# Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Authors: Yi Yuan; DongXia Zheng
Journal: Mobile Information Systems
 (2022)

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## Abstract

With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%.


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## Body

## 1. Introduction

The recognition of human motion pose has become a concern and is widely used in computer vision [1]. Video-based pose recognition usually means inputting video data and extracting and analyzing video features through various image processing and recognition methods [2]. In order to achieve the purpose of human action recognition in video, it has a wide range of applications [3. The key of video-based posture state recognition is to extract appropriate video features and analyze and recognize these features reasonably and accurately [4]. Physical motion recognition technology is widely used in various fields. Applying natural person and natural person recognition technology to the purpose of sports recognition can accurately identify sports, compare them with existing sports, and identify and correct irregular sports [5].Artificial intelligence technology began to emerge in the 1990s, and machine vision technology after 20 years of development has been widely used in video surveillance, virtual imaging, film and television production, and other industries [6]. In particular, the technology of character modeling to generate two-dimensional animation is one of the current hot spots of scientific and technological research [7, 8]. With the application of machine learning in the field of image processing gradually mature, the combination of deep learning and computer 2D animation imaging technology has become possible, [9] proposed the use of Toronto University’s general model - wireframe model modeling, modeling method is more efficient, simple image extraction, but the data noise is too large, affecting the accuracy of the action is affected by the excessive data noise [10]. Other domestic research units, still in the academic exploration and research stage, the proposed algorithm in the application of the hardware often has high requirements for computing power [11]. From the domestic and international research, the key to the recognition of character pose and 2D animation generation lies in the pose extraction of each action of the character itself, image compression and subsequent refinement of the convolutional neural networks in the processing of medical images show a strong advantage [12. In this paper, we focus on the effective combination of deep neural networks and pose recognition and propose an improved convolutional neural network architecture to achieve real-time character pose output in complex scenes of multiplayer motion.


## 2. Related Work

The detection and recognition technology of human motion gesture can be applied not only in smart home but also in military field, which will greatly promote the development of intelligent weapons, so it has important application prospects. At present, the main method of subject space recognition is the recognition of visible light based on vision and microwave. Radar microwave-based human motion gesture recognition is not affected by light, can protect user privacy, and can penetrate certain obstacles for recognition [13]. Therefore, radar microwave-based human motion gesture recognition technology has an irreplaceable position in the fields of smart home, remote control, and intelligent weapons.The key to radar microwave-based human motion pose recognition is to extract and identify the micro-Doppler features of the echoes. In the literature, micro-Doppler features are extracted from human action postures for recognition and classification by traditional algorithms such as support vector machine (SVM), orthogonal matching tracking (OMP), and dynamic time regularization (DTW). Although the above traditional algorithms can achieve high accuracy, they are limited to traditional supervised learning, which requires human extraction of features from micro-Doppler information, and the extracted features are difficult to migrate for application due to the limitation of the recognition object, while deep learning algorithms can overcome this limitation. In the literature [14, 15], deep learning algorithms such as CNNs and dual-stream fusion neural networks (TS-FNNs) were used to extract and recognize features from R-D (range-Doppler) maps of gestures generated by FM continuous wave radar, and the accuracy rate was significantly improved compared with traditional algorithms. This shows that the deep learning algorithm can bring a great improvement to the accuracy of radar gesture recognition. However, deep learning algorithms require a large amount of data and are prone to overfitting and error transfer for small datasets, resulting in poor recognition results [16].This paper proposes a CNN-based microwave recognition method for human action posture. CNN can automatically extract the depth features of action echoes without human extraction, and the model has strong generalization ability [17]. Compared with the traditional BP (backpropagation) neural network, CNN uses convolutional kernels for local connectivity and weight sharing, which reduces the number of parameters and improves the learning efficiency of the network and can better solve the overfitting and error transmission problems caused by small datasets [18]. In this paper, LFMCW radar is used to acquire the human action posture echo signals, generate the time-frequency maps of human action postures, and recognize the radar echo images of four types of human action postures: walking, sitting, standing, and falling, by CNN [19, 20]. The final recognition accuracy for walking, sitting, standing, and falling movements reaches over 97%.


