# Research on Training Model of Volleyball Based on Flexible Strain Sensing Network for Training

Authors: Juan Yin; Mingming Chen; Yuhui Ge; Qingyao Song; Hua Zheng
Journal: Journal of Sensors
 (2022)

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## Abstract

This paper provides an in-depth investigation and analysis of volleyball training patterns using a sensing network composed of flexible strain sensors. This paper models the motion of the hand and leg joints based on an improved human posture estimation technique. The pattern recognition of human motion used the fusion of acceleration sensor and spiral meter data, the output of human motion information from the spiral meter, combined with GA-BP neural network algorithm to repair and fuse the output information, effectively improving the accuracy of posture angle measurement. Based on wireless positioning technology, an improved RFID positioning algorithm combining the LANDMARC algorithm and weighted center-of-mass algorithm in wireless sensor network is proposed, and the positioning accuracy reaches 89.2%; and the error feedback mechanism is introduced to improve the improved algorithm, and the positioning accuracy is improved again by 3.2%. Finally, the two-way dynamic time regularization algorithm is used to align the input action with the standard action, and the action pose evaluation index is compared and analyzed for the action sequence after alignment to obtain the final action quality comparison analysis results. In this paper, the proposed method is applied in practical training, which can effectively locate the stance of athletes and evaluate their movement quality without disturbing them and can provide valuable analysis information to support coaches’ guidance.


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## Body

## 1. Introduction

A flexible sensor is different from traditional hard metal or semiconductor materials; with stretchability and bendability, it can be attached to the human body or complex surface to monitor the excitation signal of a class of electronic devices, because of its significant application prospects in the human movement monitoring, medical diagnosis, human-computer interaction, environmental monitoring, and other fields; since its introduction in 2009, it has received the attention of many researchers at home and abroad [1]. The wrist joint has a complex structure among human joints and plays a very important role in human upper limb movement. Currently, there is a huge demand for accurate measurement of wrist motion in many scenarios such as sports and fitness, health care, and virtual reality entertainment. However, most of the widely used mechanical or inertial wrist joint wear measurement systems have the disadvantage of being uncomfortable and unportable to wear. In recent years, flexible wearable systems for human motion integrated with flexible silicone rubber sensors have received increasingly widespread attention [2]. This kind of flexible wear system has the advantages of wearing comfortable and light, not being restricted by the site and environmental interference, being easy to carry, etc. The wrist joint has a complex structure in human joints and plays a very important role in the upper limb movement of the human body. Currently, there is a huge demand for accurate measurement of wrist motion in many scenarios such as sports and fitness, medical and health care, and virtual reality entertainment. However, most of the widely used mechanical or inertial wrist joint wear measurement systems have the disadvantage of being uncomfortable and unportable to wear. In recent years, flexible wearable systems for human motion integrated with flexible silicone rubber sensors have received increasingly widespread attention. This kind of flexible wearing system has the advantages of being comfortable and light wearing, not restricted by the site and environmental interference, easy to carry, etc.Traditional sports training is when coaches analyze the rules and shortcomings of athletes’ movements through visual observation and rely on their knowledge and experience and then give training suggestions and training methods, which are very dependent on the coach’s quality. The development of modern sports is characterized by the combination of sports and technology, from athletes’ clothing, and sports equipment to training methods, all of which are rich in high-tech components. Therefore, the technology of sports training is the common goal of sports and science and technology workers [3]. Volleyball, with the continuous innovation of competition rules, has led to the innovation of volleyball techniques and tactics. The speed of the ball and the unpredictable tactics of volleyball have greatly increased the offensive and defensive confrontation, which has put forward higher requirements on the physical quality and training level of volleyball players and the coaching level of coaches. The first and most critical step to improving the level is to master the sports information of volleyball players in training and games.National sports teams are increasingly focusing on combining the latest computer technology with traditional sports to improve athlete training and analysis. At present, sports training for volleyball still relies mainly on the personal experience of coaches, which makes training more costly and difficult to maintain stable quality. Some sports units have introduced professional software such as Data Volley to assist in training, but it requires manual entry of complex descriptions of movement records; there are also research institutions that rely on human sensors to obtain movement parameters, but they can cause inconvenience to the athletes’ activities [4]. Physical education assessment is related to the development and implementation of educational policies, and the assessment of school physical education classes is one of the basic measures to summarize and improve school physical education, as well as a basic guarantee for the achievement of school physical education goals [5]. This study combines the realistic needs of PE class assessment and the technical basis of wearable device technology, constructs a middle school PE class assessment system based on wearable devices and explores its application, and verifies the feasibility of replacing research-grade devices with popular civilian devices. On the one hand, it provides an inexpensive and objective method for physical education assessment, and on the other hand, it provides a theoretical basis and practical experience to promote the integration of modern technology and curriculum evaluation. In this paper, a volleyball training model is established based on the flexible strain sensor network, diagnosis of volleyball players’ spiking action by link, finding weak links, and improving them through training. Through the sublink diagnosis of special movements, the characteristics of test movements and diagnostic indicators of volleyball players were analyzed, and the theoretical connotation of special physical training for volleyball players was further improved, and at the same time, it provided reference and reference for special physical training of other sports. The improvement experiment on the weak links of volleyball players’ special movements expands the movement method system of volleyball players’ special physical training.


