# The Motor Action Analysis Based on Deep Learning

Authors: TianYu Zhang
Journal: Scientific Programming
 (2022)

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## Abstract

For the slow speed and low accuracy of slow motor action recognition methods, this study proposes a motor action analysis method based on the CNN network and the softmax classification model. First, in order to obtain motor action feature information, by using static spatial features of BN-inception based on CNN network extracted actions and high-dimensional features of 3D ConvNet, then based on softmax classifier structure and realizing taxonomic recognition of the motor actions. Finally, through the decision-layer fusion and time semantic continuity optimization strategy, the motion action recognition accuracy is further improved and the more efficient motion action classification recognition is realized. The results show that the proposed method can complete the motor action analysis and achieve the classification recognition accuracy to 83.11%, which has certain practical value.


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## Body

## 1. Related Work

Movement action analysis is an important branch of computer vision, which also involves data mining, image processing, and other content, and is widely used in sports, music playing, and many other scenes. Due to the complex patterns of movement action and the big differences in movement rules of different individuals, the movement action recognition analysis is somewhat challenging and has attracted the keen attention of relevant researchers. At present, motion action analysis mainly focuses on motion detection and recognition and has achieved remarkable research results. For example, Hua-xin Zhang et al. realized the estimation of human posture by capturing 3D motion [1]. In addition, Xiaoqiang Li et al. applied the convolutional neural network to action recognition. The results show that the action recognition results of a convolutional neural network with the dual-attention mechanism are comparable to the recognition results of the latest algorithm [2]. Haohua Zhao et al. extracted intraframe feature vectors by deep network training to form a multimode feature matrix. The matrix is input into CNN to achieve feature classification. The results show that the proposed method has better performance than the existing LSTM in video action recognition [3]. Ran Cui et al. analyzed the motion by constructing skeletal joints and static and dynamic features. The prediction of motion is realized through motion recognition [4]. Manikandaprabu et al. detected the ROI of the human body using the combination of background subtraction and frame subtraction [5]. Then the CAMShift algorithm is adopted for recognition. The results show that this method has good precision and has great advantages compared with the most advanced algorithms. It can be seen from the above studies that convolutional neural networks are widely used in action recognition, among which the CNN attracts more attention due to its unique characteristics.Despite the great progress in motor motion analysis, its overall performance still needs to be improved, mainly due to the blurred boundary of motor motion, which increases the difficulty of the study. For the difficulties, this study applies powerful deep learning capabilities, based on the CNN network and the softmax classifier, and proposes a deep learning-based motion action analysis method.


## 2. Basic Methods

### 2.1. Network Profile

The CNN network is a representative algorithm of deep learning, which is commonly used in image processing, video image recognition, and other fields, with the characteristics of simple structure and strong expansion performance. Its basic module includes a convolution layer, a pooling layer, and a full connection layer, as shown in Figure1. The convolution layer is responsible for extracting the local features of the input image to obtain different feature maps; the pooling layer reduces the dimension of the extracted features of the convolution layer to retain important information while reducing the risk of overfitting due to nonessential information. Common pooling layer settings include average pooling and maximum pooling; the full connection layer plays a classification role in the network and enables sample data classification by mapping the learned feature data to the space of sample markers [6–11].Figure 1
Standard CNN network structure.In recent years, with the deepening of deep learning research, a huge breakthrough in CNN network structure has been made. In terms of spatial feature extraction, the network continuously deepens, forms the inception structure module, as shown in Figure2, which greatly reduces the quantities of network parameters, realizes the multiscale processing fusion of images, and obtains a better feature representation [12–15].Figure 2
Structural representation of inception. (a) Inception v1 module. (b) Inception v2 module.
(a)(b)In terms of spatiotemporal feature extraction, a 3D ConvNet network emerged, acquiring spatiotemporal features by performing both convolutional and pooling operations in time and space simultaneously, further improving the model performance.


### 2.2. Softmax Model Introduction

The softmax model is a multiclassifier based on the logistic regression model that can handle multiclassification problems. In the softmax model, for a given inputx, the hypothetical function hθ(x) was used to estimate the probability value of each category j, p (y = j|x), i.e., estimating the probability of each classification result of x. Suppose the k-dimensional vector output by the function is the probability of these estimated k values. The hθ(x) form is as follows:(1)hθxi=pyi=1|xi;θpyi=2|xi;θ⋮pyi=k|xi;θ=1∑j=1keθjTxieθ1Txieθ2Txi⋮eθkTxi.In the formula,θ1,θ2,…,θk∈ℜn+1 represents model parameters, 1/∑j=1keθjTxx is to normalize the probability distribution so that the sum of all probabilities is one.Thus, the probability that softmax classifiesx into category j can be expressed as [16–18](2)pyi=j|xi;θ=eθjTxi∑l=1keθlTxi.Motor action analysis is a multitaxonomic recognition process. According to the above analysis, in order to better analyze motor actions, this paper first used the feature extraction method to acquire motor representation based on the CNN network and then identified by the softmax classifier to realize the analysis of motor actions.


## 2.1. Network Profile

The CNN network is a representative algorithm of deep learning, which is commonly used in image processing, video image recognition, and other fields, with the characteristics of simple structure and strong expansion performance. Its basic module includes a convolution layer, a pooling layer, and a full connection layer, as shown in Figure1. The convolution layer is responsible for extracting the local features of the input image to obtain different feature maps; the pooling layer reduces the dimension of the extracted features of the convolution layer to retain important information while reducing the risk of overfitting due to nonessential information. Common pooling layer settings include average pooling and maximum pooling; the full connection layer plays a classification role in the network and enables sample data classification by mapping the learned feature data to the space of sample markers [6–11].Figure 1
Standard CNN network structure.In recent years, with the deepening of deep learning research, a huge breakthrough in CNN network structure has been made. In terms of spatial feature extraction, the network continuously deepens, forms the inception structure module, as shown in Figure2, which greatly reduces the quantities of network parameters, realizes the multiscale processing fusion of images, and obtains a better feature representation [12–15].Figure 2
Structural representation of inception. (a) Inception v1 module. (b) Inception v2 module.
(a)(b)In terms of spatiotemporal feature extraction, a 3D ConvNet network emerged, acquiring spatiotemporal features by performing both convolutional and pooling operations in time and space simultaneously, further improving the model performance.


## 2.2. Softmax Model Introduction

The softmax model is a multiclassifier based on the logistic regression model that can handle multiclassification problems. In the softmax model, for a given inputx, the hypothetical function hθ(x) was used to estimate the probability value of each category j, p (y = j|x), i.e., estimating the probability of each classification result of x. Suppose the k-dimensional vector output by the function is the probability of these estimated k values. The hθ(x) form is as follows:(1)hθxi=pyi=1|xi;θpyi=2|xi;θ⋮pyi=k|xi;θ=1∑j=1keθjTxieθ1Txieθ2Txi⋮eθkTxi.In the formula,θ1,θ2,…,θk∈ℜn+1 represents model parameters, 1/∑j=1keθjTxx is to normalize the probability distribution so that the sum of all probabilities is one.Thus, the probability that softmax classifiesx into category j can be expressed as [16–18](2)pyi=j|xi;θ=eθjTxi∑l=1keθlTxi.Motor action analysis is a multitaxonomic recognition process. According to the above analysis, in order to better analyze motor actions, this paper first used the feature extraction method to acquire motor representation based on the CNN network and then identified by the softmax classifier to realize the analysis of motor actions.


