# Design of Motion Damage Estimation Model Based on Improved Recursive Neural Network Algorithm

Authors: Jiaju Zhu; Song Hu; Meng Zhang
Journal: Mobile Information Systems
 (2022)

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## Abstract

In recent years, the China’s rapid economic development and living standards of people have been improved, and the usage of clothing, food, housing, and transportation has increased. However, due to the prevalence of sports injuries and their high frequency of incidence, sports injury incidents do occur from time to time. Sports injury will not only affect the normal training of athletes and sports enthusiasts but also adversely affect their health. An estimation model of sports injury is designed based on an improved recursive neural network algorithm to reduce the incidence of sports injury in the process of physical exercise so that athletes and sports enthusiasts can reduce sports injury, which is conducive to physical health and sports health.


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## Body

## 1. Introduction

As we enter a new era, the development of all aspects of our country has kept pace with the times, people’s lives are improving, people’s attention to health is improving, people’s enthusiasm for sports is increasing, and the state and government are actively solving and implementing sports facilities and other problems. Although we all pay attention to sports health and sports safety, there will still be sports injuries. Sports injury is a common injury problem in sports events. It will not only harm people’s physical and mental health but also affect sports courses and training plans. We can only lower the incidence of sports injury, offer a good exercise environment for sports participants, and so fully exploit the benefits of sports projects while improving the nation’s overall quality by learning the causes of sports injury and developing specific preventative programs. The emergence of a recursive neural network can provide a healthy and safe sports training plan for sports lovers and reduce sports injuries.


### 1.1. Recurrent Neural Networks (RNN)

A recurrent neural network is also a large-scale nonlinear dynamic system with feedback loops, which has been widely used in pattern recognition, image processing, intelligent control, signal processing optimization calculation, and other fields. Based on designing a sports injury estimation model, make sports athletes and sports fans according to the estimation model for movement, under the condition of guaranteeing health and safety of movement and regular plan for sport and exercise injury, make exercise a part of our lives, and let health and safety be a constant companion.


### 1.2. Sports Injury

Sports injury is a kind of injury problem that occurs in people’s bodies under the influence of many factors during physical exercise and participation in sports events. In sports events, the rate of sports injury in ball games, aerobics, and martial arts is shown in Figure1. In all sports injury cases, the proportion of joint sprain, abrasion, and contusion is shown in Figure 2. These sports injuries will cause certain damage to the human body, which is not conducive to the development of sports projects. The common sports injuries include elbow joint, shoulder blade, calf, and arm. In the emergency treatment of different types of sports injuries, corresponding treatment methods should be adopted to avoid greater damage to the human body [1]. Sports injuries are common in daily life. Sports injuries can be divided into acute, chronic, and stale injuries based on the severity of the onset, according to relevant studies. The importance of prompt care after a sports injury cannot be overstated. If an acute sports injury is not treated promptly and the therapy is ineffective, it will develop into an old injury, lengthening the injury and disease course [2].Figure 1
Sports injury rate in sports events.Figure 2
Proportion of sprains, abrasions, and contusions in sports injury cases.


### 1.3. Time of Sports Injury

Due to the different causes and types of sports injury, the frequency of its occurrence is not the same, mainly concentrated in 2-5 days, 3-4 days, 6 to 7 days, and 7 to 8 days, and the incidence of white three sports injury is relatively high, while the prevalence of sports injury after 3 months is small but also has a certain regularity. The characteristics of sports injury mainly include the following aspects [3]:
(1)
The location of a sports injury has a certain amount of instability, which is characterized by a muscle being stretched, compressed, and bent in the course of sports, causing changes in volume and mass, resulting in impairment of body functioning and thereby impacting human health(2)
When performing an activity, limited conditions caused by a lack of technical level or field made the completion of some actions difficult, and people’s physical quality was poor, or fatigue caused the local load to be too large, causing certain joint functions to become abnormal, eventually causing the hurt(3)
The timeliness of sports injury is when the participants are under high work pressure for a long time, or because of long-term fatigue; their physiological function gradually declines fatigue, and even paralysis, which leads to the formation of disease(4)
The form of sports injury is more diverse and generally can be divided into acute and chronicIt is an important task to strengthen the protection of sports injuries and to prevent and reduce the risk of sports injury.(1)
To correctly grasp the concept and classification of sports injury and carry out scientific treatment methods, to reduce the incidence of sports injury to a certain extent(2)
To take corresponding measures according to different situations to prevent the deformation or movement of the injured part and to reduce or control the damaged position(3)
Strengthen the protection and nursing of sports injury. After a sports injury, professional rehabilitation treatment should be carried out in time, and corresponding records should be made so that medical staff can better understand and master the situation and characteristics of sports injury, to take effective measures to prevent sports injury


