# Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

Authors: Lihua Liu; MA Qing; Si Chen; Zhifang Li
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The current action recognition method has good effect when applied to static recognition, but, when applied to dynamic action sequence recognition, the temporal and spatial feature segmentation is too dependent on sample template, resulting in low recognition accuracy. To address the inadequacies of standard movement detection techniques in the application of comparable domains, a deep learning algorithm is utilised to recognise Tai Chi Chuan motions. For Tai Chi Chuan movement human body skeleton framework, add image depth parameter is added, and OpenPose model is utilised to estimate joint point coordinates. The ST-GCN deep learning model was created to recognise Tai Chi Chuan motions by extracting movement features from the spatiotemporal trajectory of human joints during Tai Chi Chuan movements. Instance test results show that rate of using the deep learning algorithm of gesture recognition is 89.22%, with significantly lower error detection rate, which is good for Tai chi chuan movement recognition effect.


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## Body

## 1. Introduction

Chinese martial arts are diverse, including Tai Chi Chuan, which mostly conforms to the concept of Chinese culture nature WuShu, capable of embodying the traditional ethnic elements of sports one of [1]. Tai Chi Chuan is a martial art formed by the perfect combination of Chinese traditional culture and the practice of defense and attack, which conforms to the physiological structure of the human body and the laws of nature. It is also the product of the development of Chinese culture to a certain extent and has the exercise effect of strengthening the body and cultivating the sentiment. Tai Chi Chuan movement training is not a random physical activity but a professional activity that is organized and planned based on the sports skills of Tai Chi Chuan. It is systematic, scientific, repeatable, and sustainable [2]. Taijiquan is widely spread around the world because it is easy to practice and suitable for all ages. However, at the same time, the popularity and simplicity of taijiquan also result in different degrees of deformation in the practice of practitioners, which affects the quality of learning.To achieve motion recognition, traditional motion recognition technology primarily employs wearable sensors to gather and interpret relevant data based on inertial motion principle, mechanical motion principle, or electromagnetic field variation information [3, 4]. Although the following approaches have high identification accuracy, they are restricted by equipment, location, and other factors and hence cannot be promoted and applied to a vast number of Tai Chi movements. The introduction and improvement of optical capture systems and image processing technologies have opened up a new avenue for human movement recognition research [5]. The paradigm provided in the literature [6] is appropriate for imaging fields or camera recordings when the perspective is not sensitive. The approach performs better on the hand motion recognition benchmark while operating on a single domain (intradomain motion classification). Using sequence and shape learning approaches, literature [7] suggested HAR-deep architecture. To implement action recognition, the built BiLSTM is used to learn the action sequence, and the pretrained convolutional neural network is used to extract the action information in each frame to mimic the temporal connection between the action frames. In literature [8], a dynamic gesture recognition system with gesture spots was designed based on the principle of self-organizing mapping, and the recognition effect was good for gesture recognition with noise interference. Literature [9] proposed a new network structure, ResLNet, which can cooperate with LSTM more effectively under the residual structure, thus learning a better space-time representation without additional computational costs with the proposed embedded variable step coproduct. HAR (human motion recognition) was used to learn the temporal and spatial aspects of skeletal sequence from various frames of human activities in the literature [10]. Then, to integrate the process of all frames formed by diverse human activities and match the spatial structural information necessary for action detection, multitask learning networks (MTLNs) were utilised. A new human instance-level video motion recognition system was suggested in the literature [11]. People boxes and key points are used to indicate instance-level attributes in this framework. The motion area’s characteristics are used as the network’s input to reflect dynamic motion changes over time. In literature [12], double convolution theory was applied to perform threshold segmentation and extraction of human motion features from original images of human movements under high intensity. Combined with Gaussian distribution and Kalman filter image processing, Bayesian classification model is used to realize human motion tracking recognition.Tai Chi Chuan movement recognition is of great research value and practical significance for enriching Tai Chi Chuan movement training methods, and deep learning has been widely used in movement recognition in different fields. In this paper, a deep learning algorithm is applied for Tai Chi movement recognition. Action recognition technology is used to assist in Tai Chi teaching and guidance, hoping to find and remedy the shortcomings of Tai Chi movement training methods and provide theoretical reference for Tai Chi movement training practice.