## 2. Related Works

Yamada and Hayamizu proposed the use of graphical structures to represent human postures, and this model has been applied well in facial structure tests. Later, Yamada and Hayamizu introduced a heuristic local search technique into the graphical structure model and improved its use by optimization, but the biggest problem with this approach is that it requires a valid initial solution and it is difficult to achieve a globally optimal solution [6]. Statistical model, this parameter learning and matching method achieve better usage, and the pose is represented by components arranged in a deformable configuration, modeled individually for different components, and the connection between pairs of components represents the deformable configuration; this model supports the qualitative description of visual appearance. Motion information acquisition systems generally include sensors, data acquisition devices, data transmission devices, and data processing devices. Based on the characteristics of the selected sensors, the motion information acquisition systems can be classified into imaging-based acquisition systems, electromagnetic acquisition systems, and other types of acquisition systems [7]. The imaging acquisition system is based on the principle of computer vision and uses one or more cameras to capture motion images at high speed, simultaneously and continuously, and obtains motion information through postprocessing of the images. The advantages of imaging acquisition systems are large coverage of the working area, no cable, mechanical device limitations, easy to use, and can meet the needs of most high-speed motion measurements; the disadvantage is that the system is expensive; although it can capture real-time motion information, to achieve target identification and tracking, spatial coordinate calculation and other information postprocessing workload are large [8]. There are certain requirements for site lighting and reflection, image calibration. It is tedious, especially when the motion is complex and the target point is obscured; it is easy to generate error information and requires manual intervention and professional image analysis tools.The main method of preparing flexible strain sensors is to introduce sensing substances such as low-dimensional carbon materials (carbon nanotubes, carbon black, graphene, etc.), metal nanomaterials (copper nanowires, silver nanoparticles, etc.), and conductive polymers (PEDOT.PSS, polyacetylene, etc.) into flexible substrates such as polydimethylsiloxane (PDMS), polyurethane (PU), polyethylene terephthalate (PET), paper, and fabric. Ecoflex silicone, polyurethane (PU), polyethylene terephthalate (PET), paper, and fabric, so that flexible strain sensors have both the excellent electrical conductivity of sensing substances and the mechanical flexibility of flexible substrates. Many flexible strain sensors cannot achieve a good balance between sensitivity and stretchability [9]. For example, the flexible strain sensor prepared by Wang et al. used carbon nanotubes as the conductive substance and polyacrylamide (HAPAAm) hydrogel as the elastomer showed considerable stretchability (about 3000%); however, the GF value was only 4.32, and such a low sensitivity was difficult to use for measuring weak signals, thus reducing its practical application potential [10]. Then, Bae et al. used the two-dimensional material Mxene as the sensing material and the thermoplastic elastomer TPU film as the flexible carrier to make use of the crack sensing mechanism so that the GF value of this flexible strain sensor reached 75,000, but the maximum stretching range was only 5%, and although it could detect pulse and vocal micro signals, it was difficult to be used for larger strain detection and real-time dynamic monitoring such as joint motion [11].At present, the vast majority of the research on human pose estimation is based on graphical structural models, where the human appearance model and the human structure model have a very important role, and they largely determine the algorithm execution effect [12]. The more complex the algorithm is, the higher the accuracy of the estimation is, and the more complex the algorithm is, the more accurate the estimation can be. However, precisely due to the complexity of the algorithm and the large retrieval range, the speed of the whole operation is not ideal. Compared with a priori cognitive and artificial features, convolutional neural networks are significantly more expressive and closer to the real structure of the human body [13]. For the deep learning model, its nonlinear mapping ability is stronger, and it can easily realize the mapping from image features to human pose, so the use effect will be more ideal. At this stage, deep learning is more widely used in human pose estimation, although the current research has accumulated many results; from the perspective of practical applications, these studies still have much room for improvement in terms of generality, accuracy, speed, and robustness, both in terms of human motion and environmental factors, which can affect the evaluation results to varying degrees.


## 3. Construction of Volleyball Training Model Based on Flexible Strain Sensing Network

### 3.1. Flexible Strain Sensing Network Model Design

Strain sensors are devices that convert mechanical deformations into electrical signals. Depending on the response mechanism to external strain, strain sensors can be classified into capacitive strain sensors, resistive strain sensors, and other strain sensors based on fiber Bragg grating (FBG), Raman shift, liquid metal, piezoelectric effect, etc. In contrast, the other types of strain sensors mentioned above have their drawbacks such as flimsy test equipment, low resolution, and poor dynamic performance, which limit their application in the field of flexible strain sensing [14]. Capacitive and resistive strain sensors require relatively simple test systems and can withstand a larger range of deformations; thus, the main types of flexible strain sensors are capacitive and resistive.Flexible capacitive strain sensors generally consist of a sandwich structure with a pair of stretchable electrodes sandwiching a dielectric insulating layer. When it is stretched, the response of the capacitive sensor is manifested by the deformation of the area deformation and the thinning of the dielectric layer thickness, which makes the capacitance increase. Taking a parallel-plate capacitor as an example, the initial length of the parallel plate isl0, the width is w0, the thickness of the insulation layer is d0, and the initial capacitance is as in equation (1), where ς0 and ςr denote the dielectric constant of the vacuum and the relative dielectric constant of the dielectric, respectively.
(1)c0=ς0ςr×l0w0d0.The anisotropy of the internal structure of the flexible capacitive strain sensor determines the anisotropy of its mechanical properties. Ultrafast carbonization of UCMFT makes the anisotropy more obvious, and the electromechanical response in different directions varies when experiencing tensile strain. When stress is applied along the parallel fiber direction, the fiber folds caused by the wrinkling process first experience stretching and are flattened, and the overlapping areas between the fibers gradually decrease until they are completely separated or even broken, and discontinuous areas appear in the conductive fiber network, leading to more tortuous current flow paths and higher sensor resistance [15]. The fast take-off speed allows athletes to exchange time for space in the online competition to compete for favorable opportunities. Before the opponent has formed an effective blocking defense, it is better to strike first. Exchange space for time, use your better height to exchange for airborne time, when the opponent’s blocking system is at a disadvantage, or lob the ball or smash the ball, beat the opponent’s strength, and overcome the enemy to win.The situation is different when stretched along the vertical fiber direction, the stress makes the already loosely connected carbon fiber molecular chains more easily separated from each other, resulting in a more discontinuous conductive path than the former, so the resistance appears to rise even more significantly. When the strain is below 40%, there is little difference in the resistance change in both directions, while above 40% strain, there is a huge difference in the resistance change. Based on this result, we can speculate that in both directions, when the sensor strain is less than 40%, although the separation between fibers has started, the separation is mild and the entire fiber network remains well maintained, while after 40% strain, the same magnitude of stress makes it easier to separate between fiber molecular chains than to break them.(2)S=L2πrd2.We equate the “core-sheath” structure of the sensing yarn to a cylindrical conductor and calculate its equivalent conductivity. The conductive polyacrylonitrile strands as sheath yarn form a conductive path on the surface of the sensing yarn with a surface spiral structure, which gives the sensing yarn conductive properties and shows good conductivity. As shown in Figure1, the conductivity of the sensing yarn decreases with increasing twist number in the range of 20-50 twists, with the highest conductivity of 14.8 S/m for the 20 twist/10 cm size and the lowest conductivity of 9.4 S/m for the 50 twist/10 cm size. This is due to the increase in twist number and the actual winding length per unit length of the sensing yarn. When the number of twists reaches 60 (overtwisting state), the conductivity of the sensing yarn suddenly increases to 14 S/m. This is because the pitch of the coils formed by the outer wrapped sheath yarn is 0, and the coils are in contact with each other, forming a new conductive path in the axial direction of the sensing yarn, which reduces the equivalent resistance and increases the conductivity.Figure 1
Comparison of results of conductivity of sensing yarn with different specifications.To visually characterize the conductivity of the sensing yarn, a sensing yarn with a natural length of 5.8 cm and a gauge of 50 twist/10 cm was connected to an LED lamp and a power supply to form a series circuit. When the switch was closed, the LED light was successfully lit, indicating the good electrical conductivity of the sensing yarn. When the sensing yarn was stretched to 9.3 cm (60% elongation), the brightness of the LED light was significantly increased, which was due to the gradual decrease of the resistance of the sensing yarn during the stretching process. The experimental data of the wrist joint motion proved that there is a certain error in the repeatability accuracy of the constructed experimental platform. The main source of the error lies in the error of binocular vision for the localization of spatial targets, and there will be deviations for the localization of each spatial point leading to errors in the converted spatial angle.(3)tanθ=Ki−KjKi+Kj.The sampling circuit of the flexible sensor is based on the principle that the magnitude voltage of the equivalent capacitance of the flexible sensor generates a certain amount of correspondence to the magnitude and frequency of the excitation signal and detects the magnitude of the capacitance. To improve the response speed of the flexible strain sensing network to human movement and the detection accuracy of the system, the sampling frequency of the wrist joint angle is set to 200 Hz. Since the signal processing circuit needs processing time for the sensing data, the frequency of the excitation signal is set to 600 Hz. As shown in Figure2, the flexible sensor is deformed by the wrist joint, the capacitance value of the flexible sensor changes as a variable capacitor. The response signal is transformed into a voltage peak-to-peak value by the analog-to-digital conversion module and then filtered by the arithmetic average filtering method to eliminate noise effects. The upper PC acquires the stabilized values calculated by averaging the filtering algorithm via multi-channel serial communication.Figure 2
Flexible strain sensor signal sampling and processing flow.Based on the design and completion of the experimental platform for flexible wearable wrist joint motion measurement, the measurement repeatability accuracy of the built experimental platform was tested. The reason for choosing the repeatability measurement accuracy as the evaluation index is the lack of an accurate definition of wrist joint motion angle, and the different chosen wrist joint axis points will lead to different descriptions of wrist joint motion between different systems. Therefore, it is a common practice for visual motion capture systems to examine the repeatability error of the system and use it as a criterion to evaluate the goodness of the motion capture system. For the widely accepted commercial motion capture systems, the same repetitive error is used for evaluation rather than absolute error [16]. To check the repeatability of the vision system, this chapter designs a mechanism with a mechanical limit function by designing a combination of chuck and spring pin structure so that the human wrist can only reach five specific working positions. A photochemical resin 3D printer printed the body of the designed mechanical structure, and it was verified that the structure obtained by 3D printing could meet the experimental requirements for stiffness and accuracy. The experiment was repeated several times to compare the motion angles calculated by the vision stage each time it reached the same position. From this, the repeatability of the positioning error of the system can be calculated. After calculation, the average repeatability error of the vision system for human wrist motion measurement was found to be 0.43 degrees. This demonstrates that the built experimental platform has a certain accuracy in the acquisition and calculation of wrist motion and can be used to provide true values of motion for the establishment of a multisensing coupling model study.