## 3. Methodology

The algorithm is based on a bottom-up human pose recognition algorithm, which is the first to identify the key points of human movement in a complex environment with multiple people and then form a skeletal map of human movement after a reasonable linkage of key points. When using convolutional neural network to process the basic image, only one convolution is needed to complete the analysis. Firstly, according to the coordinates of human joints, joint levels, and types, the feature map of human directed links is established, which facilitates the digital processing of images and then completes the convolution operation, as shown in Figure1.Figure 1
Diagram of human directed link.In the human feature map represented in Figure1, the coordinates, levels, and types of key joint points are identified in the form of feature vectors, and for the feature point x,y, the corresponding feature vector is(1)Pc,Tto,where Pc represents the probability value of the type to which the feature point x,y and its corresponding joints belong and Tto represents the offset value of the coordinates of the parent node of the feature point from the coordinates of the feature point itself, which is the value of the feature vector.


### 3.1. Acquisition of Differential Beat Signal

Figure2 shows the time-frequency relationship between the LFMCW radar transmit signal, the echo signal, and the differential beat signal.Figure 2
Time-frequency relationship of different signals.In Figure2, f0 is the starting frequency of the signal, τmax is the maximum time delay, Tr is the period of the signal, B is the bandwidth of the signal, the effective time of the signal is T, i.e., T=Tr−τmax, and the effective bandwidth of the signal is usually smaller than B.Considering the multiperiod LFMCW radar echo signal, to simplify the analysis, ignoring the initial phase, the sawtooth LFMCW radar signal in the. The complex form of the emitted signal in thek+1 sweep period is(2)st,k+1t=A0expj2πf0t−kTr+μt−kTr22,t∈k−12Tr,k+12Tr,where A0 is the random amplitude of the transmit signal at t=kTr, f0 is the instantaneous frequency of the transmit signal at t=kTr, and μ=Br/Tr is the FM slope (Br is the FM bandwidth and Tr is the sweep period). At time t  =  0, assuming that a point target has an initial distance of R0 with respect to the radar and approaches the radar with radial velocity v (with the velocity away from the radar as positive and the velocity close to the radar as negative), the echo signal sr,k+1t of the moving target in the effective time period of the k+1 sweep period is expressed as(3)sr,k+1t=βrA0expj2πf0t−kTr−τkt+μt−kTr−τkt22,t∈k−12Tr+τmax,k+12Tr,where βr is the attenuation constant, which reflects the influence of the environment on the electromagnetic wave and the ability of the target to scatter the electromagnetic wave; τkt is the instantaneous delay of the target echo in the k+1 period; and τkt=2R0−vt/c, in which c is the speed of light. By mixing the transmitting signal st,k+1t and the target echo signal sr,k+1t in the effective band in the k+1 period, the resulting differential beat signal sb,k+1t can be expressed as(4)sb,k+1t=βrA022expj2π−μτk2t2+f0τkt+μτktt−kTr,t∈k−12Tr+τmax,k+12Tr.Lett′=t−kTr; then, substitute t for t′, bring τkt+kTr=2R0−vt−vkTr/c into (4), and ignore c2,t2, and we get(5)sb,k+1t≈βrA022expj2π2μR0−2vf0−2μvkTrtc+2f0R0−2vf0kTrv,t∈−Tr2+τmax,Tr2.Fourier transform (FT) of (5) on the k+1 interval −T/2,T/2 yields the spectrum of the differential beat signal.(6)Sb,k+1f≈∫−T/2T/2βrA022expj2π2μR0−2vf0−2μvkTrtc+2f0R0−2vf0kTrcexp−j2πftdt≈βrA022expj2πf02R0−2vkTrc⋅TSaπTf−2μR0c+2vf0c+2μvkTrc.There is background clutter in the differential signal spectrum, which needs to be processed by MTI. The background clutter is mainly fixed target echo and slow moving clutter. In this paper, the high-pass Butterworth filter is chosen as the MTI filter to suppress the clutter.