## 2. Research on Tai Chi Chuan Movement Recognition Method Based on Deep Learning Algorithm

### 2.1. Get the Human Skeleton Frame of Tai Chi Chuan Movement

Is it possible to increase the accuracy of Tai Chi Chuan identification by using a standard for recognising the matching Tai Chi Chuan motions based on the movement of human joints and bones? As a result, this paper collects Tai Chi Chuan movement images from practitioners using optical image acquisition equipment and then establishes the bone movement sequence corresponding to the Tai Chi Chuan movement in order to determine the human joint point coordinates of the corresponding movement. In this study, Kinect V2 somatosensory camera based on time-of-flight method is used as a visual sensor to collect Tai Chi Chuan scene images with depth information. In the bone motion sequence, the position information of the important joints of the human body is recorded in each frame, which is generally expressed in Cartesian coordinates relative to the acquisition equipment. In the process of human movement, in addition to the extension, flexion, and lifting of parts, it also includes the rotation of limbs and trunk [13].Every two bytes represent a pixel point in the depth data pixels collected by Kinect V2 somatosensory camera. The high 13 bits of each pixel (16 bits in total) are the distance information between the target and the RGB camera, and the low 3 bits of each pixel are the index of the human target. When the index value is 0, it means that the human target is not found. When the index value is 1 or greater, it indicates that a human target is found. When the target is detected, then the depth distance is normalized. The value of the data is located in the 0–255 interval. Then, the depth data is allocated to the three channels of the pixel to render the depth image in pseudogray form [14].The size of the depth image obtained by Kinect V2 is 512 × 424, while the size of the color image is 1920 × 1080, which is much larger than the size of the depth image. Therefore, it is necessary to define the coordinate system of human skeletal joints in different images. The corresponding coordinate systems are established based on the space where the human body is located, the camera imaging space, the image two-dimensional imaging plane, and the image pixels, and the conversion relationship between the coordinate systems is obtained according to the rotation and translation of the spatial coordinate system, the imaging principle, and parameters of the camera lens [15]. This work transforms the coordinate system of the original data representation before recognising and processing Tai Chi Chuan action picture data, which is primarily a representation transformation in spatial position and does not incorporate time-domain information. For each frame of the picture, the aforementioned coordinate conversion is done. The overall centroid of the probability mass is determined using average displacement in the local density mode, and then the human joints are inferred. To acquire the bone joint sites of the human body during Tai Chi Chuan, the virtual joints are mapped to the bone joints, and the human bones are fitted by taking into account the temporal continuity of bone training data and a priori knowledge.Because the lengths of various Tai Chi Chuan motions vary and different actors do the same action at varying speeds, the length of the collected bone motion sequence varies, and each sample includes distinct frames. As a result, the frames of the sample data must be normalized. This study will employ the random sliding window to intercept and fill the sample while normalising the sample time duration. The random sliding window is a common strategy in length normalization: take the target length as the size of the sliding window, slide the window randomly on the length of different samples, and the sample segments in the window are the samples after length normalization.The specific method of using random sliding window to normalize the sample time scale is as follows: for target framefa and frame f of Tai Chi Chuan action image sequence [16]:(1)
Iffa<f, frames R0,f−fa to R0,f−fa+fa in the action image sequence are selected as the new samples after length normalization.(2)
Iffa>f, R0,f−fa zero frames are filled before the first frame of the action image sequence, and fa−f−R0,f−fa zero frames are filled after the last frame, so as to obtain the new sample after the normalization of the zero filling length.(3)
Iffa=f, the original sample is the new sample. Ra,b is a function that generates random integers between a and b.After obtaining the skeleton frame of taijiquan practitioners, it is necessary to accurately identify the type and standard of taijiquan movements and determine the coordinates of each node of corresponding movements by using the human skeleton model.