### 1.4. Application of Recursive Neural Network

Recursive neural network (RNN) is widely used because of its storage characteristics, and it can process sequential data with related input before and after.Natural language data is a typical sequence data, and RNN can process sequence data with related input due to its storage characteristics. Therefore, RNN is an important algorithm to solve various natural language processing (NPL) problems, including speech recognition, machine translation, and text classification [4].The system that combines RNN and CNN can also be used in computer vision. In text recognition, for example, CNN is used to extract features from images containing characters and feed them into an LSTM for sequence annotation [5]. For video-based computer vision problems, such as behavioral cognition, RNN can learn by using image features extracted frame-by-frame by CNN [6]. Karpathy et al. [7] have built a model to generate natural language descriptions of images and their regions. To comprehend the modal relationship between language and visual data, an image data collection and a sentence description are employed. Model+ uses multimodal embedding to align two-mode inputs for structural purposes.In the field of computational biology, deep RNN is used to analyze various sequence data containing biological information, such as dividing the broken genes of exons and introns in DNA sequences [8], identifying small RNA molecules by RNA sequences [9], and predicting protein subcellular localization by protein sequences.In earth science, RNN is used for time series modeling. According to Kuai et al. [10], LSTM was used for hydrology, and a soil moisture active passive was constructed with the input of climate forcing, model simulated humidity, and static geological features. SMAP level 3 soil moisture data prediction system removes the majority of bias through model simulation while boosting moisture climatology prediction accuracy.In other fields, some scholars have optimized RNN to accelerate convergence and antinoise of the neural network model. Such as to autumn red [11], the combination of the traditional gradient neural network (GNN) model and Zhang neural network (ZNN) model in building a new type of neural network (new neural network (NNN)) model is applied to solve the time-varying matrix M-P inverse in different environments. Yongsheng and Lin [12] introduced the double sign power excitation function based on the traditional RNN model, constructed the RNN model with finite-time convergence, and applied it to solve the quadratic minimization problem.The rest of the paper is organized follow. In Section2, the construction of motion damage estimation model based on improved recursive neural network algorithm is discussed. The experiment and application analysis are presented in Section 3, followed by a conclusion in Section 4.


## 1.1. Recurrent Neural Networks (RNN)

A recurrent neural network is also a large-scale nonlinear dynamic system with feedback loops, which has been widely used in pattern recognition, image processing, intelligent control, signal processing optimization calculation, and other fields. Based on designing a sports injury estimation model, make sports athletes and sports fans according to the estimation model for movement, under the condition of guaranteeing health and safety of movement and regular plan for sport and exercise injury, make exercise a part of our lives, and let health and safety be a constant companion.


## 1.2. Sports Injury

Sports injury is a kind of injury problem that occurs in people’s bodies under the influence of many factors during physical exercise and participation in sports events. In sports events, the rate of sports injury in ball games, aerobics, and martial arts is shown in Figure1. In all sports injury cases, the proportion of joint sprain, abrasion, and contusion is shown in Figure 2. These sports injuries will cause certain damage to the human body, which is not conducive to the development of sports projects. The common sports injuries include elbow joint, shoulder blade, calf, and arm. In the emergency treatment of different types of sports injuries, corresponding treatment methods should be adopted to avoid greater damage to the human body [1]. Sports injuries are common in daily life. Sports injuries can be divided into acute, chronic, and stale injuries based on the severity of the onset, according to relevant studies. The importance of prompt care after a sports injury cannot be overstated. If an acute sports injury is not treated promptly and the therapy is ineffective, it will develop into an old injury, lengthening the injury and disease course [2].Figure 1
Sports injury rate in sports events.Figure 2
Proportion of sprains, abrasions, and contusions in sports injury cases.