### 3.2. Volleyball Training Model Construction

Time-phase division is an important part of the study of technical movements. Volleyball players’ up-step bucket jumping technique is composed of four links: running, jumping, vacating, and landing, and the two links that play a decisive role in the effect of jumping are running and jumping. The jumping technology link can be divided into jumping leg landing, cushioning, and stirrups in three stages. For the study of volleyball bucket jumping action, the jumping phase can be simply divided into single-leg jumping and double-leg jumping based on the external characteristics of the jumping action, but this is not conducive to the technical analysis of the action and reveal the special characteristics of jumping more objectively [17]. Therefore, this paper adopts the method of combining the form of lower limb muscle work with the change in the speed of the body’s center of gravity over time to determine the temporal division of the jumping action. The jumping phase of the bucket is from the moment of landing on the right foot to the moment of jumping off the ground with both feet. To determine the structure of the action and the convenience of the temporal division, this paper first determines the characteristic picture of the jumping phase, that is, the critical point of different action phases, which can characterize the basic mechanical characteristics of each action phase and action quality. We determine the characteristic picture of the jumping phase according to the sequence of volleyball bucket jumping action as follows: “right foot on the ground,” “left foot on the ground,” “the lowest center of gravity” (the position of the body’s center of gravity) (the lowest point of the body’s center of gravity, coinciding with the moment when the body’s center of gravity, is falling at 0), and “both feet off the ground.”At present, strength training has received unprecedented attention. It is recognized that improving the nerve-muscle function of the system, enhancing the force generated by muscle contraction is the most direct and effective way to improve athletic performance. The main problems existing in the special strength training of volleyball players are as follows:(1)
The single mode of overreliance on barbells appears in lower body strength training(2)
The movements of strength exercises are unreasonable. The strength training of volleyball players should be combined with the posture, speed, and muscle contraction form of volleyball technical movements. The strengthening of any group of muscle strength must emphasize the speed and speed of completion(3)
The training load arrangement is unscientific. Although some coaches have requirements and indicators for weight, number of times, and the number of sets in the training plan, they rarely put forward specific requirements for the continuity of movements and do not consider movements and movements, groups, and group interval between(4)
Most trainers do not combine strength training with other physical training sciencesThe scholars conducted a factor analysis of various major parameters affecting the structure of the snapping action, and the factor with the largest contribution was the temporal structure factor with 22. 84%, which included the temporal parameters of the stomp phase. This indicates that the temporal characteristics of the jumping phase are an important factor affecting the action effect. In this paper, we chose “strive for the maximum height” as the criterion for evaluating the effect of jumping, and to determine the characteristic parameters affecting the height of the human center of gravity (H, the same below) among many temporal parameters, we analyzed the relationship between each temporal parameter and H.According to biomechanical principles, the short jumping time, the lower limbs from the buffer to the stirrup of the conversion speed, can avoid muscle relaxation phenomenon, improve the lower limb extensor muscle group stored up the degree of reuse of elastic deformation energy, is conducive to play muscle contraction ability, enhance the jumping effect. However, the jumping time is only a reflection of how fast the jumping stirrup is completed, but not of the quality of the completed action, so it cannot be emphasized unilaterally to shorten the jumping time. In the present study, a moderate negative correlation was found betweenT and H (R=−0.71,P<0.05). The reason for the significant difference between this test result and Dowling’s study may be that Bowling’s actual results were obtained in the in situ vertical jump, while the up-step snap jumping action is like the running vertical jumping action. From the cushioning phase of the jump, there is a highly negative correlation between the cushion time T1 and H (R=−0.70,P<0.01), which indicates that the longer the cushion time is, the more unfavorable the jump for maximum height. In terms of the jumping stretching phase, T2 is the jumping stretching time, which has a moderate negative correlation with H (R=−0.61,P<0.05), indicating that the jumping stretching time should not be too long either.
(4)fx=sgn∑i,j=1aikxi,xj−b2.Accurate analysis of human motion is an important prerequisite for building a simple exoskeleton simulation acquisition platform [18]. The human body usually maintains a certain regularity and periodicity when it performs walking movements, so when identifying and detecting human walking movements, it is necessary to first study the movement principles of the human body during walking. Walking, as one of the most basic movement patterns, requires the participation of multiple nervous systems, bones, and skeletal muscles, in which the bones play the role of supporting the human body and the skeletal muscles are contracted or stretched under the constant stimulation of the participating nerve systems to drive the bones around the relevant joints for circular motion. Only by understanding the motion characteristics of each joint in the human body and the laws of motion information can we construct a simple exoskeleton simulation acquisition platform based on structural principles. Flexible sensors have stretchability and bendability, can fit the human body or complex surface monitoring, and have great application prospects in the fields of human motion monitoring, medical diagnosis, human-computer interaction, and environmental monitoring. To obtain the motion information of the human body, it is necessary to select the data acquisition site and establish the spatial coordinate system. Considering that the human body has a large amplitude change at rest and in motion, it is possible to obtain key information based on the characteristics they have and then use appropriate algorithms to process the output device information. To ensure that the flexible sensor is attached to the surface of the human body with certain tensile tension during the running movement and stretched with different movements, the fitted sports pants suit is chosen as the carrier for monitoring so that the sensor fits the human body to a greater extent, and the placement of the flexible strain sensor in this paper is shown in Figure 3.Figure 3
Distribution of information collection system equipment.There are many types of artificial neural networks, and different forms have been developed. According to the connection of neurons, artificial neural networks can be divided into forward neural networks and feedback neural networks; according to the way of learning, artificial neural networks can be divided into tutored neural networks and tutorial neural networks; according to the realized functions, artificial neural networks can be divided into fitting (regression) neural networks, classification neural networks, etc. The full name of the BP (back propagation) algorithm is called error back propagation algorithm and the basic idea of its algorithm. In the feedforward network, the input signal is input by the input layer, calculated by the hidden layer, and output by the output layer; the output value is compared with the labeled value, and if there is any error, the error is propagated backward from the output layer to the input layer, and in the forward propagation process, the gradient descent algorithm is used to adjust the neuron weights [19]. However, BP networks also have some disadvantages, such as easy to fall into the local minima and not finding the global optimum, the convergence speed of training is slow, most of the training nodes are selected and tuned based on the empirical determination, and the old samples are easily forgotten during training. A genetic algorithm (GA) is a method to find the global optimal solution based on the Darwinian theory of biological evolution and the evolutionary theory of genetics. Flexible tactile sensors experience wear and tear during use, and the use of self-healing materials can significantly extend the life of flexible tactile sensors. The method can directly compute structural objects, thus avoiding some defects of the derivation and signal continuity, ensuring the ability to search for the global optimal solution without the need for the trainer to use custom rules for the network to guide the network to learn and can adjust the direction of the solution autonomously. In summary, the characteristics of the BP algorithm in searching for the optimal solution can be combined with the characteristics of the genetic algorithm of better global, which can well serve the defects of the local optimal solution of the BP algorithm and optimize the initial weights and thresholds of BP neural network, which can improve the stability of BP neural network and shorten the occupation time.
(5)St=ai≥0,ai≤L≤1,∑i=1ai=1.In the GA-BP neural network, the initial learning rate of the basic BP neural network is set to 0.1, the number of iterations is set to 1000, the number of neurons in the input layer is set to 7, the number of neurons in the hidden layer is set to 24, and the number of neurons in the output layer is set to 1. The initial parameters of the network are then optimized using the GA algorithm toolbox from the University of North Carolina. In the extreme learning machine method used in this paper, the number of hidden nodes has a large impact on the accuracy of the model. Therefore, the number of nodes in the hidden layer and the accuracy of the model are firstly verified, and the results are shown in Figure4. The number of hidden nodes is set to 500 to ensure the accuracy of the model.Figure 4
The relationship between the number of nodes in the hidden layer and the accuracy rate.