### 2.2. Tai Chi Chuan Action of Human Bone Joint Location

In this paper, a top-down OpenPose human skeleton model was used to obtain the coordinates of human skeleton during Tai Chi exercise. In order to realize the data fusion of visual sensor and inertial sensor, bone model of 25 nodes is selected in this paper. Using OpenPose’s correlation technique, the 2D coordinates of the human skeleton model in the pixel coordinate system can be retrieved from the color picture. Only by finding the location of the object points matching to the extracted points on the color picture in the related depth image can the 3D coordinates of the joint be determined. The movement of the limbs and trunk is the most important part of the human movement process. Because the limbs are more flexible than the trunk, the movement amplitude of the limbs is bigger and the movement mode is more diversified, while the movement amplitude of the trunk is less and the mode is more single. At the same moment, the upper and lower limbs have separate movement patterns. For various performers, the same action might result in varied left and right limb movement patterns, as well as different movement habits [17]. Considering that the different joints have different sports in the same movement pattern, according to the joints in the body parts and adjacency principle, in this article all body joints are divided into different joint groups: left upper limb joint group BLUA, right upper limb joint group BRUA, the left leg joint group BLDL, right lower limb joint group BRDL, and torso joint group BBT.The item being photographed will be warped during imaging due to the nature of optical components having an effect on light propagation. This is known as distortion. Distortion is a frequent occurrence in imaging systems using lenses, and it may also be seen in depth maps generated using the time of flight approach. As a result, using the above picture three-dimensional coordinate transformation relations, the human body joint coordinates may be determined using the following procedure. Let the coordinate of a point on the depth image beu,v in the pixel coordinate system, and let the depth value of this point be d. In order to ensure the accuracy of taijiquan movement recognition, the target pixel coordinates are interpolated. Du,v is used to represent the function value corresponding to point u,v on the depth image; then the calculation formula of the corresponding point depth value is [18](1)d=u2v2Du1,v1+1−v2Du1,v1+1+1−u2v2Du1+1,v1+1−v2Du1+1,v1+1.When part of the depth value in the above equation is 0, skip the corresponding points and simply make a linear interpolation or take the depth value of any point. According to the internal parameters of the depth camera obtained by calibration, the included angle between the pixel coordinate system and the image coordinate system is very small, so this item can be ignored, and the calculation formula of the three-dimensional coordinates of the key nodes in the depth camera coordinate system is as follows:(2)x=f−1du−u0y=f−1dv−v0z=d.In the above formula,f is the imaging focal length of the depth camera. According to the above formula, the 3D coordinate of the image point corresponding to any point whose depth value is not 0 in the RGBD image can be obtained in the camera coordinate system. After the RGBD image is obtained, the first three channels are separated and an RGB image with the same size as the depth image is synthesized. The OpenPose algorithm is used to extract the 2D coordinates of the key nodes in the separated RGB image in the pixel coordinate system. After coordinate transformation, the 3D coordinates of the key nodes in the camera coordinate system can be obtained [19]. Because Tai Chi Chuan movements have high requirements for consistency, the starting position and body orientation of each Tai Chi Chuan move are not fixed, so there will be the same move but great difference in track. The Tai Chi Chuan movement track is segmented and features are extracted to facilitate the subsequent movement recognition.


### 2.3. Extraction of Human Characteristics of Tai Chi Chuan Movement

In this paper, the method of clustering for subtrajectories is adopted and the Tai Chi Chuan trajectories are clustered under the constraint of MDL principle. Firstly, suitable feature points are selected from the trajectory to complete the segmentation of the original trajectory. The effect of trajectory segmentation mainly depends on the quality of the selected feature points, so the selected feature points should be as close as possible to the position where the trajectory has the greatest variation. Let the trajectoryT consist of points t1,t2,…,tn. The selected feature points are pf1,pf2,…,pfk. The entire trajectory is segmented according to features, and an estimate of the original trajectory can be obtained by connecting these feature points in turn. In the process of track segmentation, the minimum description length minLa represents the sum of all track lengths and minLa|b means that the sum of all original tracks needs to compress the precision and indirect segmentation results as much as possible. Calculation formula of minLa is as follows [20, 21]:(3)minLa=∑j=1k−1lgLpfj,pfj+1,where Lpfj,pfj+1 represents the Euclidean distance between pfj and pfj+1. Considering that the original track will completely wrap each sliver track, the parallel distance will not be considered when calculating the distance between the subtrack and the original track. Similarly, the value of minLa|b can be calculated. After determining the correlation function of minLa and minLa|b, specific feature points on a trajectory can be determined through calculation. However, it is relatively difficult to obtain the global optimal solution in this problem, so local optimal solution is selected to approximately replace the global optimal solution [22].(4)Ckpfi,pfk=minLa|b+minLa<Cnkpfi,pfk=minLa.In the above formula,Ck represents the coding length of the broken line formed by interconnecting feature points; Cnk represents the length of code required for the original trajectory. For the consideration of indirectness, when any point pfk in pfi and pfj satisfies the above equation, pfi and pfj are extrapolated as far as possible to obtain the local optimal solution, and the global optimal solution is approximately replaced by substitution, and the points that meet this condition are selected as feature points. ST-GCN model was established to identify Tai Chi Chuan movement with characteristic points extracted from Tai Chi Chuan movement trajectory as learning samples.