## 3. Motor Motion Analysis Method Based on Deep Learning

### 3.1. Characteristic Extraction Based on CNN Network

The analysis of motor actions includes the appearance features and action context information of the data. In order to obtain well robust action characteristics, this study, based on the CNN network, represents the motion action appearance features and motion features by extracting the low-dimensional static features and high-dimensional spatial and temporal features of the data, respectively, to represent the motion action features, as shown in Figure3.Figure 3
Structured flowchart of deep feature extraction.


#### 3.1.1. Static Spatial Characteristic Extraction

In this paper, the BN-inception network with high accuracy and efficiency extracts the static spatial features of motion action, whose network structure is shown in Table1. Specific extraction steps are as follows [19–22]:Step 1: pretreatment for image cutting, motion action images, and image level flipping to obtain a matrix that meets the BN-Inception network inputStep 2: tacking the input matrix through a pretrained BN-inception model with feature extraction and calculating the feature average of each dimension of different image parts according to equation (3)(3)dj=1M∑i=1,Mfeai,j,j=1,2,…,10,j=1,2,…1024.Step 3: obtaining final feature representation of a single-frame image, as in formula (4)(4)Staticfea=d1,d2,…,d1024.Table 1
BN-inception network structure.
TypeKernel size/stepOutput sizeConvolution7 × 7/2112 × 112 × 64Max pool3 × 3/256 × 56 × 64Convolution3 × 3/156 × 56 × 192Max pool (3a)2 × 3/228 × 28 × 192Inception (3b)28 × 28 × 2S6Inception (3a)28 × 28 × 320Inception (3c)stride214 × 14 × 576Inception (3a)14 × 14 × 576Inception (4a)14 × 14 × 576Inception (4b)14 × 14 × 608Inception (4c)14 × 14 × 60×Inception (5a)stride27 × 7 × 1056Inception (5b)7 × 7 × 1024Inception (5c)7 × 7 × 1024Avg pool7 × 7/11 × 1 × 1024Decreeingf=Staticfea, a characteristic representation of a sample of motion action data is a two-D matrix F=f1,f2,…,fN of N x D. In it, N represents the total number of motion action video segment frames, fn represents the single-frame image feature, and D represents the feature dimension size. And so forth, all the static spatial features of the motor movement can be obtained.


#### 3.1.2. Dynamic Spatiotemporal Feature Extraction

In this paper, 3D ConvNet high-dimensional spatial features and the network structure are shown in Figure4. The specific extraction method is as follows:Step 1: A multiscale frame sequence is entered and divides the video into different scale segments according to the set window sizeStep 2: the spatiotemporal feature representation of each segmentation timing segment fc6 layer is extracted by network forward propagation, such as follows:(5)Dynamicfea=d1,d2,…,d4096.Decreedf=Dynamicfea, for a sample of motion action video data with a total frame number as N, if the time overlap is 50%, the extracted action feature representation F=f1,f2,…,fK is a K × D 2D matrix. Where K=⌊N−wi/2/wi/2⌋, fk represents the input fragment features, and D represents the dimension size. With the above operation, the spatiotemporal features of all motion action video samples are extracted.Figure 4
3D ConvNet network structure block diagram.


### 3.2. Classification Identification of Motion Actions Based on the Softmax Model

#### 3.2.1. Model Structure Construction

Based on the above feature extraction, the softmax model structure was designed as shown in Figure5 in this study. This classification network includes three fully connected layers for selecting parameters, one dropout layer to prevent overfitting, and finally, connecting the softmax loss. During training, the parameters were optimized by using small-batch gradient descent [23–25].Figure 5
Structure diagram of the classification network based on softmax.Considering that CNN network-based features include low-dimensional static features extracted by BN-inception and high-dimensional spatiotemporal features extracted from C3D, to improve the classification effect, different dimensions were trained separately in the study. To set the number of full connected-layer neurons of the low-dimensional feature softmax classification network for fc1 = 512, fc2 = 256, fc3 = 6, and fc1 = 1024, fc2 = 512, and fc3 = 6, while the high-dimensional feature softmax classification network for fc1 = 1024, fc2 = 512, fc3 = 6.


#### 3.2.2. Model Training and Testing

Specific procedures of training and testing of the above softmax model are as follows:Step 1: The feature matrix of the training sample data is built. Assuming training sample QTY isM, the feature matrix of sample i (i = 1,2,3, …, M) is Fi = N x D, N represents the number of training sample frames, and D represents the size of the feature dimension extracted per frame. The total number of M samples can be represented as(6)F=∑i=1MFi.To train the soft model by the network structure in Figure5, the number of fc3 output neurons is the same as in the categorical category C and the output vector X=xi,j=1,2,…,C. Therefore, the corresponding marker probability yj for the output value xj obtained by the softmax function can be expressed as [26](7)yj=softmaxxj=expxj∑j=1Cexpxj.Step 2: to minimize the loss, a cross-entropy loss function was used, as shown in equation (8) to minimize the loss during training.(8)Hyy=−∑jyjlogyj.In the abovementioned formula, yj represents the score distribution of classification by softmax; and yj represents the target true value. Thus, the C category final-loss-value loss is the average of the cross-entropy loss for each category, as in the following formula[27]:(9)Loss=meanHyy.Step 3: after optimizing the training parameters to speed up the model training, the model parameters were optimized by using M-BGD. During optimization, weight optimization is as in formula (10)–(14) [28].(10)errors=yj−softmaxxj.(11)Δw=α×xj×errors,(12)Δβ=α×errors,(13)w=w−Δw,(14)β=β−Δβ,where errors represent the weight error, α represents the learning rate, w represents the weight, and β represents the deviation.Step 4: model testing is performed. The best softmax classification model obtained by training is selected to classify and identify the test dataset, and the corresponding action category of the maximum classification score in C categories output from the following layer is selected as the classification result of the test data, and it is expressed as follows:(15)class=indexmaxy1,y2,…,yc.


