Taekwondo motion image recognition model based on hybrid neural network algorithm for wearable sensor of Internet of Things

Conventional IoT wearable sensor Taekwondo motion image recognition model mainly uses Anchor fixed proportion whole body target anchor frame to extract recognition features, which is vulnerable to dynamic noise, resulting in low displacement recognition rate of motion image. Therefore, a new IoT wearable sensor Taekwondo motion image recognition model needs to be designed based on hybrid neural network algorithm. That is, the wearable sensor Taekwondo motion image features are extracted, and the hybrid neural network algorithm is used to generate the optimization model of the wearable sensor Taekwondo motion image recognition of the Internet of Things, so as to achieve effective recognition of Taekwondo motion images. The experimental results show that the designed wearable sensor of the Internet of Things based on the hybrid neural network algorithm has a high recognition rate of the motion image displacement of the Taekwondo motion image recognition model, which proves that the designed Taekwondo motion image recognition model has good recognition effect, reliability, and certain application value, and has made certain contributions to optimizing the Taekwondo movement.


Design of wearable sensor of Internet of Things based on hybrid neural network algorithm Taekwondo motion image recognition model
Extraction of wearable sensor Taekwondo motion image features.Wearable sensors are sensor devices that can be worn on the body to collect data.Several types of wearable sensors that can be used for feature extraction include: (1) Accelerometers-Wearable accelerometers typically measure the acceleration and direction of body movement, which can be used to measure motion characteristics such as step count, walking speed, and activity intensity.
(2) Gyroscopes-Gyroscopes can measure rotational movement of the body, such as bending, rolling, and spinning, and can be used to detect motion or posture changes.(3) Heart rate sensors-Heart rate sensors can measure changes in heart rate and the relationship between heart rate and other exercise or activity.(4) Temperature sensors-Temperature sensors can measure skin surface temperature changes, which can be used to detect changes in body temperature and other physiological features.(5) EMG sensors-Electromyography (EMG) sensors can measure the electrical signals generated when muscles contract, which can be used to detect muscle fatigue and activity level.( 6) Body pressure sensors-Body pressure sensors can measure the pressure distribution of various parts of the body, which can be used to detect changes in body position and posture.(7) Optical sensors-Optical sensors can measure the reflection light intensity on the skin surface, which can be used to detect physiological features such as skin color, blood flow, and oxygenation.
The above sensors can be used to collect motion, physiological, and environmental data, and perform feature extraction.By processing and analyzing these data, various features such as human posture, movement behavior, and physiological conditions can be extracted and recognized.This article uses wearable sensors to extract Taekwondo motion image features.
Before moving image recognition, image pre-processing is required, which may be interfered by many factors during moving image transmission, resulting in recognition noise 12 .Therefore, image pre-processing is required before extracting moving image features to reduce noise.That is to use the average calculation method to divide a processing range 13 , screen effective change points, conduct noise removal processing, solve the problem of image blur, reduce image noise, and increase image clarity 14,15 .In addition, the computer can also be used to decompose the motion steps in the moving image, divide the motion details to achieve feature differentiation 16 , extract the comprehensive features of the image, and image feature differentiation y ij as shown in (1) below.
In formula (1), W st , x i−s+1 represents the coordinate element of the moving image, S and T represent the size of the recognition filter respectively.After feature extraction of moving image, because the dimension of feature image cannot be determined, it is necessary to select improved neural network parameters, carry out dimension reduction processing, connect sampling layers 17 , reduce the complexity of dimension reduction calculation, shorten the dimension reduction range, and improve the calculation accuracy.At this time, we can use mathematical methods to describe the Taekwondo moving image 18 , and use formula (2) to calculate the corresponding gray value of pixels V gray In formula (2), V red , V green , V blue they represent different color brightness values of moving images, sa, sb, sc they represent the total content of pixels in the moving image 19 .At this time, the gray value of pixels in the moving image recognition can be used f (x m , y m ) in order to improve the effectiveness of extracted features 20 , it is necessary to calculate the original weighted average details of the moving image g(x, y) , as shown in (3) below.
In formula (3), w(i, j) represents the filtered moving image, M represents the number of pixels in the neigh- borhood, and i and j represent the change threshold 21 of the moving image.After the above steps are completed, the comprehensive features of the image can be extracted f (X) , as shown in (4) below. (1) In formula (4), α represents the iteration threshold set by the identification, g(x) represents the feature rec- ognition index of the moving image.Using the above steps can improve the recognition accuracy of the moving image and reduce the impact of external interference on the final recognition result.