### 2.4. Deep Learning Online Tai Chi Movement Recognition

ST-GCN is the first to apply GCN to skeleton-based motion recognition tasks. It uses graph convolution to implicitly learn the information features of bone sequences instead of manual features extraction, so this model is simple and has good performance. The coordinates of human bone points of the calculated Tai Chi Chuan motions generate a skeleton space-time map comprising information on bone point changes in an action sequence. The convolution of space-time map on the skeleton space-time map may be used to create the ST-GCN model. The topology of the ST-GCN network has 9 ST-GCN units, and the size of the space-time convolution kernel is 39. Each ST-GCN unit employs feature residual fusion, which allows for cross-region feature fusion to improve the model. After the ST-GCN unit adopts a dropout probability of 0.5, the risk of a fitting model is reduced. At the same time, after the 4, 7 convolution layer uses a step length of 2 pooling layer operation, for the sampling of network characteristics, the danger of a fitting model is reduced. Models 1–3 of the ST-GCN have 64 output dimensions, 4–6 have 128 output dimensions, and 7–9 have 256 output dimensions.. For the last output dimension, Softmax classifier can be used to classify the input skeleton sequence. Then, the obtained tensors are combined globally to obtain 256-dimensional feature vectors of each sequence and input into Softmax classifier [23].In the construction of space-time convolution graph, the input image and output feature graph are regarded as a two-dimensional matrix grid, and the two-dimensional convolution operation can be understood as a nonlinear mapping of input to output feature. The graph convolution formula in space is as follows [24]:(5)outgti=Igti,gtjωgti,gtjZtigtjwhere I is the input eigenmatrix; ω is the weight function, and the filter similar to two-dimensional convolution is defined; Ztigtj is the cardinality of the normalized term equal to the corresponding subset, which is used to balance the contribution of different subsets to the output. The convolution formula of the space graph above is extended to the time dimension. In constructing the skeleton space-time diagram, the time dimension is defined by joining two identical bone points in two adjacent frames, so a sequence of skeleton frames with a time range of Ψ can be selected and a time dimension can be added by applying the spatial graph convolution within this sequence. After the practitioner inputs the Tai Chi Chuan movement image sequence into the ST-GCN model, the mapping result is the recognition result of Tai Chi Chuan movement after the convolution processing of space-time graph.


## 2.1. Get the Human Skeleton Frame of Tai Chi Chuan Movement

Is it possible to increase the accuracy of Tai Chi Chuan identification by using a standard for recognising the matching Tai Chi Chuan motions based on the movement of human joints and bones? As a result, this paper collects Tai Chi Chuan movement images from practitioners using optical image acquisition equipment and then establishes the bone movement sequence corresponding to the Tai Chi Chuan movement in order to determine the human joint point coordinates of the corresponding movement. In this study, Kinect V2 somatosensory camera based on time-of-flight method is used as a visual sensor to collect Tai Chi Chuan scene images with depth information. In the bone motion sequence, the position information of the important joints of the human body is recorded in each frame, which is generally expressed in Cartesian coordinates relative to the acquisition equipment. In the process of human movement, in addition to the extension, flexion, and lifting of parts, it also includes the rotation of limbs and trunk [13].Every two bytes represent a pixel point in the depth data pixels collected by Kinect V2 somatosensory camera. The high 13 bits of each pixel (16 bits in total) are the distance information between the target and the RGB camera, and the low 3 bits of each pixel are the index of the human target. When the index value is 0, it means that the human target is not found. When the index value is 1 or greater, it indicates that a human target is found. When the target is detected, then the depth distance is normalized. The value of the data is located in the 0–255 interval. Then, the depth data is allocated to the three channels of the pixel to render the depth image in pseudogray form [14].The size of the depth image obtained by Kinect V2 is 512 × 424, while the size of the color image is 1920 × 1080, which is much larger than the size of the depth image. Therefore, it is necessary to define the coordinate system of human skeletal joints in different images. The corresponding coordinate systems are established based on the space where the human body is located, the camera imaging space, the image two-dimensional imaging plane, and the image pixels, and the conversion relationship between the coordinate systems is obtained according to the rotation and translation of the spatial coordinate system, the imaging principle, and parameters of the camera lens [15]. This work transforms the coordinate system of the original data representation before recognising and processing Tai Chi Chuan action picture data, which is primarily a representation transformation in spatial position and does not incorporate time-domain information. For each frame of the picture, the aforementioned coordinate conversion is done. The overall centroid of the probability mass is determined using average displacement in the local density mode, and then the human joints are inferred. To acquire the bone joint sites of the human body during Tai Chi Chuan, the virtual joints are mapped to the bone joints, and the human bones are fitted by taking into account the temporal continuity of bone training data and a priori knowledge.Because the lengths of various Tai Chi Chuan motions vary and different actors do the same action at varying speeds, the length of the collected bone motion sequence varies, and each sample includes distinct frames. As a result, the frames of the sample data must be normalized. This study will employ the random sliding window to intercept and fill the sample while normalising the sample time duration. The random sliding window is a common strategy in length normalization: take the target length as the size of the sliding window, slide the window randomly on the length of different samples, and the sample segments in the window are the samples after length normalization.The specific method of using random sliding window to normalize the sample time scale is as follows: for target framefa and frame f of Tai Chi Chuan action image sequence [16]:(1)
Iffa<f, frames R0,f−fa to R0,f−fa+fa in the action image sequence are selected as the new samples after length normalization.(2)
Iffa>f, R0,f−fa zero frames are filled before the first frame of the action image sequence, and fa−f−R0,f−fa zero frames are filled after the last frame, so as to obtain the new sample after the normalization of the zero filling length.(3)
Iffa=f, the original sample is the new sample. Ra,b is a function that generates random integers between a and b.After obtaining the skeleton frame of taijiquan practitioners, it is necessary to accurately identify the type and standard of taijiquan movements and determine the coordinates of each node of corresponding movements by using the human skeleton model.