## 3.1. Flexible Strain Sensing Network Model Design

Strain sensors are devices that convert mechanical deformations into electrical signals. Depending on the response mechanism to external strain, strain sensors can be classified into capacitive strain sensors, resistive strain sensors, and other strain sensors based on fiber Bragg grating (FBG), Raman shift, liquid metal, piezoelectric effect, etc. In contrast, the other types of strain sensors mentioned above have their drawbacks such as flimsy test equipment, low resolution, and poor dynamic performance, which limit their application in the field of flexible strain sensing [14]. Capacitive and resistive strain sensors require relatively simple test systems and can withstand a larger range of deformations; thus, the main types of flexible strain sensors are capacitive and resistive.Flexible capacitive strain sensors generally consist of a sandwich structure with a pair of stretchable electrodes sandwiching a dielectric insulating layer. When it is stretched, the response of the capacitive sensor is manifested by the deformation of the area deformation and the thinning of the dielectric layer thickness, which makes the capacitance increase. Taking a parallel-plate capacitor as an example, the initial length of the parallel plate isl0, the width is w0, the thickness of the insulation layer is d0, and the initial capacitance is as in equation (1), where ς0 and ςr denote the dielectric constant of the vacuum and the relative dielectric constant of the dielectric, respectively.
(1)c0=ς0ςr×l0w0d0.The anisotropy of the internal structure of the flexible capacitive strain sensor determines the anisotropy of its mechanical properties. Ultrafast carbonization of UCMFT makes the anisotropy more obvious, and the electromechanical response in different directions varies when experiencing tensile strain. When stress is applied along the parallel fiber direction, the fiber folds caused by the wrinkling process first experience stretching and are flattened, and the overlapping areas between the fibers gradually decrease until they are completely separated or even broken, and discontinuous areas appear in the conductive fiber network, leading to more tortuous current flow paths and higher sensor resistance [15]. The fast take-off speed allows athletes to exchange time for space in the online competition to compete for favorable opportunities. Before the opponent has formed an effective blocking defense, it is better to strike first. Exchange space for time, use your better height to exchange for airborne time, when the opponent’s blocking system is at a disadvantage, or lob the ball or smash the ball, beat the opponent’s strength, and overcome the enemy to win.The situation is different when stretched along the vertical fiber direction, the stress makes the already loosely connected carbon fiber molecular chains more easily separated from each other, resulting in a more discontinuous conductive path than the former, so the resistance appears to rise even more significantly. When the strain is below 40%, there is little difference in the resistance change in both directions, while above 40% strain, there is a huge difference in the resistance change. Based on this result, we can speculate that in both directions, when the sensor strain is less than 40%, although the separation between fibers has started, the separation is mild and the entire fiber network remains well maintained, while after 40% strain, the same magnitude of stress makes it easier to separate between fiber molecular chains than to break them.(2)S=L2πrd2.We equate the “core-sheath” structure of the sensing yarn to a cylindrical conductor and calculate its equivalent conductivity. The conductive polyacrylonitrile strands as sheath yarn form a conductive path on the surface of the sensing yarn with a surface spiral structure, which gives the sensing yarn conductive properties and shows good conductivity. As shown in Figure1, the conductivity of the sensing yarn decreases with increasing twist number in the range of 20-50 twists, with the highest conductivity of 14.8 S/m for the 20 twist/10 cm size and the lowest conductivity of 9.4 S/m for the 50 twist/10 cm size. This is due to the increase in twist number and the actual winding length per unit length of the sensing yarn. When the number of twists reaches 60 (overtwisting state), the conductivity of the sensing yarn suddenly increases to 14 S/m. This is because the pitch of the coils formed by the outer wrapped sheath yarn is 0, and the coils are in contact with each other, forming a new conductive path in the axial direction of the sensing yarn, which reduces the equivalent resistance and increases the conductivity.Figure 1
Comparison of results of conductivity of sensing yarn with different specifications.To visually characterize the conductivity of the sensing yarn, a sensing yarn with a natural length of 5.8 cm and a gauge of 50 twist/10 cm was connected to an LED lamp and a power supply to form a series circuit. When the switch was closed, the LED light was successfully lit, indicating the good electrical conductivity of the sensing yarn. When the sensing yarn was stretched to 9.3 cm (60% elongation), the brightness of the LED light was significantly increased, which was due to the gradual decrease of the resistance of the sensing yarn during the stretching process. The experimental data of the wrist joint motion proved that there is a certain error in the repeatability accuracy of the constructed experimental platform. The main source of the error lies in the error of binocular vision for the localization of spatial targets, and there will be deviations for the localization of each spatial point leading to errors in the converted spatial angle.(3)tanθ=Ki−KjKi+Kj.The sampling circuit of the flexible sensor is based on the principle that the magnitude voltage of the equivalent capacitance of the flexible sensor generates a certain amount of correspondence to the magnitude and frequency of the excitation signal and detects the magnitude of the capacitance. To improve the response speed of the flexible strain sensing network to human movement and the detection accuracy of the system, the sampling frequency of the wrist joint angle is set to 200 Hz. Since the signal processing circuit needs processing time for the sensing data, the frequency of the excitation signal is set to 600 Hz. As shown in Figure2, the flexible sensor is deformed by the wrist joint, the capacitance value of the flexible sensor changes as a variable capacitor. The response signal is transformed into a voltage peak-to-peak value by the analog-to-digital conversion module and then filtered by the arithmetic average filtering method to eliminate noise effects. The upper PC acquires the stabilized values calculated by averaging the filtering algorithm via multi-channel serial communication.Figure 2
Flexible strain sensor signal sampling and processing flow.Based on the design and completion of the experimental platform for flexible wearable wrist joint motion measurement, the measurement repeatability accuracy of the built experimental platform was tested. The reason for choosing the repeatability measurement accuracy as the evaluation index is the lack of an accurate definition of wrist joint motion angle, and the different chosen wrist joint axis points will lead to different descriptions of wrist joint motion between different systems. Therefore, it is a common practice for visual motion capture systems to examine the repeatability error of the system and use it as a criterion to evaluate the goodness of the motion capture system. For the widely accepted commercial motion capture systems, the same repetitive error is used for evaluation rather than absolute error [16]. To check the repeatability of the vision system, this chapter designs a mechanism with a mechanical limit function by designing a combination of chuck and spring pin structure so that the human wrist can only reach five specific working positions. A photochemical resin 3D printer printed the body of the designed mechanical structure, and it was verified that the structure obtained by 3D printing could meet the experimental requirements for stiffness and accuracy. The experiment was repeated several times to compare the motion angles calculated by the vision stage each time it reached the same position. From this, the repeatability of the positioning error of the system can be calculated. After calculation, the average repeatability error of the vision system for human wrist motion measurement was found to be 0.43 degrees. This demonstrates that the built experimental platform has a certain accuracy in the acquisition and calculation of wrist motion and can be used to provide true values of motion for the establishment of a multisensing coupling model study.