Generate the optimal model of Taekwondo motion image recognition for wearable sensors of the Internet of Things based on hybrid neural network algorithm.
In order to solve the problem that the anchor frame of anchor fixed proportion whole body target is affected by dynamic noise when extracting recognition features, which leads to the low recognition rate of motion image displacement 22,23 , this paper generates an optimization model for Taekwondo motion image recognition of Internet of Things wearable sensor based on hybrid neural network algorithm.The method designed in this paper selects BP and LSTM hybrid neural networks for motion image recognition and judgment 24 , as shown in Fig. 1.
At this time, the standard mean value of each joint point measured by IMU needs to be calculated σ , as shown in (5) below.
In formula (5), b represents the number of joint points, a 1 represents the acceleration value of the joint point, a represents the average acceleration value, n representing the number of sequence frames, combined with the above standard mean 25 , we can judge the motion stillness category of the moving image.At this time, the generated moving image recognition process is shown in Fig. 2.
It can be seen from Fig. 2 that training samples can be input in combination with the above moving image recognition process, and the input value of hidden layer neurons 26 can be calculated.At this time, the optimal model for Taekwondo moving image recognition of wearable sensors of the Internet of Things is built based on the hybrid neural network algorithm E(w) as shown in (6) below 27 .
(5) σ =  www.nature.com/scientificreports/ In formula ( 6), y n represents the hidden neuron connection weight, y represents the input neuron connection weight 28 .Using the above built IoT wearable sensor Taekwondo motion image recognition optimization model can effectively obtain the motion recognition weight, output effective image sequence recognition results 29,30 , and improve the reliability of motion image recognition.

Experiment
In order to verify the recognition effect of the designed wearable sensor Taekwondo motion image recognition model based on the hybrid neural network algorithm, this paper built an experimental platform, and compared it with the conventional wearable sensor Taekwondo motion image recognition model, and carried out experiments, as follows.
Experiment preparation.Combined with the experimental requirements, this paper selects the Solid Works 3D virtual simulation platform as the experimental platform.The experimental platform is equipped with mainstream CAD analysis software, with good comprehensive performance.In the process of Taekwondo movement, the movement modes and basic movement angles of human joints are different for different movements.Therefore, the movement trend of human joints can be predicted according to the movement transformation trend of human joints to achieve coordinated motion control.Therefore, this experiment conducted motion estimation in combination with the MMG signal of human motion images, and generated the basic experimental process, as shown in Fig. 3.
It can be seen from Fig. 3 that during the experiment, the MMG signal of the image can be continuously detected, and the action simulation can be carried out.With the relevant motion angles as a reference, effective control algorithms can be used to correct, so as to collect Taekwondo motion images that meet the experimental requirements, as shown in Fig. 4.
At this time, the joint angle discretization parameters under different motion modes are shown in Table 1.
It can be seen from Table 1 that moving image recognition data can be selected in combination with the above joint discretization parameters.In order to ensure the reliability of recognition, image data needs to be preprocessed, that is, select the PC association platform, adjust the main frequency of moving image processing to 2.5 GHz, and then the corresponding internal memory is 5 GB.Input the above obtained joint angle discretization parameters, calculate the discrete state frequency of different signal segments.After the above steps are completed, the joint angle basic signal graph can be generated to classify the experimental data.At this time, the segment slope and the number of association rules of each signal segment are shown in Table 2.
It can be seen from Table 1 that after the above experimental parameters are set, the experimental hardware can be connected.This paper selects the RFID Internet of Things wearable sensor as the experimental sensor.At this time, the schematic diagram of the experimental hardware device connection is shown in Fig. 5.    www.nature.com/scientificreports/It can be seen from Fig. 5 that the core of the above experimental hardware is Impinj R2000 RFID reader writer, which is configured with a power supply of 9000 mA and several identification antennas.The transmission power of the identification antenna is 0-30 dbm, and the accuracy can be adjusted.During the experiment, it is necessary to ensure that the reading and writing frequency band of the core reader writer is within the specified range.In order to improve the effectiveness of recognition results, this experiment selects US FCC 47 CFG as the support, and sets the ETSI EN 302,208 image recognition standard.During the experiment, it is necessary to always ensure that the reader is in the Inventory working mode, and try to improve the read-write range of the reader.After the above hardware devices are connected, the experimental indicators can be selected and the recognition rate formula after moving image displacement can be designed D , as shown in (7) below.
In formula (7), W represents the position of the base image after displacement, W 0 represents the position of the identified image before displacement, R image preset displacement, the higher the recognition rate after moving image displacement, the better the recognition effect of the moving image recognition model.On the contrary, the lower the recognition rate after moving image displacement, the poorer the recognition effect of the moving image recognition model.Before the experiment process, it is also necessary to connect the MEMS inertial sensor to ensure that it meets the requirements of actual moving image recognition.The specifications and parameters of the sensor are shown in Table 3.
Table 3 shows that the parameters of the above inertial sensors meet the experimental requirements.In addition to the above preparations, it is also necessary to set the relevant parameters of the hybrid neural network and prepare the experimental data set.
The hybrid neural network is a model that combines convolutional neural networks and fully connected neural networks.In the process of image recognition, the following parameters are usually set for the hybrid neural network: (1) Input layer size this refers to the size of the input image, which usually has three dimensions of length, width, and channel number, such as 224 × 224 × 3. ( 2) Convolutional layer parameters these include specifying the size of the convolution kernel, the number of convolution kernels, the step size, and the padding mode.These parameters determine the size of the output of the convolutional layer and the number of feature maps.(3) Pooling layer parameters these include specifying the size of the pooling kernel and the step size.These parameters determine the size of the output of the pooling layer and the number of feature maps.( 4) Fully connected layer parameters these include specifying the number of neurons in the fully connected layer and activation function.
In the recognition process of the hybrid neural network, the first step is to input an image, which then undergoes a series of processing by convolutional layers and pooling layers.The feature maps are continuously reduced in size while extracting different features of the image.Multiple feature maps are then merged into a single vector, and classification is performed through the fully connected layer, ultimately resulting in the classification result of the image.
The Taekwondo motion image recognition data set used in this article is as follows: ( Experimental results and discussion.In combination with the above experimental preparations, we can carry out subsequent experiments on the wearable sensor of the Internet of Things for Taekwondo motion image recognition.That is, in the built experimental platform, the wearable sensor Taekwondo motion image recognition model based on the hybrid neural network algorithm designed in this paper and the conventional wearable sensor Taekwondo motion image recognition model are respectively used for motion image recognition, and the public formula (1) is used to record the motion image displacement recognition rate of the two methods in different motion modes.The experimental results are shown in Table 4.
Table 4 shows that the displacement recognition rate of the motion image of the wearable sensor Taekwondo motion image recognition model designed in this paper based on the hybrid neural network algorithm is high in different motion modes, while the displacement recognition rate of the motion image of the conventional wearable sensor Taekwondo motion image recognition model is relatively low.It proves that the wearable sensor Taekwondo motion image recognition model designed in this paper has good recognition performance, effectiveness and certain application value.
By comparing the contrast, clarity, and recognition time of the denoised images using other algorithms, the advantages of the proposed algorithm in identifying image features were verified.
(1) Contrast refers to the measurement of the different brightness levels between the brightest white and darkest black areas in an image.The greater the difference range, the greater the contrast, and vice versa.This value has no standard definition, therefore, in this study, it is set to 35 based on human comfort level.(2) Clarity is the average gradient of the image, which can sensitively reflect the ability of the image to express small contrasts.Information entropy represents the size of the information contained in the image.With the increase of the information entropy value, the information value contained in the feature image also increases, resulting in higher clarity.(3) Recognition time reflects the recognition efficiency of each model, and this value is recorded by the computer.
The enhancement effect comparison results between the method proposed in this paper and traditional methods are shown in Table 5.
From Table 5, it can be concluded that the larger the average value during image recognition, the better the ability to improve image brightness, and the accuracy of the proposed method is 95.39%, the highest among compared algorithms.This indicates that the proposed algorithm has a better ability to enhance image brightness, ensuring higher contrast and information entropy values.This shows that while the image recognition effect is good, it also contains more information.In terms of algorithm running time, the proposed algorithm also has the shortest time.The experimental results above prove that the proposed algorithm has obvious advantages in identifying image features.

