the posture analysis issue in the rope skipping process may be reduced to the problem of determining whether the athletes’ body swing standard in the rope skipping process is met, which is then changed into a multilabel classification problem.


## 2.1. Human Motion Feature Recognition in Moving Scene

In the field of motion analysis and recognition based on computer vision, the single-label classification method aims to solve the problem that examples only belong to one category. Different labels are completely independent and not related to each other [5]. However, in practical application, human limb movements may need many aspects of analysis, and there are often multiple labels in one frame of the image, such as simultaneous analysis of upper limb movements and lower limb movements in one action. In the multilabel classification problem, labels are not completely independent, and there are some dependencies or mutual exclusions. In the multilabel classification task, the number of labels is relatively large, which makes the relationship between categories very complex and difficult to describe [6]. Therefore, compared with the traditional single-label classification task, multilabel classification is more complex. Figure 1 shows the difference between single-label classification and multilabel classification.Figure 1
Example of single label and multilabel.Multilabel comprehensive recognition technique is a multilabel learning process-based comprehensive learning approach. A kind of supervised learning issue is multilabel learning. Traditional classification learning algorithms presume that each sample has just one label and interpret the data from a single perspective [7]. Each sample may be connected with many category labels in real-world applications. The strategies used to solve the multilabel classification issue may be classified into two categories: problem transformation and algorithm modification. The multilabel classification problem is generally transformed into alternative learning situations using problem transformation methods, such as changing the multilabel classification issue into numerous binary classification problems [8]. The strategy based on algorithm adaptation entails adapting a current popular algorithm in the classification process to handle multilabel data. With the advancement of deep learning in recent years, several researchers have adapted CNN, RNN, and other deep learning algorithms for use in multilabel classification [9]. Changing the output layer of a neural network is a common modification method. Figure 2 depicts the multilabel learning strategy.Figure 2
Multilabel learning method.The relevant knowledge of RNN is summarized. RNN is a deep neural network that uses context state to mine time series information in data. Compared with convolutional neural network, RNN combines the state of the current model to calculate the output at each time. The structure of the pure RNN model has long-term dependence and may lose the ability to learn remote information [10]. The emergence of long-term a and b short-term memory network Wi successfully solves the problem of gradient disappearance. It is the most popular RNN network, which is widely used in speech recognition, natural language processing, video description, and behavior recognition. The input of the LSTM includes the network input γ of the current time, the LSTM output a of the last time, and the storage unit h of the previous time, and the output x includes the output λ of the current time and the storage unit θ of the current time. It uses input gate and forgetting gate to control the storage unit and combined with output gate to describe the long-distance dependence more effectively. The input gate, forgetting gate, and output gate are calculated as follows:(1)ct=aiWi⋅hr−1,xr+bi−γ,ft=afWf⋅ht−1,xr+bf−λor=aoWo⋅ht−1,xt+bo−θ,,where i,f,o represent the connection weight moments of input gate, forgetting gate, and output gate, respectively, h and p and ilr are the offset of input forgetting gate and output gate, respectively. The output of LSTM is jointly calculated by the memory unit and output gate(2)c¯i=tanctWc⋅ht−1+xl−p,d=ft×cr−1+ilr,hz=or×tanhci−p,where V0 is the candidate state at time t, W is the weight matrix of the candidate state, cr−1 is the candidate bias, n is the storage unit at time t, and k is the final output at time u. If the tag is located on the conveyor belt and assuming that the tag moves in a uniform straight line relative to the antenna in every very short time, the tag movement speed at all times can be calculated. The relative motion speed between the tag and the antenna is(3)Vr=V0cosθ−kn+t−u,where(4)cosθ=c¯id+hzhz2+kv2−1.In order to obtain the coordinate information of human key points in human posture estimation based on multilabel classification, the Gaussian modeling method is used to obtain the confidence map of keypoint position, and the confidence map is used to represent the key points, with the value in the confidence map expressed as the probability of a key point position. The key point location confidence graph may be written as(5)Cj,k=expVr−p−xj,kδ2−k,Cjp=WmaxSj,kp−lz,wherexj,k represents the joint points of the human body, l represents the k-th target person in the image, z represents the coordinates of the predicted person in the image, and W and ∈ r represent the specific coordinate position of the j-th joint point of the k-th target person, δ. It is a minimum value, which can ensure that it can have certain feasibility in the training process of the model.