### 3.3. Decision-Making Layer-Based Fusion

Considering the diversity, complexity, and ambiguity among motor movements and the different key movements, different movement performance modes need to be mixed together to improve the classification and recognition accuracy of motor movements. Currently, common fusion methods include feature-based and decision-based fusion. Since feature-based fusion stitched and fused the features, it may lead to mutual interference among features and learning efficiency. In contrast, the fusion method based on the decision-layer only needs to determine action categories based on different classification confidence sizes, which is efficient and simple. Therefore, this paper fuses the classification results in a decision-layer fusion-based manner.The fusion structure based on the decision-layer fusion mode is shown in Figure6. Assuming the number of classified motor action categories is C, for individual test data X, the classification result is where resultX=s1,s2,…,sC, in it, si represents the classification score of category i, i∈1,2,…,C, the N road classification identification results can be summarized according to formula (16).(16)resultXi′=1N∑n=1NresultXni,n∈1,2,…,N.Figure 6
Fusion structure block diagram.Then to sum and average the data to obtain the final classification results(17)resultX′=s1′,s2′,…,s3′.In the formula,Xni represents the classification score of sample X in the classifier n, and Xi′ is the classification score of X is category i after fusion. The classification result based on resultX′ maximum is the final classification category of sample X, as in the following formula:(18)label=maxresultXi′,i∈1,2,…C.


### 3.4. Time-Based Semantic Continuity Optimization

Movement actions have a certain time sequence, so there are a large number of redundant and incomplete trivial fragments during sequential action detection. To further improve the detection performance of the method according to temporal semantic continuity, this study proposes an optimization strategy based on the characteristics of motion action.First, to model the motion action time sequence semantics and time sliding window classification at different scales, initial detection results are taken. Defined all detection results of a motor action asX, segci,wk indicated a category of ci in X, and a set of tests with a sliding window size of wk can be represented as(19)X=segci,wk,(20)segci,wk=sn,en,gnn=1Ni,k.In the formula, C represents the total number of categorical categories; K represents the total number of sliding windows;Ni,k is the number of action time segments detected in ci and wk; sI,eI are the start and end times of detected action segments; and gn represents the classification score.Then to calculate the classification score, the temporal overlap values of different time periods, as in equations (21) and (22), and compare them with the set threshold.(21)gI−gs<θ,(22)IOUpl,ps≥U×minTl,Tsl,s∈1,2,…Ni,k,⋮l≠s.In the formula,P=segci,wk represents the detection results of the same category ci and the same scale wk, pl = (sl, el, gl), and ps = (ss, es, gs) both are one detection fragment of P; gl−gs, IOUpl,ps represent the score difference and time overlap value, respectively; Tl=el−sl,Ts=es−ss the execution time of two actions, and θ, U the set threshold.The two action segments were integrated if the two action segment classification scores were less than the set threshold and the time overlap was greater than the threshold.Considering that the motion action obtained by the above operation is synthesized from multiple incomplete fragments, which partially destroys the spatiotemporal structure of the action, it also needs to conduct classification detection. This study uses a 3D convolutional neural network with good classification performance for reclassification. Furthermore, to ensure more accurate classification results and reduce the classification impact of sliding windows on motor movements, it statistically calculated the weight scores of different sliding windows for different categories, further adjusted the classification confidence scores, and trained classification models by softmax classifier and overlap loss function.Finally, to reduce redundancy detection in the presence of sliding windows at different scales, they were processed by nonmaximum inhibition to bring the final results close to the start and end of the motor action.


## 3.1. Characteristic Extraction Based on CNN Network

The analysis of motor actions includes the appearance features and action context information of the data. In order to obtain well robust action characteristics, this study, based on the CNN network, represents the motion action appearance features and motion features by extracting the low-dimensional static features and high-dimensional spatial and temporal features of the data, respectively, to represent the motion action features, as shown in Figure3.Figure 3
Structured flowchart of deep feature extraction.


### 3.1.1. Static Spatial Characteristic Extraction

In this paper, the BN-inception network with high accuracy and efficiency extracts the static spatial features of motion action, whose network structure is shown in Table1. Specific extraction steps are as follows [19–22]:Step 1: pretreatment for image cutting, motion action images, and image level flipping to obtain a matrix that meets the BN-Inception network inputStep 2: tacking the input matrix through a pretrained BN-inception model with feature extraction and calculating the feature average of each dimension of different image parts according to equation (3)(3)dj=1M∑i=1,Mfeai,j,j=1,2,…,10,j=1,2,…1024.Step 3: obtaining final feature representation of a single-frame image, as in formula (4)(4)Staticfea=d1,d2,…,d1024.Table 1
BN-inception network structure.
TypeKernel size/stepOutput sizeConvolution7 × 7/2112 × 112 × 64Max pool3 × 3/256 × 56 × 64Convolution3 × 3/156 × 56 × 192Max pool (3a)2 × 3/228 × 28 × 192Inception (3b)28 × 28 × 2S6Inception (3a)28 × 28 × 320Inception (3c)stride214 × 14 × 576Inception (3a)14 × 14 × 576Inception (4a)14 × 14 × 576Inception (4b)14 × 14 × 608Inception (4c)14 × 14 × 60×Inception (5a)stride27 × 7 × 1056Inception (5b)7 × 7 × 1024Inception (5c)7 × 7 × 1024Avg pool7 × 7/11 × 1 × 1024Decreeingf=Staticfea, a characteristic representation of a sample of motion action data is a two-D matrix F=f1,f2,…,fN of N x D. In it, N represents the total number of motion action video segment frames, fn represents the single-frame image feature, and D represents the feature dimension size. And so forth, all the static spatial features of the motor movement can be obtained.


### 3.1.2. Dynamic Spatiotemporal Feature Extraction

In this paper, 3D ConvNet high-dimensional spatial features and the network structure are shown in Figure4. The specific extraction method is as follows:Step 1: A multiscale frame sequence is entered and divides the video into different scale segments according to the set window sizeStep 2: the spatiotemporal feature representation of each segmentation timing segment fc6 layer is extracted by network forward propagation, such as follows:(5)Dynamicfea=d1,d2,…,d4096.Decreedf=Dynamicfea, for a sample of motion action video data with a total frame number as N, if the time overlap is 50%, the extracted action feature representation F=f1,f2,…,fK is a K × D 2D matrix. Where K=⌊N−wi/2/wi/2⌋, fk represents the input fragment features, and D represents the dimension size. With the above operation, the spatiotemporal features of all motion action video samples are extracted.Figure 4
3D ConvNet network structure block diagram.


## 3.1.1. Static Spatial Characteristic Extraction

In this paper, the BN-inception network with high accuracy and efficiency extracts the static spatial features of motion action, whose network structure is shown in Table1. Specific extraction steps are as follows [19–22]:Step 1: pretreatment for image cutting, motion action images, and image level flipping to obtain a matrix that meets the BN-Inception network inputStep 2: tacking the input matrix through a pretrained BN-inception model with feature extraction and calculating the feature average of each dimension of different image parts according to equation (3)(3)dj=1M∑i=1,Mfeai,j,j=1,2,…,10,j=1,2,…1024.Step 3: obtaining final feature representation of a single-frame image, as in formula (4)(4)Staticfea=d1,d2,…,d1024.Table 1
BN-inception network structure.
TypeKernel size/stepOutput sizeConvolution7 × 7/2112 × 112 × 64Max pool3 × 3/256 × 56 × 64Convolution3 × 3/156 × 56 × 192Max pool (3a)2 × 3/228 × 28 × 192Inception (3b)28 × 28 × 2S6Inception (3a)28 × 28 × 320Inception (3c)stride214 × 14 × 576Inception (3a)14 × 14 × 576Inception (4a)14 × 14 × 576Inception (4b)14 × 14 × 608Inception (4c)14 × 14 × 60×Inception (5a)stride27 × 7 × 1056Inception (5b)7 × 7 × 1024Inception (5c)7 × 7 × 1024Avg pool7 × 7/11 × 1 × 1024Decreeingf=Staticfea, a characteristic representation of a sample of motion action data is a two-D matrix F=f1,f2,…,fN of N x D. In it, N represents the total number of motion action video segment frames, fn represents the single-frame image feature, and D represents the feature dimension size. And so forth, all the static spatial features of the motor movement can be obtained.