## 3.2. Volleyball Training Model Construction

Time-phase division is an important part of the study of technical movements. Volleyball players’ up-step bucket jumping technique is composed of four links: running, jumping, vacating, and landing, and the two links that play a decisive role in the effect of jumping are running and jumping. The jumping technology link can be divided into jumping leg landing, cushioning, and stirrups in three stages. For the study of volleyball bucket jumping action, the jumping phase can be simply divided into single-leg jumping and double-leg jumping based on the external characteristics of the jumping action, but this is not conducive to the technical analysis of the action and reveal the special characteristics of jumping more objectively [17]. Therefore, this paper adopts the method of combining the form of lower limb muscle work with the change in the speed of the body’s center of gravity over time to determine the temporal division of the jumping action. The jumping phase of the bucket is from the moment of landing on the right foot to the moment of jumping off the ground with both feet. To determine the structure of the action and the convenience of the temporal division, this paper first determines the characteristic picture of the jumping phase, that is, the critical point of different action phases, which can characterize the basic mechanical characteristics of each action phase and action quality. We determine the characteristic picture of the jumping phase according to the sequence of volleyball bucket jumping action as follows: “right foot on the ground,” “left foot on the ground,” “the lowest center of gravity” (the position of the body’s center of gravity) (the lowest point of the body’s center of gravity, coinciding with the moment when the body’s center of gravity, is falling at 0), and “both feet off the ground.”At present, strength training has received unprecedented attention. It is recognized that improving the nerve-muscle function of the system, enhancing the force generated by muscle contraction is the most direct and effective way to improve athletic performance. The main problems existing in the special strength training of volleyball players are as follows:(1)
The single mode of overreliance on barbells appears in lower body strength training(2)
The movements of strength exercises are unreasonable. The strength training of volleyball players should be combined with the posture, speed, and muscle contraction form of volleyball technical movements. The strengthening of any group of muscle strength must emphasize the speed and speed of completion(3)
The training load arrangement is unscientific. Although some coaches have requirements and indicators for weight, number of times, and the number of sets in the training plan, they rarely put forward specific requirements for the continuity of movements and do not consider movements and movements, groups, and group interval between(4)
Most trainers do not combine strength training with other physical training sciencesThe scholars conducted a factor analysis of various major parameters affecting the structure of the snapping action, and the factor with the largest contribution was the temporal structure factor with 22. 84%, which included the temporal parameters of the stomp phase. This indicates that the temporal characteristics of the jumping phase are an important factor affecting the action effect. In this paper, we chose “strive for the maximum height” as the criterion for evaluating the effect of jumping, and to determine the characteristic parameters affecting the height of the human center of gravity (H, the same below) among many temporal parameters, we analyzed the relationship between each temporal parameter and H.According to biomechanical principles, the short jumping time, the lower limbs from the buffer to the stirrup of the conversion speed, can avoid muscle relaxation phenomenon, improve the lower limb extensor muscle group stored up the degree of reuse of elastic deformation energy, is conducive to play muscle contraction ability, enhance the jumping effect. However, the jumping time is only a reflection of how fast the jumping stirrup is completed, but not of the quality of the completed action, so it cannot be emphasized unilaterally to shorten the jumping time. In the present study, a moderate negative correlation was found betweenT and H (R=−0.71,P<0.05). The reason for the significant difference between this test result and Dowling’s study may be that Bowling’s actual results were obtained in the in situ vertical jump, while the up-step snap jumping action is like the running vertical jumping action. From the cushioning phase of the jump, there is a highly negative correlation between the cushion time T1 and H (R=−0.70,P<0.01), which indicates that the longer the cushion time is, the more unfavorable the jump for maximum height. In terms of the jumping stretching phase, T2 is the jumping stretching time, which has a moderate negative correlation with H (R=−0.61,P<0.05), indicating that the jumping stretching time should not be too long either.
(4)fx=sgn∑i,j=1aikxi,xj−b2.Accurate analysis of human motion is an important prerequisite for building a simple exoskeleton simulation acquisition platform [18]. The human body usually maintains a certain regularity and periodicity when it performs walking movements, so when identifying and detecting human walking movements, it is necessary to first study the movement principles of the human body during walking. Walking, as one of the most basic movement patterns, requires the participation of multiple nervous systems, bones, and skeletal muscles, in which the bones play the role of supporting the human body and the skeletal muscles are contracted or stretched under the constant stimulation of the participating nerve systems to drive the bones around the relevant joints for circular motion. Only by understanding the motion characteristics of each joint in the human body and the laws of motion information can we construct a simple exoskeleton simulation acquisition platform based on structural principles. Flexible sensors have stretchability and bendability, can fit the human body or complex surface monitoring, and have great application prospects in the fields of human motion monitoring, medical diagnosis, human-computer interaction, and environmental monitoring. To obtain the motion information of the human body, it is necessary to select the data acquisition site and establish the spatial coordinate system. Considering that the human body has a large amplitude change at rest and in motion, it is possible to obtain key information based on the characteristics they have and then use appropriate algorithms to process the output device information. To ensure that the flexible sensor is attached to the surface of the human body with certain tensile tension during the running movement and stretched with different movements, the fitted sports pants suit is chosen as the carrier for monitoring so that the sensor fits the human body to a greater extent, and the placement of the flexible strain sensor in this paper is shown in Figure 3.Figure 3
Distribution of information collection system equipment.There are many types of artificial neural networks, and different forms have been developed. According to the connection of neurons, artificial neural networks can be divided into forward neural networks and feedback neural networks; according to the way of learning, artificial neural networks can be divided into tutored neural networks and tutorial neural networks; according to the realized functions, artificial neural networks can be divided into fitting (regression) neural networks, classification neural networks, etc. The full name of the BP (back propagation) algorithm is called error back propagation algorithm and the basic idea of its algorithm. In the feedforward network, the input signal is input by the input layer, calculated by the hidden layer, and output by the output layer; the output value is compared with the labeled value, and if there is any error, the error is propagated backward from the output layer to the input layer, and in the forward propagation process, the gradient descent algorithm is used to adjust the neuron weights [19]. However, BP networks also have some disadvantages, such as easy to fall into the local minima and not finding the global optimum, the convergence speed of training is slow, most of the training nodes are selected and tuned based on the empirical determination, and the old samples are easily forgotten during training. A genetic algorithm (GA) is a method to find the global optimal solution based on the Darwinian theory of biological evolution and the evolutionary theory of genetics. Flexible tactile sensors experience wear and tear during use, and the use of self-healing materials can significantly extend the life of flexible tactile sensors. The method can directly compute structural objects, thus avoiding some defects of the derivation and signal continuity, ensuring the ability to search for the global optimal solution without the need for the trainer to use custom rules for the network to guide the network to learn and can adjust the direction of the solution autonomously. In summary, the characteristics of the BP algorithm in searching for the optimal solution can be combined with the characteristics of the genetic algorithm of better global, which can well serve the defects of the local optimal solution of the BP algorithm and optimize the initial weights and thresholds of BP neural network, which can improve the stability of BP neural network and shorten the occupation time.
(5)St=ai≥0,ai≤L≤1,∑i=1ai=1.In the GA-BP neural network, the initial learning rate of the basic BP neural network is set to 0.1, the number of iterations is set to 1000, the number of neurons in the input layer is set to 7, the number of neurons in the hidden layer is set to 24, and the number of neurons in the output layer is set to 1. The initial parameters of the network are then optimized using the GA algorithm toolbox from the University of North Carolina. In the extreme learning machine method used in this paper, the number of hidden nodes has a large impact on the accuracy of the model. Therefore, the number of nodes in the hidden layer and the accuracy of the model are firstly verified, and the results are shown in Figure4. The number of hidden nodes is set to 500 to ensure the accuracy of the model.Figure 4
The relationship between the number of nodes in the hidden layer and the accuracy rate.