### 3.2. STFT Transformation to Generate Echo Time-Frequency Map

Whenf=2R0−vkTrμ/c−2vf0/c, equation (6), Sb,k+1f, obtains the maximum value, i.e.,(7)Sb,k+1f=βrA02T2expj2πf02R0−2vkTrc.It can be seen that the frequency points corresponding to the peak of the single-period signal spectrum contain both distance and velocity information. It is necessary to perform time-frequency analysis on the spectral components of all repeated-period signals within the same frequency point by STFT, so as to obtain the Doppler shift information of the differential beat signal and convert it into two-dimensional information and then convert it into a time-frequency map [21–23].(8)STFTxt=∫−∞+∞xtht−uexp−j2πftdt,where xt is the spectral component of all repeated periodic signals at the same frequency point, ht is the Hanning window, and u is the window function shift distance.To facilitate computer processing, the signal is discretized, and the discrete form of (8) is(9)STFTxn=∑n=−∞∞xnhn−mkexp−j2πnF,where x(n) is the discrete spectral component of all repetitive periodic signals within the same frequency point, hn is the Hanning window, m is the single move step of the window function, k is the number of move steps, and F is the digital frequency.


### 3.3. Recognition Using CNN

The time-frequency map is used as the input data and the network parameters are trained. Due to the small dataset, a CNN with fewer layers is constructed to reduce overfitting and error transmission, as shown in Figure3 and Tables 1 and 2.Figure 3
Schematic diagram of convolutional neural network.Table 1
Convolutional and pooling layer network parameters.
Network layer numberNetwork layerConvolution kernel/pooling window matrix dimensionNumber of convolution kernelsStepNetwork layer graph dimensionC1Convolutional layer 15 × 516116 × 220 × 220P1Pooling layer 13 × 3316 × 73 × 73C2Convolutional layer 25 × 532132 × 69 × 69P2Pooling layer 22 × 2232 × 34 × 34Table 2
Full connectivity layer network parameters.
Network layer numberNetwork layerWeight matrix dimensionOutput vector dimensionD1Fully connected layer 136992 × 641 × 64D2Fully connected layer 264 × 321 × 32D3Fully connected layer 332 × 41 × 4Two convolutional layers (C1, C2) with 5 × 5 convolutional kernel size and 16 and 32 convolutional kernels respectively, both in steps of 1; two pooling layers (P1, P2) with 3 × 3 and 2 × 2 pooling window matrices respectively, in steps of 3 and 2; three fully connected layers (D1, D2 and D3) with 36 992 × 64, 64 × 32 and 32 × 4 weight matrix dimensions respectively). The activation functions of D1 and D2 are Relu1, except for the activation function of the fully connected layer D3, which is softmax.The convolutional layers (C1 and C2) use multiple convolutional kernels to extract depth features from the image. Let the original image be X=xi1j1α×α, the convolution kernel be W=wi2j2β×β, the convolution kernel dimension be d, and the convolution kernel move step be e. X,W perform the convolution operation, and the output is Y=yi3j3γ×γ, and then the activation function Relu returns the negative value, i.e.,(10)Relux=0,x<0,x,x≥0..In the pooling layers (P1 and P2), the pool window matrix is used to extract the local maximum value of reservoir output, sample the matrix of each channel, output the dimension set in the pool window matrix, and move the pool window matrix. Let a be the input vector with dimension 1 × 36992; W1,W2,W3 be the weight matrices of fully connected layers 1, 2, and 3 with dimensions 36992 ×  4, 64 × 32, and 32 × 4, respectively; h1,h2,h2′ be the output vectors of D1, D2, and BN (batch normalization) layers with dimensions 1 × 64, 1 × 32, and 1 × 32, respectively; and out be the predicted value of the network with dimension 1 × 4, respectively. b1,b2,b3 is the bias of fully connected layers D1, D2, and D3, respectively. Let the output vector h2 of D2 layer be x1,x2,…,xN; then, the BN layer can be expressed as(11)BNx1,x2,…,xN=x1−μhσ2σ1+μh′,x2−μhσ2σ1+μh′,…,xN−μhσ2σ1+μh′.Let the input vector of softmax bey1,y2,…,yM; then,(12)Softmaxy1,y2,…,yM=∑i=1Mexpyi−1expy1,expy2,…,expyM.The network model is(13)h1=ReluaW1+b1h2=Reluh1W2+b2h2′=BNh2out=Softmaxh2′W3+b3.In this paper, the network parameters are updated by the gradient descent method, and the loss function cross entropy (cross entropy) is(14)ρloss=−∑i=1MOilnO^i.The process of updating the parameters can be expressed as(15)gθ=∇ξρloss,λθ=β1λθ−1+1−β1gθ,ρθ=β2ρθ−1+1−β2gθ2,λ^θ=λθ1−β1θ,ρ^θ=ρθ1−β2θ,ξθ=ξθ−1−ηλ^θρ^θ1/2+ε.