## 3.1.2. Dynamic Spatiotemporal Feature Extraction

In this paper, 3D ConvNet high-dimensional spatial features and the network structure are shown in Figure4. The specific extraction method is as follows:Step 1: A multiscale frame sequence is entered and divides the video into different scale segments according to the set window sizeStep 2: the spatiotemporal feature representation of each segmentation timing segment fc6 layer is extracted by network forward propagation, such as follows:(5)Dynamicfea=d1,d2,…,d4096.Decreedf=Dynamicfea, for a sample of motion action video data with a total frame number as N, if the time overlap is 50%, the extracted action feature representation F=f1,f2,…,fK is a K × D 2D matrix. Where K=⌊N−wi/2/wi/2⌋, fk represents the input fragment features, and D represents the dimension size. With the above operation, the spatiotemporal features of all motion action video samples are extracted.Figure 4
3D ConvNet network structure block diagram.


## 3.2. Classification Identification of Motion Actions Based on the Softmax Model

### 3.2.1. Model Structure Construction

Based on the above feature extraction, the softmax model structure was designed as shown in Figure5 in this study. This classification network includes three fully connected layers for selecting parameters, one dropout layer to prevent overfitting, and finally, connecting the softmax loss. During training, the parameters were optimized by using small-batch gradient descent [23–25].Figure 5
Structure diagram of the classification network based on softmax.Considering that CNN network-based features include low-dimensional static features extracted by BN-inception and high-dimensional spatiotemporal features extracted from C3D, to improve the classification effect, different dimensions were trained separately in the study. To set the number of full connected-layer neurons of the low-dimensional feature softmax classification network for fc1 = 512, fc2 = 256, fc3 = 6, and fc1 = 1024, fc2 = 512, and fc3 = 6, while the high-dimensional feature softmax classification network for fc1 = 1024, fc2 = 512, fc3 = 6.


### 3.2.2. Model Training and Testing

Specific procedures of training and testing of the above softmax model are as follows:Step 1: The feature matrix of the training sample data is built. Assuming training sample QTY isM, the feature matrix of sample i (i = 1,2,3, …, M) is Fi = N x D, N represents the number of training sample frames, and D represents the size of the feature dimension extracted per frame. The total number of M samples can be represented as(6)F=∑i=1MFi.To train the soft model by the network structure in Figure5, the number of fc3 output neurons is the same as in the categorical category C and the output vector X=xi,j=1,2,…,C. Therefore, the corresponding marker probability yj for the output value xj obtained by the softmax function can be expressed as [26](7)yj=softmaxxj=expxj∑j=1Cexpxj.Step 2: to minimize the loss, a cross-entropy loss function was used, as shown in equation (8) to minimize the loss during training.(8)Hyy=−∑jyjlogyj.In the abovementioned formula, yj represents the score distribution of classification by softmax; and yj represents the target true value. Thus, the C category final-loss-value loss is the average of the cross-entropy loss for each category, as in the following formula[27]:(9)Loss=meanHyy.Step 3: after optimizing the training parameters to speed up the model training, the model parameters were optimized by using M-BGD. During optimization, weight optimization is as in formula (10)–(14) [28].(10)errors=yj−softmaxxj.(11)Δw=α×xj×errors,(12)Δβ=α×errors,(13)w=w−Δw,(14)β=β−Δβ,where errors represent the weight error, α represents the learning rate, w represents the weight, and β represents the deviation.Step 4: model testing is performed. The best softmax classification model obtained by training is selected to classify and identify the test dataset, and the corresponding action category of the maximum classification score in C categories output from the following layer is selected as the classification result of the test data, and it is expressed as follows:(15)class=indexmaxy1,y2,…,yc.


## 3.2.1. Model Structure Construction

Based on the above feature extraction, the softmax model structure was designed as shown in Figure5 in this study. This classification network includes three fully connected layers for selecting parameters, one dropout layer to prevent overfitting, and finally, connecting the softmax loss. During training, the parameters were optimized by using small-batch gradient descent [23–25].Figure 5
Structure diagram of the classification network based on softmax.Considering that CNN network-based features include low-dimensional static features extracted by BN-inception and high-dimensional spatiotemporal features extracted from C3D, to improve the classification effect, different dimensions were trained separately in the study. To set the number of full connected-layer neurons of the low-dimensional feature softmax classification network for fc1 = 512, fc2 = 256, fc3 = 6, and fc1 = 1024, fc2 = 512, and fc3 = 6, while the high-dimensional feature softmax classification network for fc1 = 1024, fc2 = 512, fc3 = 6.


## 3.2.2. Model Training and Testing

Specific procedures of training and testing of the above softmax model are as follows:Step 1: The feature matrix of the training sample data is built. Assuming training sample QTY isM, the feature matrix of sample i (i = 1,2,3, …, M) is Fi = N x D, N represents the number of training sample frames, and D represents the size of the feature dimension extracted per frame. The total number of M samples can be represented as(6)F=∑i=1MFi.To train the soft model by the network structure in Figure5, the number of fc3 output neurons is the same as in the categorical category C and the output vector X=xi,j=1,2,…,C. Therefore, the corresponding marker probability yj for the output value xj obtained by the softmax function can be expressed as [26](7)yj=softmaxxj=expxj∑j=1Cexpxj.Step 2: to minimize the loss, a cross-entropy loss function was used, as shown in equation (8) to minimize the loss during training.(8)Hyy=−∑jyjlogyj.In the abovementioned formula, yj represents the score distribution of classification by softmax; and yj represents the target true value. Thus, the C category final-loss-value loss is the average of the cross-entropy loss for each category, as in the following formula[27]:(9)Loss=meanHyy.Step 3: after optimizing the training parameters to speed up the model training, the model parameters were optimized by using M-BGD. During optimization, weight optimization is as in formula (10)–(14) [28].(10)errors=yj−softmaxxj.(11)Δw=α×xj×errors,(12)Δβ=α×errors,(13)w=w−Δw,(14)β=β−Δβ,where errors represent the weight error, α represents the learning rate, w represents the weight, and β represents the deviation.Step 4: model testing is performed. The best softmax classification model obtained by training is selected to classify and identify the test dataset, and the corresponding action category of the maximum classification score in C categories output from the following layer is selected as the classification result of the test data, and it is expressed as follows:(15)class=indexmaxy1,y2,…,yc.