## 2.2. Human Motion Feature Classification Algorithm in Moving Scene

The main difference between multilabel learning and traditional learning is that the increase in the number of labels makes the output space grow exponentially, and the computational complexity increases significantly [11]. There are three common processing methods for multilabel learning process. The simplest and direct method is to ignore the internal relationship between multiple labels and transform multilabel learning into multiple traditional single-label learning processes [12]. Let x represent the sample space, xi represent yj possible labels. For multilabel learning, it is to learn a function E from sample labels. If the multilabel learning process is transformed into Q single-label learning, the probability of label y obtained for each learning of sample x is(6)Pi=EQ,xi|yj∈hxi,1≤j≤k.The movement speed of the tag relative to the antenna causes the Doppler shift in the tag echo signal. The frequency of the echo signal is higher than the original signal when the tag is near to the antenna [13]. The larger the frequency shift, the quicker the tag travels away from the antenna; when the tag is far away from the antenna, the echo signal has a lower frequency than the original signal. The larger the frequency shift, the quicker the tag travels relative to the antenna [14]. The frequency difference between the antenna’s broadcast and received signals may then be determined as follows:(7)fd=VrPic⋅f0−Cj,k+Cjp.According to the formula, the real-time Doppler is(8)fdpt=Vxpt⋅1xp2t+yp2t+Pi⋅VrPic⋅f0.Combining all the learning results, we can get a set of multiple tagsxpt, and the total learning method is a set of single learning functions ypt, where(9)vypt=h1xi,h2xi,⋯,hqxi.Document classification, information retrieval, and bioinformatics are just a few of the disciplines where multilabel classification learning is applied. However, the approach of multilabel learning has not been thoroughly investigated and utilised in the area of picture and target recognition [15]. A single-label identification technique for targets is the standard target recognition approach. A target can only have one label in the same recognition algorithm, which only explains and characterises the target from one perspective [16]. For example, via the detection and identification of targets in image data, we may gain extremely comprehensive classification results for the appearance of the target in various top-level target recognition algorithms based on convolutional neural networks.


## 2.3. Implementation of Human Action Multilabel Classification in Moving Scene

The label’s location and motion state will be dynamically shown by the scene module, as illustrated in Figure3, which is an image of the module’s real-time scene [17]. The simulation approach also calculates each tag’s multidimensional information, adds suitable noise, and outputs the simulated multidimensional data using the reader’s R selective reading concept.Figure 3
Method composition block diagram of human motion scene platform.In order to clearly and accurately describe the data requirements of users, it is usually necessary to establish a conceptual model of data. The conceptual structure design of the database is the most important part of the whole database design. The purpose of conceptual structure design is to produce the conceptual structure of data. The data entities involved in the method include user entity, data entity, and message entity [18]. The conceptual design of the database is to find out the relationship between data and model the data with the management method of the database, so as to obtain the conceptual model of data. E-R diagram is a conceptual model, through which the relationship between data can be clearly displayed. In the form of an E-R diagram, the following depicts the entity structure and connection between entities: user name, user password, and user authority are the three entity properties of users. The primary key is set to the user ID, and the user authority is configured to differentiate between regular and administrator users [19]. Its major business is to give consumers with limb posture correction services while shaking their feet and jumping rope as an intelligent analysis technique of rope skipping at the end of artificial intelligence mobile phone. Users who want to assess their posture during rope skiing should use their mobile phone to log in, examine information about the successful admission test, upload their own rope skiing video, and get the final analysis results [20]. The method’s core business process is separated into six following elements, as illustrated in Figure 4: user login business process, model upload business process, user management business process, message publishing business process, video upload business process, and analysis result in watching the business process.Figure 4
Function module of intelligent rope skipping teaching method.Human motion analysis only focuses on the overall performance, is not specific to various limb movements, and cannot work effectively in a complex environment [21]. In order to effectively solve this problem, the motion analysis algorithm designed in this paper is based on this method. Because this method has strong generalization ability and universality, in the selection of this method, we use the network model of CNN structure and the LSTM network joint model with an attention mechanism to obtain robustness data through CNN and analyze the standard of limb movement through the joint LSTM model [22]. Figure 5 gives the specific flow chart. According to the flow chart, it can be clearly seen that the analysis of forward shaking double foot jump mainly includes two modules: one module is used to obtain the key point information of the human body, and the other module is to mathematically model the obtained key point information and establish a multilabel classification network model.Figure 5
Design flow of rope skipping action analysis model.To assess athletes’ limb motions during forward shaking two-foot rope skipping, the video stream data are first converted into human key point coordinate data using an enhanced multilabel classification network model, and then, a time series containing human key point coordinates is created [23]. In the process of robust rope skipping, the data are then preprocessed to acquire the limb key point coordinate data. Because the coordinates of the key locations of body motions are a time series with particular connections, this approach is eventually applied to the analysis of body movements using the algorithm transformation method in the multilabel classification algorithm [24]. According to the aforementioned analysis, the posture analysis issue in the rope skipping process may be reduced to the problem of determining whether the athletes’ body swing standard in the rope skipping process is met, which is then changed into a multilabel classification problem.