## 1.3. Time of Sports Injury

Due to the different causes and types of sports injury, the frequency of its occurrence is not the same, mainly concentrated in 2-5 days, 3-4 days, 6 to 7 days, and 7 to 8 days, and the incidence of white three sports injury is relatively high, while the prevalence of sports injury after 3 months is small but also has a certain regularity. The characteristics of sports injury mainly include the following aspects [3]:
(1)
The location of a sports injury has a certain amount of instability, which is characterized by a muscle being stretched, compressed, and bent in the course of sports, causing changes in volume and mass, resulting in impairment of body functioning and thereby impacting human health(2)
When performing an activity, limited conditions caused by a lack of technical level or field made the completion of some actions difficult, and people’s physical quality was poor, or fatigue caused the local load to be too large, causing certain joint functions to become abnormal, eventually causing the hurt(3)
The timeliness of sports injury is when the participants are under high work pressure for a long time, or because of long-term fatigue; their physiological function gradually declines fatigue, and even paralysis, which leads to the formation of disease(4)
The form of sports injury is more diverse and generally can be divided into acute and chronicIt is an important task to strengthen the protection of sports injuries and to prevent and reduce the risk of sports injury.(1)
To correctly grasp the concept and classification of sports injury and carry out scientific treatment methods, to reduce the incidence of sports injury to a certain extent(2)
To take corresponding measures according to different situations to prevent the deformation or movement of the injured part and to reduce or control the damaged position(3)
Strengthen the protection and nursing of sports injury. After a sports injury, professional rehabilitation treatment should be carried out in time, and corresponding records should be made so that medical staff can better understand and master the situation and characteristics of sports injury, to take effective measures to prevent sports injury


## 1.4. Application of Recursive Neural Network

Recursive neural network (RNN) is widely used because of its storage characteristics, and it can process sequential data with related input before and after.Natural language data is a typical sequence data, and RNN can process sequence data with related input due to its storage characteristics. Therefore, RNN is an important algorithm to solve various natural language processing (NPL) problems, including speech recognition, machine translation, and text classification [4].The system that combines RNN and CNN can also be used in computer vision. In text recognition, for example, CNN is used to extract features from images containing characters and feed them into an LSTM for sequence annotation [5]. For video-based computer vision problems, such as behavioral cognition, RNN can learn by using image features extracted frame-by-frame by CNN [6]. Karpathy et al. [7] have built a model to generate natural language descriptions of images and their regions. To comprehend the modal relationship between language and visual data, an image data collection and a sentence description are employed. Model+ uses multimodal embedding to align two-mode inputs for structural purposes.In the field of computational biology, deep RNN is used to analyze various sequence data containing biological information, such as dividing the broken genes of exons and introns in DNA sequences [8], identifying small RNA molecules by RNA sequences [9], and predicting protein subcellular localization by protein sequences.In earth science, RNN is used for time series modeling. According to Kuai et al. [10], LSTM was used for hydrology, and a soil moisture active passive was constructed with the input of climate forcing, model simulated humidity, and static geological features. SMAP level 3 soil moisture data prediction system removes the majority of bias through model simulation while boosting moisture climatology prediction accuracy.In other fields, some scholars have optimized RNN to accelerate convergence and antinoise of the neural network model. Such as to autumn red [11], the combination of the traditional gradient neural network (GNN) model and Zhang neural network (ZNN) model in building a new type of neural network (new neural network (NNN)) model is applied to solve the time-varying matrix M-P inverse in different environments. Yongsheng and Lin [12] introduced the double sign power excitation function based on the traditional RNN model, constructed the RNN model with finite-time convergence, and applied it to solve the quadratic minimization problem.The rest of the paper is organized follow. In Section2, the construction of motion damage estimation model based on improved recursive neural network algorithm is discussed. The experiment and application analysis are presented in Section 3, followed by a conclusion in Section 4.


## 2. Construction of Motion Damage Estimation Model Based on Improved Recursive Neural Network Algorithm

In this section, we will discuss recurrent neural networks, the overall structure of training intensity divider in the rehabilitation process of sports injury, and functional analysis of training intensity divider during recovery of sports injury in detail.