## 3.3. Decision-Making Layer-Based Fusion

Considering the diversity, complexity, and ambiguity among motor movements and the different key movements, different movement performance modes need to be mixed together to improve the classification and recognition accuracy of motor movements. Currently, common fusion methods include feature-based and decision-based fusion. Since feature-based fusion stitched and fused the features, it may lead to mutual interference among features and learning efficiency. In contrast, the fusion method based on the decision-layer only needs to determine action categories based on different classification confidence sizes, which is efficient and simple. Therefore, this paper fuses the classification results in a decision-layer fusion-based manner.The fusion structure based on the decision-layer fusion mode is shown in Figure6. Assuming the number of classified motor action categories is C, for individual test data X, the classification result is where resultX=s1,s2,…,sC, in it, si represents the classification score of category i, i∈1,2,…,C, the N road classification identification results can be summarized according to formula (16).(16)resultXi′=1N∑n=1NresultXni,n∈1,2,…,N.Figure 6
Fusion structure block diagram.Then to sum and average the data to obtain the final classification results(17)resultX′=s1′,s2′,…,s3′.In the formula,Xni represents the classification score of sample X in the classifier n, and Xi′ is the classification score of X is category i after fusion. The classification result based on resultX′ maximum is the final classification category of sample X, as in the following formula:(18)label=maxresultXi′,i∈1,2,…C.


## 3.4. Time-Based Semantic Continuity Optimization

Movement actions have a certain time sequence, so there are a large number of redundant and incomplete trivial fragments during sequential action detection. To further improve the detection performance of the method according to temporal semantic continuity, this study proposes an optimization strategy based on the characteristics of motion action.First, to model the motion action time sequence semantics and time sliding window classification at different scales, initial detection results are taken. Defined all detection results of a motor action asX, segci,wk indicated a category of ci in X, and a set of tests with a sliding window size of wk can be represented as(19)X=segci,wk,(20)segci,wk=sn,en,gnn=1Ni,k.In the formula, C represents the total number of categorical categories; K represents the total number of sliding windows;Ni,k is the number of action time segments detected in ci and wk; sI,eI are the start and end times of detected action segments; and gn represents the classification score.Then to calculate the classification score, the temporal overlap values of different time periods, as in equations (21) and (22), and compare them with the set threshold.(21)gI−gs<θ,(22)IOUpl,ps≥U×minTl,Tsl,s∈1,2,…Ni,k,⋮l≠s.In the formula,P=segci,wk represents the detection results of the same category ci and the same scale wk, pl = (sl, el, gl), and ps = (ss, es, gs) both are one detection fragment of P; gl−gs, IOUpl,ps represent the score difference and time overlap value, respectively; Tl=el−sl,Ts=es−ss the execution time of two actions, and θ, U the set threshold.The two action segments were integrated if the two action segment classification scores were less than the set threshold and the time overlap was greater than the threshold.Considering that the motion action obtained by the above operation is synthesized from multiple incomplete fragments, which partially destroys the spatiotemporal structure of the action, it also needs to conduct classification detection. This study uses a 3D convolutional neural network with good classification performance for reclassification. Furthermore, to ensure more accurate classification results and reduce the classification impact of sliding windows on motor movements, it statistically calculated the weight scores of different sliding windows for different categories, further adjusted the classification confidence scores, and trained classification models by softmax classifier and overlap loss function.Finally, to reduce redundancy detection in the presence of sliding windows at different scales, they were processed by nonmaximum inhibition to bring the final results close to the start and end of the motor action.


## 4. Simulation Experiment

### 4.1. Data Source and Preprocessing

The project dataset and the Thoumos14 publicdata set were used for this experiment. The project dataset contains 72 video action segments of six action categories, including brushing, mouthwash, and cleaning, with the characteristics of complex background, variable perspective, and an obvious difference in action execution speed. Its specific description is shown in Table2. The Thoumos14 public dataset includes 2,755 clips in 20 sports action categories, with a total of 212 test videos annotated with timing. Because there were two mislabels of “270” and “1496” in this dataset, the remaining 210 annotated timing videos were selected for this experiment.Table 2
Project dataset description.
DifficultiesDescriptionBackground noiseDifferent individual clothing changes, relationships with the environment, etcAction nonstandardizedThere are interruptions in the process of action execution, disorderly order, property management action, etcSpeed difference of action executionDifferences in speed and performance time during action execution occur due to the behavioral habits of different individualsDifferent perspectivesCamera location, different angles, etcShelterCameras and their own occlusion problemsLens movementThere are varying degrees of lens movement during the dataset recording process


### 4.2. Parameter Settings

#### 4.2.1. BN-Inception Network Parameter Settings

The BN-inception network parameters of this experiment are set as in Table3.Table 3
BN-inception network parameter settings.
typeDepth#1 × 1#1 × 1 reduce#3 × 3#3 × 3 reduceDouble #3 × 3Pool + projConvolution1Max pool0Convolution164192Max pool0Inception (3a)36464646496Avg + 32Inception (3b)36464966496Avg + 64Inception (3c)301281606496Max + passInception (4a)3224649696128Avg + 128Inception (4b)31929612896128Avg + 128Inception (4c)3160128160128160Avg + 128Inception (4d)396128192160192Avg + 128Inception (4e)30128192192256Max + passInception (5a)3352192320160224Avg + 128Inception (5b)3352192320192224Max + 128Avg pool0


#### 4.2.2. 3D ConvNet Network Parameter Settings

In this experiment, the 3D ConvNet network parameters were set as follows: the convolutional kernel size was 3 × 3 × 3, the step size was 1 × 1 × 1, the size of the first pooling layer was 1 × 2 × 2, and the size of the remaining pooling layers to 2 × 2 × 2 with maximum pooling.


#### 4.2.3. Softmax Classifier Parameter Selection

The M-BGD optimized softmax classifier parameters were used for this experiment. First, the batch-size size of the softmax classifier was selected. The average accuracy change curve on the project dataset test set under the same number of iterations when different batch-size values are taken in Figure7. It is known from Figure 7(a) that when batch-size = 64, the test had the highest average accuracy, hence batch-size = 64 is set. Second, the number of iterations is selected. The effect of the different number of iterations on the identification results during training is shown in Figure 7(b). As Figure 7(b) shows that the highest identification result was achieved when the number of iterations was 20000, so the number of iterations was set at 20000.Figure 7
Selection of training parameters. (a) Effect on the identification results. (b) Effect of the number of training iterations on the identification results.
(a)(b)During the training session, the loss change curves are shown in Figure8. This figure shows that the loss values gradually decrease and tend to 0 during training.Figure 8
Loss curves of different iteration times. (a) The loss change with 0–2,000 iterations. (b) The loss change with 50000–11,000 iterations.
(a)(b)


#### 4.2.4. Thresholding Selection Based on Temporal Semantic Continuity

The choice of score difference optimized thresholdθ has a certain impact on the integration speed of the detection window. If θ value is too large, it will easily lead to excessive integration of the detection window; if θ value is too small, it will lead to the integration time fragments cannot being merged, and there are still too many incomplete time fragments. Therefore, this experiment determined reasonable values by analyzing the influence of different θ values on mAP. Figure 9 shows the mAP taking different values at different temporal overlap thresholds on the Thoumos14 public dataset. And the highest mAP value is when θ = 0.5e−3. Therefore, this experiment was set θ = 0.5−3.Figure 9
Different thresholdsθ impact on mAP. (a) θ changing curves with mAP when A is 0.1,0.2. (b) θ changing curves with mAP when A is 0.3,0.4.
(a)(b)Considering the time continuity of motion movements and the variability of sliding windows at different scales, the time overlap threshold was set forU = 2/3.