## 4. Analysis of Results

### 4.1. Flexible Strain Sensing Network Model Performance Results

The digital scatter pattern has higher controllability by selecting a specific gray ratio and distribution randomness by the program so that a randomly distributed scatter pattern can be obtained compared to the method of spraying on the garment carrier with a jet gun or attaching pigments to the flexible wearable garment carrier. The pattern obtained by this method can easily satisfy the randomness and anisotropy of the scattered spots, and the size of the scattered spots can be kept consistent, thus achieving higher accuracy [20]. The quality of the scatter pattern determines the error of the system measurement. According to Pan and Palanca, a good scatter pattern should have (1) high contrast, (2) sufficient randomness and anisotropy, and (3) scatter spot size and the need to match the resolution of the camera. The marker points attached to the human body are circular, and the circular pattern changes in the image during wrist movement, making it difficult to solve its geometric center. In contrast, commercial motion capture systems such as the Phase Space system use active light sources at the joints, which are tracked by the vision system in conjunction with the human motion model, resulting in lower errors. The designed scatter pattern is generated by the program, through which the size of the spots in the pattern, as well as the gray ratio, can be adjusted to form a digital scatter pattern. Since the digital scatter pattern has higher controllability, the method can easily satisfy the randomness and anisotropy of the scatter and the size of the scatter spots can be kept consistent compared to other methods, thus achieving higher accuracy. The digital-analog scatter pattern is generated according to the corresponding specification, and the quality of the pattern still needs to be checked. The scatter pattern is made to produce an actual displacement of 7 pixels, and the image correlation algorithm is run to compare the displacement derived from the algorithm with the actual displacement to obtain the result shown in Figure 5.Figure 5
Matching results with the original scatter using the image correlation algorithm.In the acquisition and transmission process, due to the influence of external environmental interference or the sensor itself, there will inevitably be some noise mixed in the middle, and the noise will affect the detection and identification work for the target signal motion information, greatly reducing its performance; therefore, before the motion signal analysis and feature extraction, this paper first denoises the signal processing. The gyroscope can be used to know the attitude (cross-roll angle, pitch angle, and heading angle) and angular velocity of the moving carrier. A gyroscope is a gyroscope inside, and its axis is always parallel to the initial direction due to the gyroscopic effect so that the direction and angle of rotation can be calculated from the deviation from the initial direction [21]. The dynamic response of the accelerometer is slow and not adapted to track dynamic angular motion, and if a fast response is expected, it will cause a large noise; the gyroscope will produce drift errors due to temperature changes and friction, unstable moments, etc. For the accelerometer in a longer period, the measured value is correct, while in a shorter period due to the presence of signal noise, there will be errors; the gyroscope in a shorter period is more accurate while a longer period will have errors due to drift. Therefore, the two (mutually adjusted) fusion is needed to ensure the accuracy of the motion information measurement, the fusion algorithm has a Kalman filter, particle filter, complementary filter algorithm, etc., and this section selected the Kalman filter algorithm for fusion.
(6)habx=a2hx+b.The data taken in the previous paper were organized and modeling experiments were performed on them using different methods to classify the collected data and divide the data set into a training set and test set, label the output labels corresponding to each data, and no data overlap between the training set data and the test set data when the division was performed. The normalization of data is to scale the data to fall into a specific0,1 interval. The benefit is that it can improve the convergence speed of the model with the accuracy of the model and prevent the gradient explosion of the model. To train the model in MATLAB, the divided training set data are input to the model for training, and the training is completed when the model converges. The test set data are tested and the correctness of each model is calculated based on the model output, which is compared with the test set labels. The obtained experimental results of motion pattern classification are shown in Figure 6.Figure 6
GA-BP algorithm recognition results.The GA-BP neural network is optimized based on the BP algorithm, but it still cannot guarantee the global performance and robustness of the algorithm. The accuracy of the extreme learning machine is improved compared to the first two methods, but the number of neurons in the hidden layer of the network structure leads to a longer recognition time, which does not guarantee real-time computation. The support vector machine, on the other hand, has improved both the accuracy and the time used for recognition, and the average recognition rate has reached 96.3%, which is the required accuracy rate in practical engineering. Like these functions of human skin, tactile sensors can provide tactile feedback of objects to people with tactile impairments through the integration of various sensors, helping people perceive information such as material properties, motion and relative position, and ambient temperature and humidity.


