# Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks

Authors: Qingkun Feng; Yanying Liu; Lijun Wang
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

For football players who participate in sports, the word “health” is extremely important. Athletes cannot create their own value in competitive competitions without a strong foundation. Scholars have paid a lot of attention to athlete health this year, and many analysis methods have been proposed, but there have been few studies using neural networks. As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. To begin, this article employs wearable sensors to collect health data from football players. The time step data are then fed into a recurrent neural network to extract deep features, followed by the health prediction results. The collected football player health dataset is used in this paper to conduct experiments. The simulation results prove the reliability and superiority of the proposed algorithm. Furthermore, the algorithm presented in this paper can serve as a foundation for the football team’s and coaches’ scientific training plans.


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## Body

## 1. Introduction

Health is inextricably linked to one’s field of endeavor. Our forefathers have faced health issues that cannot be avoided as living creatures seek a place to call home in the natural world since the dawn of civilization. The most important thing for football players to know in order to train and play in a healthy manner is to understand their current state of health and to deal with it scientifically based on their individual circumstances. Many scientific studies have shown that the decline in the physical function of football players does not begin with the skin, hair, muscle tissue, or internal organ circulatory system, but rather with our spine, which is the first to show signs of aging. The health of the spine is closely related to the aging and disease of athletes’ physical functions, and the health of the spine is often reflected in a person’s body shape and movements in daily life. These data are gathered in a timely manner and relayed to the coach or professional doctor. Correct diagnosis and intervention are critical in preventing diseases in football players.Wearable health monitoring technology [1–3] based on various athletes’ human indicators, monitoring indicators, and scope of use of these systems and products are different. People’s daily activity that can most often reflect their health status is walking. Walking can reflect the aging and health characteristics of the human spine, thereby predicting the health threats athletes may face. Based on the athlete’s training volume, it can be evaluated. Their daily training volume level reflects their exercise volume and their own metabolic consumption level. In recent years, there have been abundant research studies [4, 5] on human wearable health monitoring technology, but health monitoring far away from professional environments such as hospitals relies more on various smart instruments and equipment, which is more challenging [6, 7]. Therefore, health monitoring and data acquisition based on athlete training are becoming more and more important. Therefore, this article uses wearable health monitoring technology to obtain athlete health data.The design of a compact wearable sensor patch was proposed by Wu et al. [8] which measures various physiological signals and is easily accessible for remote health monitoring applications in the human body. In the context of the ongoing health control system for athletes, Huifeng et al. [9] proposed a wearable sensor based on the Internet of Things (WS-IoT). The objective is to establish sports medicine health clinics and sports team performance services in order to use the technology more effectively to help athletes return to competitions in various sports areas. Health information is gathered, and wearable tracking equipment activity is tracked.With the rapid development of neural networks and sensor technologies based on the Internet of Things, we can more intelligently detect athletes’ training status and physical conditions. Therefore, this paper proposes a wearable device smart football player health prediction algorithm [10, 11] based on the recurrent neural network (RNN). Firstly, wearable sensors are used to obtain the health information data [12–14] of football players and to obtain the big data of the athletes’ health [15, 16]. Secondly, the time step data are input into the recurrent neural network for deep feature mining, and finally, the health prediction result is obtained.In addition, for the health data, the previous studies used traditional methods to analyze and predict, but with the rapid development of neural networks, we can use deep network technology to propose deep features of the health data to obtain more accurate prediction results. Especially, the recurrent neural network has achieved powerful performance in the processing of the time series data, so this article uses the recurrent neural network for feature extraction and prediction.The main work of this paper is summarized as follows:(1)
This paper proposes the use of wearable computing to obtain sufficient athletes’ health information, and the method of obtaining is real time and effective.(2)
This paper proposes to use the recurrent neural network to process the time series health data of football players, which can make accurate health predictions.(3)
We proved the effectiveness of the smart football sports health prediction algorithm based on the RNN and wearable devices proposed in this paper, which can be formulated for sports economic teams and coaches. Reasonable training plan and athlete management monitoring provide a scientific basis.