### 4.3. Results and Analyses

#### 4.3.1. Softmax Classified Network Performance Analysis

To validate the performance of the proposed softmax classification network, this study was validated on the project dataset and compared with the SVM classification network, and the results are presented in Table4. According to the table, compared with the SVM classifier, the softmax classifier has a better effect, achieves a classification identification accuracy of 78.52%, and improves by 12.22%. Moreover, with the same classification recognition accuracy, the proposed softmax network in this study has a shorter training time and looks about 10 times shorter than the SVM classifier. This shows that the softmax classification network proposed in this study performs better and is more conducive to motion action analysis.Table 4
Comparison of classification and recognition results of different classifiers.
Features test videosSoftmaxSVMPl76.8458.73P280.3359.51P351.2572.07P486.3984.24P576.2933.33P6943 694.21P793.3461.82P871.6972.82P982.8058.42P1071.8867.91AVG78.5266.30


#### 4.3.2. Fusion Result Analysis Based on the Decision Layer

To verify the effectiveness of the decision-layer-based fusion method proposed in this study, the study was validated on the project dataset and compared with the prefusion classification identification results, results are shown in Table5. According to the table, the average classification recognition accuracy reached 79.89%, an improvement of 1.38% compared with before the fusion method, indicating that the fusion method has some effectiveness.Table 5
Fusion experimental results.
classifier Features test videoSoftmax classifierClass score confusionBN-inception features (%)C3D features (%)Pl76.8458.7375.91P280.3359.5181.63P351.2572.063.26P486.3984.2486.13P576.2933.3374.29P694.3694.2194.43P793.3461.8294.52P871.6972.8276.17P982.8058.4284.41P1071.8867.9168.23AVG78.5166.3079.89


#### 4.3.3. Validation Based on the Temporal Semantic Continuity Optimization Method

To further verify the effectiveness of this temporal semantic-based continuity optimization method, the study was validated on the project dataset and compared the identification results of partial test samples before and after optimization, as shown in Table6 and Table 7. According to the table, this proposed method in this study can effectively improve the recognition accuracy from 79.89% to 83.11%.Table 6
Comparison of results before and after optimization.
Methods test videosClass score confusion (%)Action continuity and temporal integrated (%)Pl75.9181.18P281.638139P363.2676.97P486.1383.81P574.2 V72.02P694.4394.62P794.5295.24P876.1780.46P984.4189.78P1068.2375.61AVG79.8983.11Table 7
Comparison of map values at different time overlapping thresholds.
ɑ0.50.60.70.70.80.9mAP (%)60.245.135.335.327.114.3In the test dataset, the mAP values at different time overlap thresholds are shown in Table6. According to the table, when α = 0.5, the average detection accuracy of the present study is 60.2%.


#### 4.3.4. Classification Identification Results Analysis

To verify the effectiveness of the proposed method, this study visualized the method classification identification results in Figure10. Figure 10(a) is the input video stream identification result, where the abscissa is the video frame, and the ordinate is the identification accuracy with the highest classification score for each frame. Figure 10(b) is the action performed at different time periods in the video stream, the abscissa represents the video stream, and the ordinate is the action category. Since the project dataset used for the experiment includes six action categories, each color in the figure corresponds to one action category, so there are six colors in Figure 10. According to the figure, the proposed method has the highest accuracy of the actions in the process. The recognition accuracy of different actions decreased due to the vague beginning and end of the operation type and time location. In Figure 10(b), green presents for the third action category. The proposed method presents the detection results (545,687), (697,672), and (3440,3468) frames, consistent with actual conditions.Figure 10
Visualization of classification recognition results. (a) Optimal visualization results of single frames in a video stream. (b) Classification results in the action timing flow.
(a)(b)To further analyze the classification and recognition effects of the similarity action, a study performed taxonomic identification of a test sample, and the results were shown in Figure11. In the figure, abscissa represents video streams, the ordinate represents action categories, blue represents true values, red represents predictive values, and green boxes represent misclassification caused by similarity actions. According to the figure, considering the action time timing structure is conducive to the analysis of motion movements.Figure 11
Visualization of classification recognition results.


## 4.1. Data Source and Preprocessing

The project dataset and the Thoumos14 publicdata set were used for this experiment. The project dataset contains 72 video action segments of six action categories, including brushing, mouthwash, and cleaning, with the characteristics of complex background, variable perspective, and an obvious difference in action execution speed. Its specific description is shown in Table2. The Thoumos14 public dataset includes 2,755 clips in 20 sports action categories, with a total of 212 test videos annotated with timing. Because there were two mislabels of “270” and “1496” in this dataset, the remaining 210 annotated timing videos were selected for this experiment.Table 2
Project dataset description.
DifficultiesDescriptionBackground noiseDifferent individual clothing changes, relationships with the environment, etcAction nonstandardizedThere are interruptions in the process of action execution, disorderly order, property management action, etcSpeed difference of action executionDifferences in speed and performance time during action execution occur due to the behavioral habits of different individualsDifferent perspectivesCamera location, different angles, etcShelterCameras and their own occlusion problemsLens movementThere are varying degrees of lens movement during the dataset recording process


## 4.2. Parameter Settings

### 4.2.1. BN-Inception Network Parameter Settings

The BN-inception network parameters of this experiment are set as in Table3.Table 3
BN-inception network parameter settings.
typeDepth#1 × 1#1 × 1 reduce#3 × 3#3 × 3 reduceDouble #3 × 3Pool + projConvolution1Max pool0Convolution164192Max pool0Inception (3a)36464646496Avg + 32Inception (3b)36464966496Avg + 64Inception (3c)301281606496Max + passInception (4a)3224649696128Avg + 128Inception (4b)31929612896128Avg + 128Inception (4c)3160128160128160Avg + 128Inception (4d)396128192160192Avg + 128Inception (4e)30128192192256Max + passInception (5a)3352192320160224Avg + 128Inception (5b)3352192320192224Max + 128Avg pool0


### 4.2.2. 3D ConvNet Network Parameter Settings

In this experiment, the 3D ConvNet network parameters were set as follows: the convolutional kernel size was 3 × 3 × 3, the step size was 1 × 1 × 1, the size of the first pooling layer was 1 × 2 × 2, and the size of the remaining pooling layers to 2 × 2 × 2 with maximum pooling.