## 2.2. Tai Chi Chuan Action of Human Bone Joint Location

In this paper, a top-down OpenPose human skeleton model was used to obtain the coordinates of human skeleton during Tai Chi exercise. In order to realize the data fusion of visual sensor and inertial sensor, bone model of 25 nodes is selected in this paper. Using OpenPose’s correlation technique, the 2D coordinates of the human skeleton model in the pixel coordinate system can be retrieved from the color picture. Only by finding the location of the object points matching to the extracted points on the color picture in the related depth image can the 3D coordinates of the joint be determined. The movement of the limbs and trunk is the most important part of the human movement process. Because the limbs are more flexible than the trunk, the movement amplitude of the limbs is bigger and the movement mode is more diversified, while the movement amplitude of the trunk is less and the mode is more single. At the same moment, the upper and lower limbs have separate movement patterns. For various performers, the same action might result in varied left and right limb movement patterns, as well as different movement habits [17]. Considering that the different joints have different sports in the same movement pattern, according to the joints in the body parts and adjacency principle, in this article all body joints are divided into different joint groups: left upper limb joint group BLUA, right upper limb joint group BRUA, the left leg joint group BLDL, right lower limb joint group BRDL, and torso joint group BBT.The item being photographed will be warped during imaging due to the nature of optical components having an effect on light propagation. This is known as distortion. Distortion is a frequent occurrence in imaging systems using lenses, and it may also be seen in depth maps generated using the time of flight approach. As a result, using the above picture three-dimensional coordinate transformation relations, the human body joint coordinates may be determined using the following procedure. Let the coordinate of a point on the depth image beu,v in the pixel coordinate system, and let the depth value of this point be d. In order to ensure the accuracy of taijiquan movement recognition, the target pixel coordinates are interpolated. Du,v is used to represent the function value corresponding to point u,v on the depth image; then the calculation formula of the corresponding point depth value is [18](1)d=u2v2Du1,v1+1−v2Du1,v1+1+1−u2v2Du1+1,v1+1−v2Du1+1,v1+1.When part of the depth value in the above equation is 0, skip the corresponding points and simply make a linear interpolation or take the depth value of any point. According to the internal parameters of the depth camera obtained by calibration, the included angle between the pixel coordinate system and the image coordinate system is very small, so this item can be ignored, and the calculation formula of the three-dimensional coordinates of the key nodes in the depth camera coordinate system is as follows:(2)x=f−1du−u0y=f−1dv−v0z=d.In the above formula,f is the imaging focal length of the depth camera. According to the above formula, the 3D coordinate of the image point corresponding to any point whose depth value is not 0 in the RGBD image can be obtained in the camera coordinate system. After the RGBD image is obtained, the first three channels are separated and an RGB image with the same size as the depth image is synthesized. The OpenPose algorithm is used to extract the 2D coordinates of the key nodes in the separated RGB image in the pixel coordinate system. After coordinate transformation, the 3D coordinates of the key nodes in the camera coordinate system can be obtained [19]. Because Tai Chi Chuan movements have high requirements for consistency, the starting position and body orientation of each Tai Chi Chuan move are not fixed, so there will be the same move but great difference in track. The Tai Chi Chuan movement track is segmented and features are extracted to facilitate the subsequent movement recognition.


## 2.3. Extraction of Human Characteristics of Tai Chi Chuan Movement

In this paper, the method of clustering for subtrajectories is adopted and the Tai Chi Chuan trajectories are clustered under the constraint of MDL principle. Firstly, suitable feature points are selected from the trajectory to complete the segmentation of the original trajectory. The effect of trajectory segmentation mainly depends on the quality of the selected feature points, so the selected feature points should be as close as possible to the position where the trajectory has the greatest variation. Let the trajectoryT consist of points t1,t2,…,tn. The selected feature points are pf1,pf2,…,pfk. The entire trajectory is segmented according to features, and an estimate of the original trajectory can be obtained by connecting these feature points in turn. In the process of track segmentation, the minimum description length minLa represents the sum of all track lengths and minLa|b means that the sum of all original tracks needs to compress the precision and indirect segmentation results as much as possible. Calculation formula of minLa is as follows [20, 21]:(3)minLa=∑j=1k−1lgLpfj,pfj+1,where Lpfj,pfj+1 represents the Euclidean distance between pfj and pfj+1. Considering that the original track will completely wrap each sliver track, the parallel distance will not be considered when calculating the distance between the subtrack and the original track. Similarly, the value of minLa|b can be calculated. After determining the correlation function of minLa and minLa|b, specific feature points on a trajectory can be determined through calculation. However, it is relatively difficult to obtain the global optimal solution in this problem, so local optimal solution is selected to approximately replace the global optimal solution [22].(4)Ckpfi,pfk=minLa|b+minLa<Cnkpfi,pfk=minLa.In the above formula,Ck represents the coding length of the broken line formed by interconnecting feature points; Cnk represents the length of code required for the original trajectory. For the consideration of indirectness, when any point pfk in pfi and pfj satisfies the above equation, pfi and pfj are extrapolated as far as possible to obtain the local optimal solution, and the global optimal solution is approximately replaced by substitution, and the points that meet this condition are selected as feature points. ST-GCN model was established to identify Tai Chi Chuan movement with characteristic points extracted from Tai Chi Chuan movement trajectory as learning samples.