## 2. Related Work

Wearable health monitoring systems (WHMSs) for health monitoring can include various types of miniature sensors that can be worn on the surface of the body and can measure important physiological parameters of the human body and even some devices that can be implanted into the human body to obtain more accurate and detailed health information. The important data collected by the microsensor will be transmitted to a central processing node through wired or wireless communication, such as some kind of MCU motherboard, and display its health on the user interface after some calculations. Information or the comprehensive information that characterizes the life condition is sent to the medical center. A company called Heapsylon has developed a smart sock Sensoria to monitor the calorie consumption and exercise of football players. Sensoria uses a soft fabric sensor woven instead of tufting. These sensors are designed for footsteps and generate accurate motion data, allowing users to quickly assess and improve their posture. To play a better role, this sock should also have a magnetic foot ring. The sensory center of the sock sensor contains one or two pressure sensors that collect everyone at the same distance.Artificial neural networks [17–19] have been widely used in all life spheres in recent years. Schmidhuber was the first to propose the structure of the RNN. Its neurons can not only receive information from other neurons but also receive their own information. RNN mainly uses unsupervised learning in the deep neural network [20–22] to train itself. Each independent RNN neuron can be used to predict the input of the next neuron after it has been trained independently. The RNN model has an advantage in processing short-term time series data because the hidden layer has only one cell state, which is very sensitive to short-term data. However, due to the delay in the weight update in the activation function, which causes the gradient to propagate in a longer time as the amount of time series data and the number of model training layers increase, the traditional recurrent neural network will show obvious gradient disappearance as the amount of time series data and the number of model training layers increase. It will not be possible to complete it in the time allotted. Because the model initialization weight of the traditional recurrent neural network is too large, the weight of the shallow network is updated faster than the weight of the deep network, causing the model weight to grow as the number of network layers grows. The effect of the model gradient gradually dissipating is created.


## 3. Methodology

Figure1 is a schematic diagram of the architecture of the proposed WD-RNN algorithm. Firstly, we use wearable sensors to obtain health information data from football players. Secondly, the athlete’s health data are fed into the recurrent neural network for health prediction and then inputted to the team of doctors and coaches, respectively, to get medical advice and sports training mechanism advice. Finally, according to the two suggestions, the final health prediction of football players is made in order to formulate a reasonable training plan.Figure 1
Schematic diagram of the algorithm in this paper.


### 3.1. Wearable Health Monitoring System

WHMS for health monitoring can include various types of miniature sensors that can be worn on the surface of the body and can measure important physiological parameters of the human body and even some devices that can be implanted into the human body to obtain more accurate and detailed health information. The important data collected by the microsensor will be sent to a central processing node via wired or wireless communication, such as a personal data assistant (PDA) or some kind of MCU motherboard, which will then display its health information on the user interface or send these comprehensive data representing life conditions to a third party. The WHMS network structure is depicted in Figure2.Figure 2
Wearable health monitoring system structure.Table1 lists the human body monitoring indicators in the general WHMS architecture. However, there is no standard system design for the WHMS because the architecture methods used between different systems may be very different. For example, biological signals can be transmitted in an analog form. There is no need to preprocess the central node, or the two-way communication between the sensor and the central node does not exist.Table 1
Common WHMS monitoring index.
Health information typeSensor typeECGSkin electrodeBlood pressureArm sleeve detectorBreath ratePiezoresistive sensorBlood oxygen saturationPulse oximeterHeart ratePulse oximeterHeart soundPhonocardiographHuman movementInertial sensorThe complex and diverse WHMS architecture shows that designing a wearable health monitoring system is a very challenging task. Designers usually need to meet some conflicting requirements under many high constraints. No single ideal system design exists, and the balance between the parameters of system countermeasures should be balanced according to specific application areas. In the WHMS environment, there are two different purposes in transmitting the measurement data, namely, to transmit the physiologic signal collected through the biosensor to the system central node and to transmit the wearable system measurement data to the remote medical station or doctor. Data transmission or other short-range transmissions can be handled by wired or multiple wireless links in some WHMSs. However, in a health monitoring system with wired data transmission, the user’s mobility and comfort will be severely limited, and the risk of system failure will be increased. Currently, major research labs working on wearable health monitoring systems use conductive yarns to transmit the measurement data collected from sensors integrated on specific flexible smart textile clothing, allowing the sensor nodes to form a body area network. Usually, in the basic configuration of the star topology, data are transmitted to the central node of the network, which can be a PDA, a smart phone, a handheld computer, or a microcontroller-based device customized by the developer.


### 3.2. Recurrent Neural Network

RNN [23–25] is a neural network that specializes in processing time series data. It is like a cyclic dynamic system in which the output results of the previous cycle are retained and used as part of the input of the next cycle. Compared with other neural networks [26, 27], RNN has certain advantages. For example, neurons in the same layer of a traditional neural network will not communicate with each other, while the RNN model can allow neurons in the hidden layer to communicate with each other and store the output results of the previous neural unit in the hidden layer as information. Its model unit structure is shown in Figure 3.Figure 3
Schematic diagram of the RNN model unit.