## 3.1. Acquisition of Differential Beat Signal

Figure2 shows the time-frequency relationship between the LFMCW radar transmit signal, the echo signal, and the differential beat signal.Figure 2
Time-frequency relationship of different signals.In Figure2, f0 is the starting frequency of the signal, τmax is the maximum time delay, Tr is the period of the signal, B is the bandwidth of the signal, the effective time of the signal is T, i.e., T=Tr−τmax, and the effective bandwidth of the signal is usually smaller than B.Considering the multiperiod LFMCW radar echo signal, to simplify the analysis, ignoring the initial phase, the sawtooth LFMCW radar signal in the. The complex form of the emitted signal in thek+1 sweep period is(2)st,k+1t=A0expj2πf0t−kTr+μt−kTr22,t∈k−12Tr,k+12Tr,where A0 is the random amplitude of the transmit signal at t=kTr, f0 is the instantaneous frequency of the transmit signal at t=kTr, and μ=Br/Tr is the FM slope (Br is the FM bandwidth and Tr is the sweep period). At time t  =  0, assuming that a point target has an initial distance of R0 with respect to the radar and approaches the radar with radial velocity v (with the velocity away from the radar as positive and the velocity close to the radar as negative), the echo signal sr,k+1t of the moving target in the effective time period of the k+1 sweep period is expressed as(3)sr,k+1t=βrA0expj2πf0t−kTr−τkt+μt−kTr−τkt22,t∈k−12Tr+τmax,k+12Tr,where βr is the attenuation constant, which reflects the influence of the environment on the electromagnetic wave and the ability of the target to scatter the electromagnetic wave; τkt is the instantaneous delay of the target echo in the k+1 period; and τkt=2R0−vt/c, in which c is the speed of light. By mixing the transmitting signal st,k+1t and the target echo signal sr,k+1t in the effective band in the k+1 period, the resulting differential beat signal sb,k+1t can be expressed as(4)sb,k+1t=βrA022expj2π−μτk2t2+f0τkt+μτktt−kTr,t∈k−12Tr+τmax,k+12Tr.Lett′=t−kTr; then, substitute t for t′, bring τkt+kTr=2R0−vt−vkTr/c into (4), and ignore c2,t2, and we get(5)sb,k+1t≈βrA022expj2π2μR0−2vf0−2μvkTrtc+2f0R0−2vf0kTrv,t∈−Tr2+τmax,Tr2.Fourier transform (FT) of (5) on the k+1 interval −T/2,T/2 yields the spectrum of the differential beat signal.(6)Sb,k+1f≈∫−T/2T/2βrA022expj2π2μR0−2vf0−2μvkTrtc+2f0R0−2vf0kTrcexp−j2πftdt≈βrA022expj2πf02R0−2vkTrc⋅TSaπTf−2μR0c+2vf0c+2μvkTrc.There is background clutter in the differential signal spectrum, which needs to be processed by MTI. The background clutter is mainly fixed target echo and slow moving clutter. In this paper, the high-pass Butterworth filter is chosen as the MTI filter to suppress the clutter.