### 4.2. Analysis of Volleyball Training Model Applications

The movement of human limbs and trunks is controlled and coordinated by the nervous system through muscle activity to achieve and function. When the muscle moment is greater than the resistance moment, the muscle achieves centripetal contraction to produce tension, thus gaining the effectiveness of the activity. The most critical aspect of the muscle moment is the muscle force, which is related to the size of the physiological cross section of the muscle, to the frequency and duration of the stimulation issued by the nervous system to the muscle, and the size of the external load given to the muscle and the speed of the resulting muscle contraction [22]. Because of the common characteristics of muscle strength and electromyographic changes in the test subjects, to further analyze the relationship between the jumping action and the deep squatting up action, a player was randomly selected to compare and analyze the action of the best muscle strength play at different speeds with the upstroke jumping action of the player. The raw EMG changes of the six muscles of the lower limbs during the stirrup and extension phase had similar characteristics, and the comparative analysis of the percentage of work and integral EMG values of the six muscles of the test subjects by the time-domain analysis method showed the same change pattern [23]. The raw EMG, integrated EMG load percentage, and EMG-angle curve plots of the peak force value of one movement with the highest peak force value, as shown in Figure 7, were obtained from a randomly selected tester in different angular velocity tests by EMG synchronization with video, combined with joint angle notation analysis, in the order of 15 degrees/s, 25 degrees/s, and 35 degrees/s.Figure 7
15 degrees/s, 25 degrees/s, and 35 degrees/s isometric deep squat graph.As the angular velocity of the test system increased, the time to peak force decreased significantly and the peak force decreased significantly, but the position of the resistance arm movement to peak force remained between 20 and 22 degrees. The peak knee joint angle was measured by an arthroscope between 90 and 110 degrees. The EMG curves of the corresponding movements showed the amplitude of the relevant muscles also reached the maximum at the angle of peak force, which suggests that this angle may be the best force angle for volleyball players and is consistent with the results of the jumping movement [24].RFID positioning system requires the reader to provide the signal strength information of the received tag, but most RFID systems do not provide the signal strength information directly, the reader can only detect the power level of the received signal, and the distinguishable power level of RFID system using LANDMARK algorithm positioning is generally 8. The increase of the distinguishable power levelp of the reader makes the distance accuracy obtainable higher, which affects the positioning accuracy of the tag. The reader in the simulation increases the power level p from 8 to 12 to identify the signal power emitted by the tag. Simulations were performed using 110 reference tags, as shown in Figure 8. The simulation results show that the increase in power level increases the localization accuracy, which is the reason for the increase in distance recognition accuracy due to the increase in distinguishable power and level.Figure 8
Comparison of simulation result error.Peak force and an average peak force of lower extremity multijoint flexor and extensor muscle groups showed a significant positive correlation with special sports performance, which indicates that stronger lower extremity muscle groups of athletes are very beneficial to the height of the jumping center of gravity takeoff. This further illustrates the importance of coordinated development of lower limb joint muscle strength to improve the jumping effect of the upper step bucket [25]. Therefore, special strength training focuses on the lower extremity extensor muscle group’s ability to improve; at the same time, the lower extremity flexor muscle group strength training should not be ignored. The maximum power and average power of the lower limb muscle groups also showed a significant positive correlation with the performance, which indicates that the power of the lower limb flexor and extensor muscle groups plays a coordinating role in the athletes’ jumping action, which coincides with the actual action of the stirrup phase. Strengthening the strength of the posterior muscle groups and small muscle groups should not be ignored, which is not only related to the coordination of muscle force, better play the efficiency of muscle work, successful completion of technical movements, but also one of the important methods to prevent sports injuries. The strengthening of the strength of the extensor group is conducive to the improvement of the efficiency of the stirrups, and the strengthening of the strength of the flexor group is conducive to the shortening of the buffer time, therefore conducive to the improvement of the jumping speed.


## 4.1. Flexible Strain Sensing Network Model Performance Results

The digital scatter pattern has higher controllability by selecting a specific gray ratio and distribution randomness by the program so that a randomly distributed scatter pattern can be obtained compared to the method of spraying on the garment carrier with a jet gun or attaching pigments to the flexible wearable garment carrier. The pattern obtained by this method can easily satisfy the randomness and anisotropy of the scattered spots, and the size of the scattered spots can be kept consistent, thus achieving higher accuracy [20]. The quality of the scatter pattern determines the error of the system measurement. According to Pan and Palanca, a good scatter pattern should have (1) high contrast, (2) sufficient randomness and anisotropy, and (3) scatter spot size and the need to match the resolution of the camera. The marker points attached to the human body are circular, and the circular pattern changes in the image during wrist movement, making it difficult to solve its geometric center. In contrast, commercial motion capture systems such as the Phase Space system use active light sources at the joints, which are tracked by the vision system in conjunction with the human motion model, resulting in lower errors. The designed scatter pattern is generated by the program, through which the size of the spots in the pattern, as well as the gray ratio, can be adjusted to form a digital scatter pattern. Since the digital scatter pattern has higher controllability, the method can easily satisfy the randomness and anisotropy of the scatter and the size of the scatter spots can be kept consistent compared to other methods, thus achieving higher accuracy. The digital-analog scatter pattern is generated according to the corresponding specification, and the quality of the pattern still needs to be checked. The scatter pattern is made to produce an actual displacement of 7 pixels, and the image correlation algorithm is run to compare the displacement derived from the algorithm with the actual displacement to obtain the result shown in Figure 5.Figure 5
Matching results with the original scatter using the image correlation algorithm.In the acquisition and transmission process, due to the influence of external environmental interference or the sensor itself, there will inevitably be some noise mixed in the middle, and the noise will affect the detection and identification work for the target signal motion information, greatly reducing its performance; therefore, before the motion signal analysis and feature extraction, this paper first denoises the signal processing. The gyroscope can be used to know the attitude (cross-roll angle, pitch angle, and heading angle) and angular velocity of the moving carrier. A gyroscope is a gyroscope inside, and its axis is always parallel to the initial direction due to the gyroscopic effect so that the direction and angle of rotation can be calculated from the deviation from the initial direction [21]. The dynamic response of the accelerometer is slow and not adapted to track dynamic angular motion, and if a fast response is expected, it will cause a large noise; the gyroscope will produce drift errors due to temperature changes and friction, unstable moments, etc. For the accelerometer in a longer period, the measured value is correct, while in a shorter period due to the presence of signal noise, there will be errors; the gyroscope in a shorter period is more accurate while a longer period will have errors due to drift. Therefore, the two (mutually adjusted) fusion is needed to ensure the accuracy of the motion information measurement, the fusion algorithm has a Kalman filter, particle filter, complementary filter algorithm, etc., and this section selected the Kalman filter algorithm for fusion.
(6)habx=a2hx+b.The data taken in the previous paper were organized and modeling experiments were performed on them using different methods to classify the collected data and divide the data set into a training set and test set, label the output labels corresponding to each data, and no data overlap between the training set data and the test set data when the division was performed. The normalization of data is to scale the data to fall into a specific0,1 interval. The benefit is that it can improve the convergence speed of the model with the accuracy of the model and prevent the gradient explosion of the model. To train the model in MATLAB, the divided training set data are input to the model for training, and the training is completed when the model converges. The test set data are tested and the correctness of each model is calculated based on the model output, which is compared with the test set labels. The obtained experimental results of motion pattern classification are shown in Figure 6.Figure 6
GA-BP algorithm recognition results.The GA-BP neural network is optimized based on the BP algorithm, but it still cannot guarantee the global performance and robustness of the algorithm. The accuracy of the extreme learning machine is improved compared to the first two methods, but the number of neurons in the hidden layer of the network structure leads to a longer recognition time, which does not guarantee real-time computation. The support vector machine, on the other hand, has improved both the accuracy and the time used for recognition, and the average recognition rate has reached 96.3%, which is the required accuracy rate in practical engineering. Like these functions of human skin, tactile sensors can provide tactile feedback of objects to people with tactile impairments through the integration of various sensors, helping people perceive information such as material properties, motion and relative position, and ambient temperature and humidity.