#### 3.2.1. Standard Recurrent Neural Network

The input data of the RNN are a sequence (time, sequence, etc.), and a closed loop can be formed during the transmission and iteration of the sequence data to store the relationship between the input and output of the neuron at the current time and the previous time. When data information is transmitted from one neuron to the next, the information does not disappear immediately, but still exists. RNN can remember multiple information at the same time because the RNN activates multiple different neurons at once. Its basic structure is shown in Figure3. The meaning of each parameter is as follows: U is the weight matrix from the input layer to the hidden layer, V is the weight matrix from the hidden layer to the output layer, W is a mapping from the hidden layer to itself, and O is the value of the output layer. The value of the hidden layer S is related to the input value X at the current moment and the hidden layer S at the previous moment, indicating that the RNN has the ability to remember. Figure 3 is the basic structure model of the RNN expanded in the time dimension.(1)Ot=gV×St,St=fUXt+WSt−1,where U,V,andW are a set of shared parameters.


#### 3.2.2. Bidirectional Recurrent Neural Network

The current output of a bidirectional RNN is assumed to be related to both previous and subsequent sequence data. As shown in Figure4, the hidden layer goes from both forward and reverse directions.(2)h⟶t=fW⟶Xt+V⟶h⟶t−1+b⟶,h←t=fW←Xt+V←←ht−1+b←,yt=gUh⟶t;h←t+c.Figure 4
Schematic diagram of the bidirectional RNN.


#### 3.2.3. Bidirectional Long Short-Term Memory Network

LSTM still uses the RNN modeling method in the direction of the time stream, which means LSTM only uses past time serial information and does not use future information of the data, although the long-term dependency problem can be resolved and RNN deficiencies can be compensated. However, we cannot underestimate the importance of future modeling information. The Bi-LSTM model has therefore come into being.The Bi-LSTM model can, at the same time, take into account information from the past and future data which is expanded in Figure5. The principle that works is to obtain two clock layer states with opposite series of times through forward LSTM and backward LSTM and then connect them to the same output. Forward LSTM and backward LSTM can obtain information about the past input sequence and information about the future. The hidden state Ht of Bi-LSTM at time t includes forward h⟶t and backward ht←:(3)ht⟶=LSTM⟶ht−1,xt,ct−1,t∈1,T,ht←=LSTM←ht+1,xt,ct+1,t∈T,1,Ht=ht⟶,ht←,where T is the length of the sequence.Figure 5
Basic structure of Bi-LSTM.


## 3.1. Wearable Health Monitoring System

WHMS for health monitoring can include various types of miniature sensors that can be worn on the surface of the body and can measure important physiological parameters of the human body and even some devices that can be implanted into the human body to obtain more accurate and detailed health information. The important data collected by the microsensor will be sent to a central processing node via wired or wireless communication, such as a personal data assistant (PDA) or some kind of MCU motherboard, which will then display its health information on the user interface or send these comprehensive data representing life conditions to a third party. The WHMS network structure is depicted in Figure2.Figure 2
Wearable health monitoring system structure.Table1 lists the human body monitoring indicators in the general WHMS architecture. However, there is no standard system design for the WHMS because the architecture methods used between different systems may be very different. For example, biological signals can be transmitted in an analog form. There is no need to preprocess the central node, or the two-way communication between the sensor and the central node does not exist.Table 1
Common WHMS monitoring index.
Health information typeSensor typeECGSkin electrodeBlood pressureArm sleeve detectorBreath ratePiezoresistive sensorBlood oxygen saturationPulse oximeterHeart ratePulse oximeterHeart soundPhonocardiographHuman movementInertial sensorThe complex and diverse WHMS architecture shows that designing a wearable health monitoring system is a very challenging task. Designers usually need to meet some conflicting requirements under many high constraints. No single ideal system design exists, and the balance between the parameters of system countermeasures should be balanced according to specific application areas. In the WHMS environment, there are two different purposes in transmitting the measurement data, namely, to transmit the physiologic signal collected through the biosensor to the system central node and to transmit the wearable system measurement data to the remote medical station or doctor. Data transmission or other short-range transmissions can be handled by wired or multiple wireless links in some WHMSs. However, in a health monitoring system with wired data transmission, the user’s mobility and comfort will be severely limited, and the risk of system failure will be increased. Currently, major research labs working on wearable health monitoring systems use conductive yarns to transmit the measurement data collected from sensors integrated on specific flexible smart textile clothing, allowing the sensor nodes to form a body area network. Usually, in the basic configuration of the star topology, data are transmitted to the central node of the network, which can be a PDA, a smart phone, a handheld computer, or a microcontroller-based device customized by the developer.