## 3.2. STFT Transformation to Generate Echo Time-Frequency Map

Whenf=2R0−vkTrμ/c−2vf0/c, equation (6), Sb,k+1f, obtains the maximum value, i.e.,(7)Sb,k+1f=βrA02T2expj2πf02R0−2vkTrc.It can be seen that the frequency points corresponding to the peak of the single-period signal spectrum contain both distance and velocity information. It is necessary to perform time-frequency analysis on the spectral components of all repeated-period signals within the same frequency point by STFT, so as to obtain the Doppler shift information of the differential beat signal and convert it into two-dimensional information and then convert it into a time-frequency map [21–23].(8)STFTxt=∫−∞+∞xtht−uexp−j2πftdt,where xt is the spectral component of all repeated periodic signals at the same frequency point, ht is the Hanning window, and u is the window function shift distance.To facilitate computer processing, the signal is discretized, and the discrete form of (8) is(9)STFTxn=∑n=−∞∞xnhn−mkexp−j2πnF,where x(n) is the discrete spectral component of all repetitive periodic signals within the same frequency point, hn is the Hanning window, m is the single move step of the window function, k is the number of move steps, and F is the digital frequency.


## 3.3. Recognition Using CNN

The time-frequency map is used as the input data and the network parameters are trained. Due to the small dataset, a CNN with fewer layers is constructed to reduce overfitting and error transmission, as shown in Figure3 and Tables 1 and 2.Figure 3
Schematic diagram of convolutional neural network.Table 1
Convolutional and pooling layer network parameters.
Network layer numberNetwork layerConvolution kernel/pooling window matrix dimensionNumber of convolution kernelsStepNetwork layer graph dimensionC1Convolutional layer 15 × 516116 × 220 × 220P1Pooling layer 13 × 3316 × 73 × 73C2Convolutional layer 25 × 532132 × 69 × 69P2Pooling layer 22 × 2232 × 34 × 34Table 2
Full connectivity layer network parameters.
Network layer numberNetwork layerWeight matrix dimensionOutput vector dimensionD1Fully connected layer 136992 × 641 × 64D2Fully connected layer 264 × 321 × 32D3Fully connected layer 332 × 41 × 4Two convolutional layers (C1, C2) with 5 × 5 convolutional kernel size and 16 and 32 convolutional kernels respectively, both in steps of 1; two pooling layers (P1, P2) with 3 × 3 and 2 × 2 pooling window matrices respectively, in steps of 3 and 2; three fully connected layers (D1, D2 and D3) with 36 992 × 64, 64 × 32 and 32 × 4 weight matrix dimensions respectively). The activation functions of D1 and D2 are Relu1, except for the activation function of the fully connected layer D3, which is softmax.The convolutional layers (C1 and C2) use multiple convolutional kernels to extract depth features from the image. Let the original image be X=xi1j1α×α, the convolution kernel be W=wi2j2β×β, the convolution kernel dimension be d, and the convolution kernel move step be e. X,W perform the convolution operation, and the output is Y=yi3j3γ×γ, and then the activation function Relu returns the negative value, i.e.,(10)Relux=0,x<0,x,x≥0..In the pooling layers (P1 and P2), the pool window matrix is used to extract the local maximum value of reservoir output, sample the matrix of each channel, output the dimension set in the pool window matrix, and move the pool window matrix. Let a be the input vector with dimension 1 × 36992; W1,W2,W3 be the weight matrices of fully connected layers 1, 2, and 3 with dimensions 36992 ×  4, 64 × 32, and 32 × 4, respectively; h1,h2,h2′ be the output vectors of D1, D2, and BN (batch normalization) layers with dimensions 1 × 64, 1 × 32, and 1 × 32, respectively; and out be the predicted value of the network with dimension 1 × 4, respectively. b1,b2,b3 is the bias of fully connected layers D1, D2, and D3, respectively. Let the output vector h2 of D2 layer be x1,x2,…,xN; then, the BN layer can be expressed as(11)BNx1,x2,…,xN=x1−μhσ2σ1+μh′,x2−μhσ2σ1+μh′,…,xN−μhσ2σ1+μh′.Let the input vector of softmax bey1,y2,…,yM; then,(12)Softmaxy1,y2,…,yM=∑i=1Mexpyi−1expy1,expy2,…,expyM.The network model is(13)h1=ReluaW1+b1h2=Reluh1W2+b2h2′=BNh2out=Softmaxh2′W3+b3.In this paper, the network parameters are updated by the gradient descent method, and the loss function cross entropy (cross entropy) is(14)ρloss=−∑i=1MOilnO^i.The process of updating the parameters can be expressed as(15)gθ=∇ξρloss,λθ=β1λθ−1+1−β1gθ,ρθ=β2ρθ−1+1−β2gθ2,λ^θ=λθ1−β1θ,ρ^θ=ρθ1−β2θ,ξθ=ξθ−1−ηλ^θρ^θ1/2+ε.