### 4.2.3. Softmax Classifier Parameter Selection

The M-BGD optimized softmax classifier parameters were used for this experiment. First, the batch-size size of the softmax classifier was selected. The average accuracy change curve on the project dataset test set under the same number of iterations when different batch-size values are taken in Figure7. It is known from Figure 7(a) that when batch-size = 64, the test had the highest average accuracy, hence batch-size = 64 is set. Second, the number of iterations is selected. The effect of the different number of iterations on the identification results during training is shown in Figure 7(b). As Figure 7(b) shows that the highest identification result was achieved when the number of iterations was 20000, so the number of iterations was set at 20000.Figure 7
Selection of training parameters. (a) Effect on the identification results. (b) Effect of the number of training iterations on the identification results.
(a)(b)During the training session, the loss change curves are shown in Figure8. This figure shows that the loss values gradually decrease and tend to 0 during training.Figure 8
Loss curves of different iteration times. (a) The loss change with 0–2,000 iterations. (b) The loss change with 50000–11,000 iterations.
(a)(b)


### 4.2.4. Thresholding Selection Based on Temporal Semantic Continuity

The choice of score difference optimized thresholdθ has a certain impact on the integration speed of the detection window. If θ value is too large, it will easily lead to excessive integration of the detection window; if θ value is too small, it will lead to the integration time fragments cannot being merged, and there are still too many incomplete time fragments. Therefore, this experiment determined reasonable values by analyzing the influence of different θ values on mAP. Figure 9 shows the mAP taking different values at different temporal overlap thresholds on the Thoumos14 public dataset. And the highest mAP value is when θ = 0.5e−3. Therefore, this experiment was set θ = 0.5−3.Figure 9
Different thresholdsθ impact on mAP. (a) θ changing curves with mAP when A is 0.1,0.2. (b) θ changing curves with mAP when A is 0.3,0.4.
(a)(b)Considering the time continuity of motion movements and the variability of sliding windows at different scales, the time overlap threshold was set forU = 2/3.


## 4.2.1. BN-Inception Network Parameter Settings

The BN-inception network parameters of this experiment are set as in Table3.Table 3
BN-inception network parameter settings.
typeDepth#1 × 1#1 × 1 reduce#3 × 3#3 × 3 reduceDouble #3 × 3Pool + projConvolution1Max pool0Convolution164192Max pool0Inception (3a)36464646496Avg + 32Inception (3b)36464966496Avg + 64Inception (3c)301281606496Max + passInception (4a)3224649696128Avg + 128Inception (4b)31929612896128Avg + 128Inception (4c)3160128160128160Avg + 128Inception (4d)396128192160192Avg + 128Inception (4e)30128192192256Max + passInception (5a)3352192320160224Avg + 128Inception (5b)3352192320192224Max + 128Avg pool0


## 4.2.2. 3D ConvNet Network Parameter Settings

In this experiment, the 3D ConvNet network parameters were set as follows: the convolutional kernel size was 3 × 3 × 3, the step size was 1 × 1 × 1, the size of the first pooling layer was 1 × 2 × 2, and the size of the remaining pooling layers to 2 × 2 × 2 with maximum pooling.


## 4.2.3. Softmax Classifier Parameter Selection

The M-BGD optimized softmax classifier parameters were used for this experiment. First, the batch-size size of the softmax classifier was selected. The average accuracy change curve on the project dataset test set under the same number of iterations when different batch-size values are taken in Figure7. It is known from Figure 7(a) that when batch-size = 64, the test had the highest average accuracy, hence batch-size = 64 is set. Second, the number of iterations is selected. The effect of the different number of iterations on the identification results during training is shown in Figure 7(b). As Figure 7(b) shows that the highest identification result was achieved when the number of iterations was 20000, so the number of iterations was set at 20000.Figure 7
Selection of training parameters. (a) Effect on the identification results. (b) Effect of the number of training iterations on the identification results.
(a)(b)During the training session, the loss change curves are shown in Figure8. This figure shows that the loss values gradually decrease and tend to 0 during training.Figure 8
Loss curves of different iteration times. (a) The loss change with 0–2,000 iterations. (b) The loss change with 50000–11,000 iterations.
(a)(b)


## 4.2.4. Thresholding Selection Based on Temporal Semantic Continuity

The choice of score difference optimized thresholdθ has a certain impact on the integration speed of the detection window. If θ value is too large, it will easily lead to excessive integration of the detection window; if θ value is too small, it will lead to the integration time fragments cannot being merged, and there are still too many incomplete time fragments. Therefore, this experiment determined reasonable values by analyzing the influence of different θ values on mAP. Figure 9 shows the mAP taking different values at different temporal overlap thresholds on the Thoumos14 public dataset. And the highest mAP value is when θ = 0.5e−3. Therefore, this experiment was set θ = 0.5−3.Figure 9
Different thresholdsθ impact on mAP. (a) θ changing curves with mAP when A is 0.1,0.2. (b) θ changing curves with mAP when A is 0.3,0.4.
(a)(b)Considering the time continuity of motion movements and the variability of sliding windows at different scales, the time overlap threshold was set forU = 2/3.


## 4.3. Results and Analyses

### 4.3.1. Softmax Classified Network Performance Analysis

To validate the performance of the proposed softmax classification network, this study was validated on the project dataset and compared with the SVM classification network, and the results are presented in Table4. According to the table, compared with the SVM classifier, the softmax classifier has a better effect, achieves a classification identification accuracy of 78.52%, and improves by 12.22%. Moreover, with the same classification recognition accuracy, the proposed softmax network in this study has a shorter training time and looks about 10 times shorter than the SVM classifier. This shows that the softmax classification network proposed in this study performs better and is more conducive to motion action analysis.Table 4
Comparison of classification and recognition results of different classifiers.
Features test videosSoftmaxSVMPl76.8458.73P280.3359.51P351.2572.07P486.3984.24P576.2933.33P6943 694.21P793.3461.82P871.6972.82P982.8058.42P1071.8867.91AVG78.5266.30


### 4.3.2. Fusion Result Analysis Based on the Decision Layer

To verify the effectiveness of the decision-layer-based fusion method proposed in this study, the study was validated on the project dataset and compared with the prefusion classification identification results, results are shown in Table5. According to the table, the average classification recognition accuracy reached 79.89%, an improvement of 1.38% compared with before the fusion method, indicating that the fusion method has some effectiveness.Table 5
Fusion experimental results.
classifier Features test videoSoftmax classifierClass score confusionBN-inception features (%)C3D features (%)Pl76.8458.7375.91P280.3359.5181.63P351.2572.063.26P486.3984.2486.13P576.2933.3374.29P694.3694.2194.43P793.3461.8294.52P871.6972.8276.17P982.8058.4284.41P1071.8867.9168.23AVG78.5166.3079.89


### 4.3.3. Validation Based on the Temporal Semantic Continuity Optimization Method

To further verify the effectiveness of this temporal semantic-based continuity optimization method, the study was validated on the project dataset and compared the identification results of partial test samples before and after optimization, as shown in Table6 and Table 7. According to the table, this proposed method in this study can effectively improve the recognition accuracy from 79.89% to 83.11%.Table 6
Comparison of results before and after optimization.
Methods test videosClass score confusion (%)Action continuity and temporal integrated (%)Pl75.9181.18P281.638139P363.2676.97P486.1383.81P574.2 V72.02P694.4394.62P794.5295.24P876.1780.46P984.4189.78P1068.2375.61AVG79.8983.11Table 7
Comparison of map values at different time overlapping thresholds.
ɑ0.50.60.70.70.80.9mAP (%)60.245.135.335.327.114.3In the test dataset, the mAP values at different time overlap thresholds are shown in Table6. According to the table, when α = 0.5, the average detection accuracy of the present study is 60.2%.