### 2.1. Recurrent Neural Network

In recent years, deep learning technology develops rapidly and has been widely used in data information extraction. There are many kinds of neural network classification, such as deep neural network (DNN), convolutional neural network (CNN), and recursive neural network (RNN) [13]. Deep learning (DL) is an autonomous feature learning method that uses several nonlinear transformations such as rotation, distortion, and flips to turn the original data into a more abstract and higher-level representation. A recursive neural network is an artificial neural network (ANN) with a tree-like hierarchical structure in which the network nodes recurse the input information based on their connection order. One of the deep learning algorithms is this one.The structure of RNN is shown in Figure3. The prediction result of each moment will be put into the input of the next moment for calculation, and the result of the next moment will be generated together with the input of the next moment. RNN information forward transmission structure is shown in Figure 4. A represents the network, Xt represents the X input at time T, ht represents the result generated by the network, and A-line pointing to itself on network A indicates that the output at the last time is then input into A. A series of X inputs into the structure on the left side of the equals sign in Figure 4 is expanded to become the right side of the network containing the hidden layer, except that the output of the hidden layer is changed into two parts, one to the next node and the other to its node.Figure 3
Structure of RNN.Figure 4
Forward propagation structure of RNN.RNN features time self-feedback, as can be seen from its propagation structure, and the time information in the input sequence can be used to address time series classification and prediction issues. The prediction output of RNN at each moment is composed of two parts, one is from the input of the current moment, and the other is from the hidden state of the last moment. At present, RNN shows great advantages in speech recognition, machine translation, text language processing, and other fields [14, 15].


### 2.2. Overall Structure of Training Intensity Divider in the Rehabilitation Process of Sports Injury

To achieve the optimal design of a training intensity distributor in the process of sports injury recovery, the overall design framework for a training intensity distributor in the process of sports injury recovery was completed first, followed by the construction of a sensor system model for the automatic collection of training intensity information in the process of sports injury recovery. Construct the perception layer and network layer of training intensity distributor and information collection in the process of sports injury recovery [16]. The sensor network distribution and control structure module for training intensity information collection in the process of sports injury recovery are shown in Figure 5.Figure 5
Sensor network distribution and control structure module for information collection.According to the sensor network distribution and control structure module of information collection as shown in Figure5, combined with the wireless sensor network control method, training intensity allocation and adaptive control are carried out in the process of sports injury recovery. The parameter information of cardiopulmonary function and myocardial function is largely included in the information related to training intensity in the recovery process of sports injuries, and the type parameters are heart rate, vital capacity, muscle tensile strength, and so on. The index system of training intensity information collection in the recovery process of sports injury is shown in Figure 6.Figure 6
Index system of training intensity information collection in the recovery process of sports injury.


### 2.3. Functional Analysis of Training Intensity Divider during Recovery of Sports Injury

The mechanical sensor module, data processing module, man-machine control module, information interaction module, and output module interface, for example, were used to realize intelligence gathering and the strength of sports injury recovery training information acquisition terminal processing platform. Sports injury recovery training intensity information acquisition intelligent service system adopts the underlying framework agreement, combined with knowledge rule base management model design method, using the underlying information load control method, implementation of sports injury recovery training intensity information collection, and the process of interaction, and the sports injury recovery process training intensity allocator function module is shown in Figure7.Figure 7
Functional structure module of training intensity distributor in sports injury recovery process.According to the structure distribution of functional modules in Figure7, mathematical statistics and sample analysis methods are adopted in the data information processing module to realize the modulation processing and sensor information collection of intelligent information sampling of pressure, flow, and other sensing parameters.


## 2.1. Recurrent Neural Network

In recent years, deep learning technology develops rapidly and has been widely used in data information extraction. There are many kinds of neural network classification, such as deep neural network (DNN), convolutional neural network (CNN), and recursive neural network (RNN) [13]. Deep learning (DL) is an autonomous feature learning method that uses several nonlinear transformations such as rotation, distortion, and flips to turn the original data into a more abstract and higher-level representation. A recursive neural network is an artificial neural network (ANN) with a tree-like hierarchical structure in which the network nodes recurse the input information based on their connection order. One of the deep learning algorithms is this one.The structure of RNN is shown in Figure3. The prediction result of each moment will be put into the input of the next moment for calculation, and the result of the next moment will be generated together with the input of the next moment. RNN information forward transmission structure is shown in Figure 4. A represents the network, Xt represents the X input at time T, ht represents the result generated by the network, and A-line pointing to itself on network A indicates that the output at the last time is then input into A. A series of X inputs into the structure on the left side of the equals sign in Figure 4 is expanded to become the right side of the network containing the hidden layer, except that the output of the hidden layer is changed into two parts, one to the next node and the other to its node.Figure 3
Structure of RNN.Figure 4
Forward propagation structure of RNN.RNN features time self-feedback, as can be seen from its propagation structure, and the time information in the input sequence can be used to address time series classification and prediction issues. The prediction output of RNN at each moment is composed of two parts, one is from the input of the current moment, and the other is from the hidden state of the last moment. At present, RNN shows great advantages in speech recognition, machine translation, text language processing, and other fields [14, 15].