## 4. Experiments and Analysis

The above method is experimentally validated using a publicly available dataset on the Web [24–27]. The dataset is obtained from the LFMCW radar, which detects four types of human gestures: walking, sitting, standing, and falling. The experiments were conducted in an indoor environment with 106 participants to obtain the motion data, and each motion was repeated 2-3 times. STFT used a Hanning window with a length of 0.2 s and an overlap time of 0.19 s [28, 29].


### 4.1. CNN Generalization Performance

In order to avoid the phenomenon of slow convergence due to too small learning rate and oscillation of accuracy when the parameters converge to near the optimal point due to too large learning rate, this paper adopts a segmented decay strategy of learning rate, i.e.,η  = 5 × 10−4 when iterating within 20 rounds, η  = 1 × 10−4 from 20 to 30 rounds, η  = 5 × 10−5 from 30 to 40 rounds, and η  = 1 × 10−5 above 40 rounds.The accuracy and error of the training set with the number of iteration rounds are shown in Figure4, and the accuracy of the test set with 4 classes of image classification is shown in Table 3.Figure 4
Variation of accuracy and error with the number of iteration rounds in the training set. (a) Accuracy. (b) Error.
(a)(b)Table 3
Accuracy of 4-class image classification in the test set.
Human motion image typeClassification accuracy (%)Walking92Sitting99Standing up100Falling100From Figure4, the accuracy of the training set has reached more than 90% within 5 iterations, indicating that the network parameters have converged to a smaller range, but the curve oscillation amplitude is more obvious due to the large learning rate, and when the learning rate decreases after 40 iterations, the curve oscillation amplitude decreases significantly due to the reduction of the learning rate, and the accuracy reaches more than 99% after 150 iterations, and the average error is 0.0114. Due to the slight overfitting, the accuracy of the test set is always slightly smaller than that of the training set, and after 150 iterations, the accuracy is 97.208%, with an average error of 0.1106.


### 4.2. Effect of Network Parameters on the Recognition Effect of CNN

The accuracy and error of the training set with the number of iterations are shown in Figures5(a)–5(c).Figure 5
(a) Training set accuracy and error when changing the activation function. (A) Accuracy of training set. (B) Error of training set. (b) Training set accuracy and error when changing the optimizer. (A) Accuracy of training set. (B) Error of training set. (c) Training set accuracy and error when changing the learning rate. (A) Accuracy of training set. (B) Error of training set.
(a)(b)(c)From Figure5, we can get the following. ①When the activation function is changed, after 150 iterations, the accuracy of the training set is 98.6%, and the average error is 0.1668; compared with that before the parameters are changed, the oscillation amplitude of the training set is significantly larger, the overfitting is aggravated, and the generalization ability of the model is reduced. ②When the optimizer is changed, after 150 iterations, the accuracy of the training set is 99.8%, and the average error is 0.0162; compared with that before the parameters are changed, the oscillation amplitude of the training set is basically the same, the overfitting is reduced, and the model generalization ability is basically the same. When the learning rate is changed, after 150 iterations, the accuracy of the training set is 99.6%, and the average error is 0.0219. Compared with that before the parameters are changed, the oscillation amplitude of the training set is slightly increased, the overfitting is slightly reduced, and the generalization ability of the model is improved. The test results are shown in Table 4.Table 4
Test results after changing parameters.
Change parameter typeAccuracy (%)Average errorActivation function94.5880.3576Optimizer97.8590.1101Learning rate98.5520.0697Therefore, when individual network parameters are changed, the generalization ability of the network model will be affected to some extent, but the accuracy of the test set always remains above 94% (see Table4), which indicates that the network model has certain robustness and can better extract and recognize the micro-Doppler features of some simple human action postures.