Conclusion
Taekwondo is a common sport, which has many trainers in various countries.The motion pattern of Taekwondo is complex and difficult to identify effectively, so it needs to be comprehensively evaluated through the sensor photography.In recent years, computer vision technology has developed more and more rapidly in China, and some researchers have applied it to the recognition of Taekwondo motion images.The conventional recognition model of Taekwondo motion image has poor recognition effect and does not meet the current recognition requirements.Therefore, this paper designs a new wearable sensor Taekwondo motion image recognition model based on the hybrid neural network algorithm.The experimental results show that the wearable sensor Taekwondo motion image recognition model has good recognition performance, reliability and certain application value, and has made certain contributions to optimizing Taekwondo sports skills.At present, hybrid neural network algorithms have achieved certain results, but there is still room for improvement.Future research will use many state-of-the-art deep learning models such as Alexnet, Googlenet, and Xception to classify images and improve algorithm performance.

Table 1 .
Joint angle discretization parameters under different motion modes.

Table 2 .
Segment slope and number of association rules of experimental data.

Table 3 .
1) KTH-TIPS2-B This data set contains 384 images of 6 different Taekwondo movements (front kick, back kick, turn kick, side kick, high side kick and low side kick).Each movement was performed by two different actors, and each actor performed it 16 times.(2)KyungHee TaeKwonDo Dataset This data set contains 1010 images of 10 different Taekwondo movements (push hands, wrist palm strike, front kick, knee kick, backward kick, back kick, side kick, single leg jump kick, consecutive kick and bottom kick).Each movement was performed by different actors.(3)NTU RGB + D Dataset This data set contains 56 different actions, including 11 different Taekwondo movements.Each movement was performed by 40 different actors, and was captured under RGB and depth sensors.Specifications and parameters of MEMS inertial sensor.After all experimental preparation devices have been connected, subsequent motion image recognition experiments can be conducted.