## 3. Analysis of Experimental Results

In order to show the superiority of the algorithm and test the accuracy and robustness of the algorithm in this chapter, this chapter will compare the tracking effect of the algorithm with the mainstream algorithms in the current target tracking field in a variety of complex environments through experiments, select IVT, ML, struct, TLD, and DLT algorithms as the comparison algorithm, use opencv as the programming platform parameters, and use the default values. Under the condition of the Microsoft Kinect camera, the experimental database is established by using a depth sensor, as shown in Table1.Table 1
Experimental database.
DatabaseTypeResolving powerRemarksMSRRGBD340∗254015 fps; Txt file to the customs clearance nodeRGBDRGBD360∗270Indoor daily exercise; single person; whole bodyLIRIS databaseRGBD340∗280Indoor office action or interaction; single or whole bodyChalearn gesture databaseRGBD240∗46016 daily behaviors; 15 fps; Txt file joint pointKitchen databaseRGBD240∗280The camera is installed on the kitchen table; mainly arm movement the camera is installed on the kitchen table; mainly arm movementReading act databaseRGBD320∗24019 actions; 30 fps; two camerasThe data used in the experiment select video sequences suitable for different complex environments in common target tracking data sets [25]. These video sequences include a variety of interference conditions encountered in the process of target tracking and changes in the target itself, such as partial occlusion, illumination change, and size ratio change. Experiments such as in-plane and out-of-plane rotation motion blur compare the accuracy of target tracking between the algorithms in this chapter and the above target tracking algorithms in the test set video. For each video, 100 video frames containing the target are selected as the frames to be tracked, different tracking algorithms are used for target tracking, and the number of video frames successfully tracked is recorded. Table 2 shows the quantitative comparison of various algorithms in the comparison experiment.Table 2
Comparison data of accuracy of classification methods.
Video sequenceIVTMILTLDStruckDLTPaper algorithmWoman0.750.100.250.910.460.92Walking0.910.860.770.850.860.85Trellis0.760.350.590.780.770.82Boy0.380.780.760.680.410.86CarDark0.850.790.850.860.810.85Jumping0.280.820.760.550.260.83Tiger0.150.160.190.190.180.26Professionals get the data labels by analysing the video and labelling it according to the time segment Table3. Maintain the body upright (straight but not rigid), tighten the left arm with the body, tighten the right arm with the body, shake the rope with the left wrist, shake the body with the right wrist, and keep the left and right arms horizontal are the six data labels. The labels are chosen based on the rope skipping abilities as shown in the middle school entrance test. The particular contents are shown in Table 4, with a total of 64 options. Assemble the data’s feature set X and label set Y, and keep track of the names of limb action markers as follows.Table 3
Name of limb movement mark.
ClassificationDescribeKeep your body at attentionJudge whether the whole body remains at attentionLeft arm and bodyJudge whether the left arm is close to the left bodyTighten the right arm with the bodyJudge whether the right arm is close to the right bodyLeft wrist swing ropeJudge whether the wrist drives the rope to shakeRight wrist swing ropeJudge whether the wrist drives the rope to shakeBalance the left and right armsJudge whether the left and right arms are balancedTable 4
Comparison between this method and other methods.
ModelClassification accuracy (%)Precision (%)Recall (%)F1 (%)Paper method95.396.595.295.9ALSTM-BN92.995.689.891.8LSTM-BN93.193.192.292.5ALSTM92.192.888.991.5LSTM89.888.889.388.9ML-KNN79.875.675.275.6SVM77.875.277.776.5The rope skipping process is a long-time sequence analysis process, which needs to use a sliding window to segment the data. In order to find out the appropriate sliding window length, this paper sets up three groups of experiments for analysis, namely frame cumulative coordinates, 15 frame cumulative coordinates, and 20 frame cumulative coordinates, and the step size is set to 30% data overlap. Figure6 shows the label classification comparison test results, as follows.Figure 6
Comparison of test results.Based on the comparison test results, compared with the traditional methods, this method has better classification accuracy in the process of practical application. In order to further verify the performance of the model proposed in this paper, this method is compared with multiple models in the experiment, as shown in Table4.According to the findings, our technique outperforms the standard machine learning algorithm in the multilabel classification issue of rope skipping action analysis. The influence of the batch domestication approach on the model’s performance is also investigated in this article. Finally, the experiment demonstrates that this technique performs the best across all indices, whereas SVM performs the poorest. This demonstrates the applicability of the strategy presented in this study.


## 4. Conclusion

Aiming at the complex problem of rope skipping motion analysis based on vision, this paper transforms the video analysis problem into the analysis problem of human key point coordinates. The research of this paper can be divided into two stages. The first stage is the process of obtaining robust coordinate points, and the second stage is the process of modeling the obtained coordinate points. In the second stage, in order to improve the efficiency and accuracy of attitude analysis, this paper creatively transforms the multilabel classification and uses the lightweight network mobilenetv2 to replace the V gg19 network model in the process of graph feature extraction. In the final loss function, because the multilabel classification takes the loss of two stages as the final loss, ignoring that the loss of a single stage in the two stages may affect the final result in some way, this study adds two weights and a penalty term to the final loss function, so as to obtain more accurate human key point coordinate data. It provides data support for the attitude analysis in the subsequent rope skipping process. In order to analyze whether the limb movements in rope skipping are standardized or not, this paper transforms the movement analysis problem into a multilabel classification problem based on deep learning, designs the network model through the adaptive method in the multilabel problem, and proposes an LSTM joint model based on attention mechanism. In the experimental process of multilabel classification, in order to obtain a high-performance network model, the selection and comparison experiment of multiple groups of super parameters is set in the process of model training. In order to verify the effectiveness of the model, the proposed model is compared with multiple models. The experimental results show that the proposed model can solve the practical application problems.



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*Source: 8202383-2022-05-16.xml*