## 4.1. CNN Generalization Performance

In order to avoid the phenomenon of slow convergence due to too small learning rate and oscillation of accuracy when the parameters converge to near the optimal point due to too large learning rate, this paper adopts a segmented decay strategy of learning rate, i.e.,η  = 5 × 10−4 when iterating within 20 rounds, η  = 1 × 10−4 from 20 to 30 rounds, η  = 5 × 10−5 from 30 to 40 rounds, and η  = 1 × 10−5 above 40 rounds.The accuracy and error of the training set with the number of iteration rounds are shown in Figure4, and the accuracy of the test set with 4 classes of image classification is shown in Table 3.Figure 4
Variation of accuracy and error with the number of iteration rounds in the training set. (a) Accuracy. (b) Error.
(a)(b)Table 3
Accuracy of 4-class image classification in the test set.
Human motion image typeClassification accuracy (%)Walking92Sitting99Standing up100Falling100From Figure4, the accuracy of the training set has reached more than 90% within 5 iterations, indicating that the network parameters have converged to a smaller range, but the curve oscillation amplitude is more obvious due to the large learning rate, and when the learning rate decreases after 40 iterations, the curve oscillation amplitude decreases significantly due to the reduction of the learning rate, and the accuracy reaches more than 99% after 150 iterations, and the average error is 0.0114. Due to the slight overfitting, the accuracy of the test set is always slightly smaller than that of the training set, and after 150 iterations, the accuracy is 97.208%, with an average error of 0.1106.


## 4.2. Effect of Network Parameters on the Recognition Effect of CNN

The accuracy and error of the training set with the number of iterations are shown in Figures5(a)–5(c).Figure 5
(a) Training set accuracy and error when changing the activation function. (A) Accuracy of training set. (B) Error of training set. (b) Training set accuracy and error when changing the optimizer. (A) Accuracy of training set. (B) Error of training set. (c) Training set accuracy and error when changing the learning rate. (A) Accuracy of training set. (B) Error of training set.
(a)(b)(c)From Figure5, we can get the following. ①When the activation function is changed, after 150 iterations, the accuracy of the training set is 98.6%, and the average error is 0.1668; compared with that before the parameters are changed, the oscillation amplitude of the training set is significantly larger, the overfitting is aggravated, and the generalization ability of the model is reduced. ②When the optimizer is changed, after 150 iterations, the accuracy of the training set is 99.8%, and the average error is 0.0162; compared with that before the parameters are changed, the oscillation amplitude of the training set is basically the same, the overfitting is reduced, and the model generalization ability is basically the same. When the learning rate is changed, after 150 iterations, the accuracy of the training set is 99.6%, and the average error is 0.0219. Compared with that before the parameters are changed, the oscillation amplitude of the training set is slightly increased, the overfitting is slightly reduced, and the generalization ability of the model is improved. The test results are shown in Table 4.Table 4
Test results after changing parameters.
Change parameter typeAccuracy (%)Average errorActivation function94.5880.3576Optimizer97.8590.1101Learning rate98.5520.0697Therefore, when individual network parameters are changed, the generalization ability of the network model will be affected to some extent, but the accuracy of the test set always remains above 94% (see Table4), which indicates that the network model has certain robustness and can better extract and recognize the micro-Doppler features of some simple human action postures.


## 5. Conclusion

A CNN-based human posture action recognition method is proposed for motion action judgment. The method obtains the time-frequency map of human action gestures by two-dimensional Fourier transform and then uses CNN to extract micro-Doppler features from the radar time-frequency map for classification. Compared with the traditional BP (backpropagation) neural network, it improves the learning efficiency of the network and better solves the problems of overfitting and mistransmission caused by small datasets. The robustness and superiority of the method are evaluated from various aspects, and the experiments are perfect and effective. Specifically, high recognition accuracy was achieved in the classification of four human action poses, namely, walking, sitting, standing, and falling, and the final recognition accuracy reached more than 97%, which achieved the expected goal.



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*Source: 7581079-2022-08-17.xml*