## 2.4. Deep Learning Online Tai Chi Movement Recognition

ST-GCN is the first to apply GCN to skeleton-based motion recognition tasks. It uses graph convolution to implicitly learn the information features of bone sequences instead of manual features extraction, so this model is simple and has good performance. The coordinates of human bone points of the calculated Tai Chi Chuan motions generate a skeleton space-time map comprising information on bone point changes in an action sequence. The convolution of space-time map on the skeleton space-time map may be used to create the ST-GCN model. The topology of the ST-GCN network has 9 ST-GCN units, and the size of the space-time convolution kernel is 39. Each ST-GCN unit employs feature residual fusion, which allows for cross-region feature fusion to improve the model. After the ST-GCN unit adopts a dropout probability of 0.5, the risk of a fitting model is reduced. At the same time, after the 4, 7 convolution layer uses a step length of 2 pooling layer operation, for the sampling of network characteristics, the danger of a fitting model is reduced. Models 1–3 of the ST-GCN have 64 output dimensions, 4–6 have 128 output dimensions, and 7–9 have 256 output dimensions.. For the last output dimension, Softmax classifier can be used to classify the input skeleton sequence. Then, the obtained tensors are combined globally to obtain 256-dimensional feature vectors of each sequence and input into Softmax classifier [23].In the construction of space-time convolution graph, the input image and output feature graph are regarded as a two-dimensional matrix grid, and the two-dimensional convolution operation can be understood as a nonlinear mapping of input to output feature. The graph convolution formula in space is as follows [24]:(5)outgti=Igti,gtjωgti,gtjZtigtjwhere I is the input eigenmatrix; ω is the weight function, and the filter similar to two-dimensional convolution is defined; Ztigtj is the cardinality of the normalized term equal to the corresponding subset, which is used to balance the contribution of different subsets to the output. The convolution formula of the space graph above is extended to the time dimension. In constructing the skeleton space-time diagram, the time dimension is defined by joining two identical bone points in two adjacent frames, so a sequence of skeleton frames with a time range of Ψ can be selected and a time dimension can be added by applying the spatial graph convolution within this sequence. After the practitioner inputs the Tai Chi Chuan movement image sequence into the ST-GCN model, the mapping result is the recognition result of Tai Chi Chuan movement after the convolution processing of space-time graph.


## 3. Case Experiment

Whether the motion recognition method proposed in this paper can be applied to daily Tai Chi Chuan teaching needs to be tested by small-scale experiments. Therefore, this section will randomly select a certain number of Tai Chi Chuan learners from different age groups and learning periods to conduct the performance experiment of the identification method.


### 3.1. Example Parameter Description

Volunteers who are in good physical condition and can proficiently complete 24 Tai Chi were recruited for this experiment. Subjects were in excellent physical condition, with no history of surgery in their arms, waist, thighs, or legs and no significant sickness in the previous six months. One week before the experiment, there was no strenuous activity and the body was not tired and can actively cooperate with researchers to complete the evaluation of experimental indicators according to experimental standards. 1000 Taijiquan learners aged between 18 and 70 were randomly selected to collect basic information of taijiquan practitioners and classify them. Tai Chi Chuan learners were divided into 6 groups according to their age, learning time, and learning experience of similar types of sports. The grouping parameters of 1000 Tai Chi practitioners participating in this identification method test are shown in Table1.Table 1
Grouping parameters of Tai Chi Chuan practitioners.
Group/projectAge sectionAverage Tai Chi practice time/yearStandard proportion of Tai Chi movements (%)Those with similar sports learning experience accounted for (%)G118–230.547.517.5G220–251.250.323.9G325–402.342.810.8G440–553.935.620.6G555–606.851.227.4G660–7012.463.932.3The Tai chi practitioners as shown in Table1 record Tai Chi Chuan exercise under the condition of the same scene independently and without interfering. Tai Chi Chuan exercise video was collected with MV-EM130 C camera, and the video resolution was 1280 × 960. Each type of action was captured with about 10s of action sequences, and the number of frames of each action sequence was about 300. After preprocessing the practitioners’ Tai Chi Chuan practice videos according to the same steps, the corresponding groups and codes were recorded. The sequence of coded Tai Chi exercise videos was randomly scrambled to form an experimental dataset for Tai Chi action recognition for this experimental study.