## 3.2. Recurrent Neural Network

RNN [23–25] is a neural network that specializes in processing time series data. It is like a cyclic dynamic system in which the output results of the previous cycle are retained and used as part of the input of the next cycle. Compared with other neural networks [26, 27], RNN has certain advantages. For example, neurons in the same layer of a traditional neural network will not communicate with each other, while the RNN model can allow neurons in the hidden layer to communicate with each other and store the output results of the previous neural unit in the hidden layer as information. Its model unit structure is shown in Figure 3.Figure 3
Schematic diagram of the RNN model unit.


### 3.2.1. Standard Recurrent Neural Network

The input data of the RNN are a sequence (time, sequence, etc.), and a closed loop can be formed during the transmission and iteration of the sequence data to store the relationship between the input and output of the neuron at the current time and the previous time. When data information is transmitted from one neuron to the next, the information does not disappear immediately, but still exists. RNN can remember multiple information at the same time because the RNN activates multiple different neurons at once. Its basic structure is shown in Figure3. The meaning of each parameter is as follows: U is the weight matrix from the input layer to the hidden layer, V is the weight matrix from the hidden layer to the output layer, W is a mapping from the hidden layer to itself, and O is the value of the output layer. The value of the hidden layer S is related to the input value X at the current moment and the hidden layer S at the previous moment, indicating that the RNN has the ability to remember. Figure 3 is the basic structure model of the RNN expanded in the time dimension.(1)Ot=gV×St,St=fUXt+WSt−1,where U,V,andW are a set of shared parameters.


### 3.2.2. Bidirectional Recurrent Neural Network

The current output of a bidirectional RNN is assumed to be related to both previous and subsequent sequence data. As shown in Figure4, the hidden layer goes from both forward and reverse directions.(2)h⟶t=fW⟶Xt+V⟶h⟶t−1+b⟶,h←t=fW←Xt+V←←ht−1+b←,yt=gUh⟶t;h←t+c.Figure 4
Schematic diagram of the bidirectional RNN.


### 3.2.3. Bidirectional Long Short-Term Memory Network

LSTM still uses the RNN modeling method in the direction of the time stream, which means LSTM only uses past time serial information and does not use future information of the data, although the long-term dependency problem can be resolved and RNN deficiencies can be compensated. However, we cannot underestimate the importance of future modeling information. The Bi-LSTM model has therefore come into being.The Bi-LSTM model can, at the same time, take into account information from the past and future data which is expanded in Figure5. The principle that works is to obtain two clock layer states with opposite series of times through forward LSTM and backward LSTM and then connect them to the same output. Forward LSTM and backward LSTM can obtain information about the past input sequence and information about the future. The hidden state Ht of Bi-LSTM at time t includes forward h⟶t and backward ht←:(3)ht⟶=LSTM⟶ht−1,xt,ct−1,t∈1,T,ht←=LSTM←ht+1,xt,ct+1,t∈T,1,Ht=ht⟶,ht←,where T is the length of the sequence.Figure 5
Basic structure of Bi-LSTM.


## 3.2.1. Standard Recurrent Neural Network

The input data of the RNN are a sequence (time, sequence, etc.), and a closed loop can be formed during the transmission and iteration of the sequence data to store the relationship between the input and output of the neuron at the current time and the previous time. When data information is transmitted from one neuron to the next, the information does not disappear immediately, but still exists. RNN can remember multiple information at the same time because the RNN activates multiple different neurons at once. Its basic structure is shown in Figure3. The meaning of each parameter is as follows: U is the weight matrix from the input layer to the hidden layer, V is the weight matrix from the hidden layer to the output layer, W is a mapping from the hidden layer to itself, and O is the value of the output layer. The value of the hidden layer S is related to the input value X at the current moment and the hidden layer S at the previous moment, indicating that the RNN has the ability to remember. Figure 3 is the basic structure model of the RNN expanded in the time dimension.(1)Ot=gV×St,St=fUXt+WSt−1,where U,V,andW are a set of shared parameters.


## 3.2.2. Bidirectional Recurrent Neural Network

The current output of a bidirectional RNN is assumed to be related to both previous and subsequent sequence data. As shown in Figure4, the hidden layer goes from both forward and reverse directions.(2)h⟶t=fW⟶Xt+V⟶h⟶t−1+b⟶,h←t=fW←Xt+V←←ht−1+b←,yt=gUh⟶t;h←t+c.Figure 4
Schematic diagram of the bidirectional RNN.