### 4.3.4. Classification Identification Results Analysis

To verify the effectiveness of the proposed method, this study visualized the method classification identification results in Figure10. Figure 10(a) is the input video stream identification result, where the abscissa is the video frame, and the ordinate is the identification accuracy with the highest classification score for each frame. Figure 10(b) is the action performed at different time periods in the video stream, the abscissa represents the video stream, and the ordinate is the action category. Since the project dataset used for the experiment includes six action categories, each color in the figure corresponds to one action category, so there are six colors in Figure 10. According to the figure, the proposed method has the highest accuracy of the actions in the process. The recognition accuracy of different actions decreased due to the vague beginning and end of the operation type and time location. In Figure 10(b), green presents for the third action category. The proposed method presents the detection results (545,687), (697,672), and (3440,3468) frames, consistent with actual conditions.Figure 10
Visualization of classification recognition results. (a) Optimal visualization results of single frames in a video stream. (b) Classification results in the action timing flow.
(a)(b)To further analyze the classification and recognition effects of the similarity action, a study performed taxonomic identification of a test sample, and the results were shown in Figure11. In the figure, abscissa represents video streams, the ordinate represents action categories, blue represents true values, red represents predictive values, and green boxes represent misclassification caused by similarity actions. According to the figure, considering the action time timing structure is conducive to the analysis of motion movements.Figure 11
Visualization of classification recognition results.


## 4.3.1. Softmax Classified Network Performance Analysis

To validate the performance of the proposed softmax classification network, this study was validated on the project dataset and compared with the SVM classification network, and the results are presented in Table4. According to the table, compared with the SVM classifier, the softmax classifier has a better effect, achieves a classification identification accuracy of 78.52%, and improves by 12.22%. Moreover, with the same classification recognition accuracy, the proposed softmax network in this study has a shorter training time and looks about 10 times shorter than the SVM classifier. This shows that the softmax classification network proposed in this study performs better and is more conducive to motion action analysis.Table 4
Comparison of classification and recognition results of different classifiers.
Features test videosSoftmaxSVMPl76.8458.73P280.3359.51P351.2572.07P486.3984.24P576.2933.33P6943 694.21P793.3461.82P871.6972.82P982.8058.42P1071.8867.91AVG78.5266.30


## 4.3.2. Fusion Result Analysis Based on the Decision Layer

To verify the effectiveness of the decision-layer-based fusion method proposed in this study, the study was validated on the project dataset and compared with the prefusion classification identification results, results are shown in Table5. According to the table, the average classification recognition accuracy reached 79.89%, an improvement of 1.38% compared with before the fusion method, indicating that the fusion method has some effectiveness.Table 5
Fusion experimental results.
classifier Features test videoSoftmax classifierClass score confusionBN-inception features (%)C3D features (%)Pl76.8458.7375.91P280.3359.5181.63P351.2572.063.26P486.3984.2486.13P576.2933.3374.29P694.3694.2194.43P793.3461.8294.52P871.6972.8276.17P982.8058.4284.41P1071.8867.9168.23AVG78.5166.3079.89


## 4.3.3. Validation Based on the Temporal Semantic Continuity Optimization Method

To further verify the effectiveness of this temporal semantic-based continuity optimization method, the study was validated on the project dataset and compared the identification results of partial test samples before and after optimization, as shown in Table6 and Table 7. According to the table, this proposed method in this study can effectively improve the recognition accuracy from 79.89% to 83.11%.Table 6
Comparison of results before and after optimization.
Methods test videosClass score confusion (%)Action continuity and temporal integrated (%)Pl75.9181.18P281.638139P363.2676.97P486.1383.81P574.2 V72.02P694.4394.62P794.5295.24P876.1780.46P984.4189.78P1068.2375.61AVG79.8983.11Table 7
Comparison of map values at different time overlapping thresholds.
ɑ0.50.60.70.70.80.9mAP (%)60.245.135.335.327.114.3In the test dataset, the mAP values at different time overlap thresholds are shown in Table6. According to the table, when α = 0.5, the average detection accuracy of the present study is 60.2%.


## 4.3.4. Classification Identification Results Analysis

To verify the effectiveness of the proposed method, this study visualized the method classification identification results in Figure10. Figure 10(a) is the input video stream identification result, where the abscissa is the video frame, and the ordinate is the identification accuracy with the highest classification score for each frame. Figure 10(b) is the action performed at different time periods in the video stream, the abscissa represents the video stream, and the ordinate is the action category. Since the project dataset used for the experiment includes six action categories, each color in the figure corresponds to one action category, so there are six colors in Figure 10. According to the figure, the proposed method has the highest accuracy of the actions in the process. The recognition accuracy of different actions decreased due to the vague beginning and end of the operation type and time location. In Figure 10(b), green presents for the third action category. The proposed method presents the detection results (545,687), (697,672), and (3440,3468) frames, consistent with actual conditions.Figure 10
Visualization of classification recognition results. (a) Optimal visualization results of single frames in a video stream. (b) Classification results in the action timing flow.
(a)(b)To further analyze the classification and recognition effects of the similarity action, a study performed taxonomic identification of a test sample, and the results were shown in Figure11. In the figure, abscissa represents video streams, the ordinate represents action categories, blue represents true values, red represents predictive values, and green boxes represent misclassification caused by similarity actions. According to the figure, considering the action time timing structure is conducive to the analysis of motion movements.Figure 11
Visualization of classification recognition results.


## 5. Conclusion

In summary, using the deep-learning-based motor action analysis method proposed in this study, static spatial features of the motion action are extracted by using BN-inception and high-dimensional spatiotemporal features of motor movements are extracted by 3D ConvNet. A characteristic representation containing the spatiotemporal information of the motor movements is obtained. By using the softmax classifier and integrating the extracted features with the fusion based on decision layers, the accuracy of motion action classification and recognition was improved, so that the average motion action classification and recognition accuracy reached 79.89%. Through the time-based semantic continuity optimization strategy, the recognition accuracy of motion movements was further improved, and the average motion action classification recognition accuracy reached to 83.11%, realizing the efficient recognition of motion actions. However, there are still some deficiencies in this study, mainly manifested in feature extraction. The BN-inception network and 3D ConvNet network used in the study trained the model in advance for the public dataset and did not fine-tune the model structure according to the research content, so its robustness needs to be further improved.



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*Source: 9436736-2022-03-10.xml*