### 3.2. Experimental Process Design

Based on the recognition accuracy of the action recognition method, this paper compares the recognition method with the recognition method based on Bayes and the recognition method based on self-organization mapping.Videos of professional Tai Chi practitioners were used to demonstrate Tai Chi movements, and the standard demonstration videos of Tai Chi movements recorded from different angles were used as parameter training sets of recognition methods to determine the identification parameters of training recognition methods. In order to avoid subjective variables affecting the result of the experiment, 20 professional Tai Chi Quan teaching experts were invited in the experiment to cross score the movement level and standard degree of 1000 Tai Chi Quan practitioners, who are required to participate in the movements strictly in accordance with the national sports regulations. The main factors of Tai Chi evaluation such as head, waist, shoulder, elbow, wrist, hip, knee, and ankle were objectively evaluated. The degree of beauty of Tai Chi movements was not considered in the rating, as the standard degree of movements was only concerned. The rating results of Tai Chi Chuan teaching experts were taken as the measurement standard of the accuracy of the motion recognition method, so as to evaluate the accuracy of the motion recognition method.


### 3.3. Experimental Results

The data comparison of the recognition accuracy and error detection rate of the action recognition methods when different methods are used to identify the dataset of the action training video of the practitioner is shown in Table2.Table 2
Data comparison of recognition accuracy and false detection rate of recognition methods.
GroupDeep learningBayesian modelSelf-organizing mapAccuracy rate (%)Error detection rate (%)Accuracy rate (%)Error detection rate (%)Accuracy rate (%)Error detection rate (%)G187.95.665.812.463.818.4G286.25.470.210.668.516.3G386.33.777.98.772.414.1G492.52.880.17.578.212.9G590.62.288.57.879.910.8G691.81.986.76.977.611.0According to the data analysis in Table2, the action recognition accuracy of the deep learning algorithm for each group of objects is higher than those of the other two recognition models. Compared with the Bayesian model, the average error detection rate of the deep learning algorithm in each dataset is reduced by 5.38%, reaching 3.6%, and the average recognition accuracy is improved by 11.02%, reaching 89.22%. Compared with the self-organizing mapping model, deep learning algorithm of error detection rate was reduced by 10.32% on average, 15.82% increase of recognition rate. It shows that the deep learning algorithm can accurately identify the Tai Chi movements of objects with different proficiency levels.Figures1–3 show the three identification methods confusion matrix of the different parts of the recognition results of each act. Three methods of gesture recognition can be seen from the confusion matrix for Tai Chi Chuan different action recognition effect of refinement to the parts of the body.Figure 1
Confusion matrix identified by deep learning algorithm.Figure 2
Confusion matrix identified by Bayesian model.Figure 3
Confusion matrix of self-organizing map recognition.In the confusion matrix, the elements on the diagonal represent the recognition accuracy of each action category. The darker the color is, the more accurate the recognition and classification will be. The elements on the nondiagonal represent the ratio of misclassification. By summarizing the above experimental data, it can be seen that the Tai Chi Chuan movement recognition method based on deep learning algorithm proposed in this paper has good recognition accuracy and efficiency, and the recognition effect is better than those of other methods, which can provide assistance for the movement guidance and correction in the promotion of Tai Chi Chuan teaching.


## 3.1. Example Parameter Description

Volunteers who are in good physical condition and can proficiently complete 24 Tai Chi were recruited for this experiment. Subjects were in excellent physical condition, with no history of surgery in their arms, waist, thighs, or legs and no significant sickness in the previous six months. One week before the experiment, there was no strenuous activity and the body was not tired and can actively cooperate with researchers to complete the evaluation of experimental indicators according to experimental standards. 1000 Taijiquan learners aged between 18 and 70 were randomly selected to collect basic information of taijiquan practitioners and classify them. Tai Chi Chuan learners were divided into 6 groups according to their age, learning time, and learning experience of similar types of sports. The grouping parameters of 1000 Tai Chi practitioners participating in this identification method test are shown in Table1.Table 1
Grouping parameters of Tai Chi Chuan practitioners.
Group/projectAge sectionAverage Tai Chi practice time/yearStandard proportion of Tai Chi movements (%)Those with similar sports learning experience accounted for (%)G118–230.547.517.5G220–251.250.323.9G325–402.342.810.8G440–553.935.620.6G555–606.851.227.4G660–7012.463.932.3The Tai chi practitioners as shown in Table1 record Tai Chi Chuan exercise under the condition of the same scene independently and without interfering. Tai Chi Chuan exercise video was collected with MV-EM130 C camera, and the video resolution was 1280 × 960. Each type of action was captured with about 10s of action sequences, and the number of frames of each action sequence was about 300. After preprocessing the practitioners’ Tai Chi Chuan practice videos according to the same steps, the corresponding groups and codes were recorded. The sequence of coded Tai Chi exercise videos was randomly scrambled to form an experimental dataset for Tai Chi action recognition for this experimental study.