## 4.2. Analysis of Volleyball Training Model Applications

The movement of human limbs and trunks is controlled and coordinated by the nervous system through muscle activity to achieve and function. When the muscle moment is greater than the resistance moment, the muscle achieves centripetal contraction to produce tension, thus gaining the effectiveness of the activity. The most critical aspect of the muscle moment is the muscle force, which is related to the size of the physiological cross section of the muscle, to the frequency and duration of the stimulation issued by the nervous system to the muscle, and the size of the external load given to the muscle and the speed of the resulting muscle contraction [22]. Because of the common characteristics of muscle strength and electromyographic changes in the test subjects, to further analyze the relationship between the jumping action and the deep squatting up action, a player was randomly selected to compare and analyze the action of the best muscle strength play at different speeds with the upstroke jumping action of the player. The raw EMG changes of the six muscles of the lower limbs during the stirrup and extension phase had similar characteristics, and the comparative analysis of the percentage of work and integral EMG values of the six muscles of the test subjects by the time-domain analysis method showed the same change pattern [23]. The raw EMG, integrated EMG load percentage, and EMG-angle curve plots of the peak force value of one movement with the highest peak force value, as shown in Figure 7, were obtained from a randomly selected tester in different angular velocity tests by EMG synchronization with video, combined with joint angle notation analysis, in the order of 15 degrees/s, 25 degrees/s, and 35 degrees/s.Figure 7
15 degrees/s, 25 degrees/s, and 35 degrees/s isometric deep squat graph.As the angular velocity of the test system increased, the time to peak force decreased significantly and the peak force decreased significantly, but the position of the resistance arm movement to peak force remained between 20 and 22 degrees. The peak knee joint angle was measured by an arthroscope between 90 and 110 degrees. The EMG curves of the corresponding movements showed the amplitude of the relevant muscles also reached the maximum at the angle of peak force, which suggests that this angle may be the best force angle for volleyball players and is consistent with the results of the jumping movement [24].RFID positioning system requires the reader to provide the signal strength information of the received tag, but most RFID systems do not provide the signal strength information directly, the reader can only detect the power level of the received signal, and the distinguishable power level of RFID system using LANDMARK algorithm positioning is generally 8. The increase of the distinguishable power levelp of the reader makes the distance accuracy obtainable higher, which affects the positioning accuracy of the tag. The reader in the simulation increases the power level p from 8 to 12 to identify the signal power emitted by the tag. Simulations were performed using 110 reference tags, as shown in Figure 8. The simulation results show that the increase in power level increases the localization accuracy, which is the reason for the increase in distance recognition accuracy due to the increase in distinguishable power and level.Figure 8
Comparison of simulation result error.Peak force and an average peak force of lower extremity multijoint flexor and extensor muscle groups showed a significant positive correlation with special sports performance, which indicates that stronger lower extremity muscle groups of athletes are very beneficial to the height of the jumping center of gravity takeoff. This further illustrates the importance of coordinated development of lower limb joint muscle strength to improve the jumping effect of the upper step bucket [25]. Therefore, special strength training focuses on the lower extremity extensor muscle group’s ability to improve; at the same time, the lower extremity flexor muscle group strength training should not be ignored. The maximum power and average power of the lower limb muscle groups also showed a significant positive correlation with the performance, which indicates that the power of the lower limb flexor and extensor muscle groups plays a coordinating role in the athletes’ jumping action, which coincides with the actual action of the stirrup phase. Strengthening the strength of the posterior muscle groups and small muscle groups should not be ignored, which is not only related to the coordination of muscle force, better play the efficiency of muscle work, successful completion of technical movements, but also one of the important methods to prevent sports injuries. The strengthening of the strength of the extensor group is conducive to the improvement of the efficiency of the stirrups, and the strengthening of the strength of the flexor group is conducive to the shortening of the buffer time, therefore conducive to the improvement of the jumping speed.


## 5. Conclusion

This paper constructs a flexible strain sensing network model using flexible sensors and simulates volleyball training patterns through model training, which has a positive effect on the skill improvement of athletes and coaches in volleyball training. The advantages and disadvantages of existing information acquisition systems are compared with the development status of sports information acquisition systems. In this paper, the pattern recognition of human motion by data fusion of acceleration sensor and gyroscope is combined with GA-BP neural network algorithm to repair and fuse the output information, which effectively improves the accuracy of attitude angle measurement. Based on the wireless positioning technology, an improved RFID positioning algorithm combining the LANDMARC algorithm and the weighted centroid algorithm in the wireless sensor network is proposed, and the positioning accuracy is as high as 89.2%. Through simulation training, the model can accurately capture the movement data of volleyball players’ jumping, snapping, and squatting cushions. However, the limitations of the monitoring channels of the sensor acquisition module make the synchronization of monitoring in the same motion state of the test object cannot be achieved yet, and more monitoring channels need to be considered for development in future research to achieve the purpose of synchronized monitoring and further increase the comparability of test results.



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*Source: 3907002-2022-08-18.xml*