## 2.2. Overall Structure of Training Intensity Divider in the Rehabilitation Process of Sports Injury

To achieve the optimal design of a training intensity distributor in the process of sports injury recovery, the overall design framework for a training intensity distributor in the process of sports injury recovery was completed first, followed by the construction of a sensor system model for the automatic collection of training intensity information in the process of sports injury recovery. Construct the perception layer and network layer of training intensity distributor and information collection in the process of sports injury recovery [16]. The sensor network distribution and control structure module for training intensity information collection in the process of sports injury recovery are shown in Figure 5.Figure 5
Sensor network distribution and control structure module for information collection.According to the sensor network distribution and control structure module of information collection as shown in Figure5, combined with the wireless sensor network control method, training intensity allocation and adaptive control are carried out in the process of sports injury recovery. The parameter information of cardiopulmonary function and myocardial function is largely included in the information related to training intensity in the recovery process of sports injuries, and the type parameters are heart rate, vital capacity, muscle tensile strength, and so on. The index system of training intensity information collection in the recovery process of sports injury is shown in Figure 6.Figure 6
Index system of training intensity information collection in the recovery process of sports injury.


## 2.3. Functional Analysis of Training Intensity Divider during Recovery of Sports Injury

The mechanical sensor module, data processing module, man-machine control module, information interaction module, and output module interface, for example, were used to realize intelligence gathering and the strength of sports injury recovery training information acquisition terminal processing platform. Sports injury recovery training intensity information acquisition intelligent service system adopts the underlying framework agreement, combined with knowledge rule base management model design method, using the underlying information load control method, implementation of sports injury recovery training intensity information collection, and the process of interaction, and the sports injury recovery process training intensity allocator function module is shown in Figure7.Figure 7
Functional structure module of training intensity distributor in sports injury recovery process.According to the structure distribution of functional modules in Figure7, mathematical statistics and sample analysis methods are adopted in the data information processing module to realize the modulation processing and sensor information collection of intelligent information sampling of pressure, flow, and other sensing parameters.


## 3. Experiment and Application Analysis

To verify the application performance of the design system in realizing intelligent allocation of training intensity in the process of sports injury recovery, experimental tests were carried out. Firstly, sensors were used to collect training intensity data in the process of sports injury recovery, and the distribution of sampling data was shown in Table1.Table 1
Data collection of training intensity during the recovery process of sports injury.
Sensing nodeThe intensity of training (kPa)Training load (kJ)Adaptation10.4080.5780.80520.4140.5620.80030.4060.5550.79740.4160.5670.80150.4040.5890.80960.4050.5830.80770.4000.5920.81080.4030.5700.80290.4060.5760.805100.3990.5710.803110.3970.5800.806120.3950.5940.811130.3980.5640.800140.3900.5690.802150.4130.5890.809The automatic distribution mechanism is used to realize the intelligent distribution of training intensity in the process of sports injury recovery and reduce the risk of sports training injury. It is based on an improved recursive neural network algorithm, combined with mechanics distribution and parameter control technology. The optimization design of the intelligent distributor of training intensity in the process of sports injury recovery is carried out. The mechanical feedback sensor is used to collect the training intensity intelligently. The flexible programmable logic coding control method is used in conjunction with the encoder to realize the intensity monitoring and precision analysis of training injury, as well as to realize the programming control and cross-compilation in the intelligent allocation of training intensity in the recovery process of sports injury and to realize the development and design of distributor. The results show that the method has a better balance in the intelligent allocation of training intensity in the process of sports injury recovery.


## 4. Conclusion

RNN is widely used in natural language processing due to its storage characteristics, which can process sequential data with related input. The design of the sports injury estimating model in this research, based on the improved recursive neural network algorithm, corresponds to the recovery training process of sports injury and provides a feasible technique for athletes and sports fans to enjoy the bliss of sports. Although the research on RNN has been very thorough at present, there is still great room for progress in many aspects. As a result, in colleges and universities, special sports training and daily, the relevant personnel should be guided by the necessary education guidance; guide athletes and sports fans and students attach importance to sports injury. The method of understanding is based on the causes of sports injury, avoiding physical injury risk, and at the same time, being able to fully understand sports injury, understand the effective method of dealing with the injury. In this way, the incidence of sports injury can be reduced, the physical education curriculum practice and special sports training can be completed in an orderly manner, and the goal of sports power can be achieved.



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*Source: 2018867-2022-04-30.xml*