## 3.2. Experimental Process Design

Based on the recognition accuracy of the action recognition method, this paper compares the recognition method with the recognition method based on Bayes and the recognition method based on self-organization mapping.Videos of professional Tai Chi practitioners were used to demonstrate Tai Chi movements, and the standard demonstration videos of Tai Chi movements recorded from different angles were used as parameter training sets of recognition methods to determine the identification parameters of training recognition methods. In order to avoid subjective variables affecting the result of the experiment, 20 professional Tai Chi Quan teaching experts were invited in the experiment to cross score the movement level and standard degree of 1000 Tai Chi Quan practitioners, who are required to participate in the movements strictly in accordance with the national sports regulations. The main factors of Tai Chi evaluation such as head, waist, shoulder, elbow, wrist, hip, knee, and ankle were objectively evaluated. The degree of beauty of Tai Chi movements was not considered in the rating, as the standard degree of movements was only concerned. The rating results of Tai Chi Chuan teaching experts were taken as the measurement standard of the accuracy of the motion recognition method, so as to evaluate the accuracy of the motion recognition method.


## 3.3. Experimental Results

The data comparison of the recognition accuracy and error detection rate of the action recognition methods when different methods are used to identify the dataset of the action training video of the practitioner is shown in Table2.Table 2
Data comparison of recognition accuracy and false detection rate of recognition methods.
GroupDeep learningBayesian modelSelf-organizing mapAccuracy rate (%)Error detection rate (%)Accuracy rate (%)Error detection rate (%)Accuracy rate (%)Error detection rate (%)G187.95.665.812.463.818.4G286.25.470.210.668.516.3G386.33.777.98.772.414.1G492.52.880.17.578.212.9G590.62.288.57.879.910.8G691.81.986.76.977.611.0According to the data analysis in Table2, the action recognition accuracy of the deep learning algorithm for each group of objects is higher than those of the other two recognition models. Compared with the Bayesian model, the average error detection rate of the deep learning algorithm in each dataset is reduced by 5.38%, reaching 3.6%, and the average recognition accuracy is improved by 11.02%, reaching 89.22%. Compared with the self-organizing mapping model, deep learning algorithm of error detection rate was reduced by 10.32% on average, 15.82% increase of recognition rate. It shows that the deep learning algorithm can accurately identify the Tai Chi movements of objects with different proficiency levels.Figures1–3 show the three identification methods confusion matrix of the different parts of the recognition results of each act. Three methods of gesture recognition can be seen from the confusion matrix for Tai Chi Chuan different action recognition effect of refinement to the parts of the body.Figure 1
Confusion matrix identified by deep learning algorithm.Figure 2
Confusion matrix identified by Bayesian model.Figure 3
Confusion matrix of self-organizing map recognition.In the confusion matrix, the elements on the diagonal represent the recognition accuracy of each action category. The darker the color is, the more accurate the recognition and classification will be. The elements on the nondiagonal represent the ratio of misclassification. By summarizing the above experimental data, it can be seen that the Tai Chi Chuan movement recognition method based on deep learning algorithm proposed in this paper has good recognition accuracy and efficiency, and the recognition effect is better than those of other methods, which can provide assistance for the movement guidance and correction in the promotion of Tai Chi Chuan teaching.


## 4. Conclusion

Motion recognition technology has been widely used in motion analysis, behavior recognition, human-computer interaction, and other fields, which can greatly improve the efficiency and accuracy of motion recognition. Tai Chi Quan method can improve the body’s balance ability, improve muscle strength, improve physical quality, improve joint stability, and so on, for different levels of needs of the people’s health and can not be ignored. Many international competitions have made Tai Chi popular all over the world. Fans from all over the world communicate and compete with each other through different platforms, which has promoted the development of Tai Chi. If the movements do not meet the standards in Tai Chi Chuan learning, it will not be possible to achieve the training goal of Tai Chi Chuan. Integrating movement recognition into the auxiliary teaching of Tai Chi Chuan can improve the interest of Tai Chi Chuan practice and open up a new way for the diversified development of Tai Chi Chuan. Based on deep learning algorithm a method of identification for each act is put forward to improve the precision of Tai Chi Chuan movements teaching and the quality of learning and it can be widely used in Tai Chi Chuan lovers and other similar projects evaluation and guidance of Tai Chi Chuan training and is advantageous to the Tai Chi promotion and exercising the right. It will lay a solid foundation for the future research on movement technology, athletic ability, and teaching of other sports.



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*Source: 7974669-2022-04-18.xml*