## 3.2.3. Bidirectional Long Short-Term Memory Network

LSTM still uses the RNN modeling method in the direction of the time stream, which means LSTM only uses past time serial information and does not use future information of the data, although the long-term dependency problem can be resolved and RNN deficiencies can be compensated. However, we cannot underestimate the importance of future modeling information. The Bi-LSTM model has therefore come into being.The Bi-LSTM model can, at the same time, take into account information from the past and future data which is expanded in Figure5. The principle that works is to obtain two clock layer states with opposite series of times through forward LSTM and backward LSTM and then connect them to the same output. Forward LSTM and backward LSTM can obtain information about the past input sequence and information about the future. The hidden state Ht of Bi-LSTM at time t includes forward h⟶t and backward ht←:(3)ht⟶=LSTM⟶ht−1,xt,ct−1,t∈1,T,ht←=LSTM←ht+1,xt,ct+1,t∈T,1,Ht=ht⟶,ht←,where T is the length of the sequence.Figure 5
Basic structure of Bi-LSTM.


## 4. Experiments

### 4.1. Experimental Environment

The computer used in the experiment will be configured to use Intel Core i5-7200U CPU, Python 3.6 will be installed under 64 bit Windows System, GTX TitanX1080 will be used for the graphics card in the training of algorithms, and TensorFlow 1.4 is a network training framework under Linux. In the experiment, the update method adopts the stochastic gradient descent method, which is a gradual learning framework. This article implements specific algorithms based on the TensorFlow framework.


### 4.2. Hyperparameter Settings

In the process of network training, for hyperparameters such as network learning rate and dropout probability, this article first manually adjusts based on small-scale training data. After roughly determining the appropriate parameter range by using the dichotomy method, the direct loop method is used to finally refine the specific parameters. The value is taken, and then the determined network parameters are used for large-scale dataset training to obtain the final network parameters. The final network learning rate is determined to be 0.003, and the dropout probability is 40%.


### 4.3. Experimental Results of Different Methods

Experimental results to verify the proposed system are presented in this section. This paper presents experimental findings to verify the system proposed. Empirical experiments on 1,000 pieces of soccer player health information collected in this paper have been conducted to ensure the effectiveness of this method. We compared our method with existing mainstream methods, including the traditional method, SVM, BP, and CNN, to prove the effectiveness of the WD-RNN algorithm in this paper. A quantitative comparison of various methods is presented in Table2. Table 3 shows that our method may achieve less time for training while maintaining precision at a level similar to other methods. The experimental results show that the algorithm in this paper has achieved the latest precision. In short, the method based on recurrent neural networks is acceptable and runs very quickly.Table 2
Comparison of the accuracy of different methods.
MethodsAccuracy (%)Traditional method0.42SVM0.50BP0.64CNN0.73WD-RNN (ours)0.81Table 3
Calculation time of each method.
MethodsTimeTraditional method1 daySVM0.53 sBP0.34 sCNN0.33 sWD-RNN (ours)0.21 s


### 4.4. Visualization of Results

We selected 100 football players for experimental testing. All of these 100 football players will undergo normal sports training within a week. Our wearable sensors will collect the physical health information of the athletes and then use the collected training feedback. Enter the recurrent neural network for training, and finally, we compared the output results of the neural network with the comprehensive results of the team of doctors and coaches. From Figure6, the prediction of the health index of every football player is good, which once again proves the effectiveness of the algorithm in this paper.Figure 6
Visualization of test results.


### 4.5. Ablation Experiments

In order to further verify the effectiveness of our algorithm, we set up an ablation experiment in this section. We setF to be the upload frequency of the wearable sensor data. The frequency is set to once in 10 seconds, once in 1 minute, and once in 5 minutes. The results are shown in Table 4.Table 4
Results of ablation experiments.
Frequency (s)Accuracy (%)100.80600.813000.72As can be seen from Table4, when the frequency is set to 1 minute, the calculation cost will be increased, while the model performance will remain almost unchanged. At the same time, if the frequency is increased to 5 minutes, the model performance will decline significantly. Therefore, the wearable sensor’s frequency set to 1 minute is the best.


## 4.1. Experimental Environment

The computer used in the experiment will be configured to use Intel Core i5-7200U CPU, Python 3.6 will be installed under 64 bit Windows System, GTX TitanX1080 will be used for the graphics card in the training of algorithms, and TensorFlow 1.4 is a network training framework under Linux. In the experiment, the update method adopts the stochastic gradient descent method, which is a gradual learning framework. This article implements specific algorithms based on the TensorFlow framework.


## 4.2. Hyperparameter Settings

In the process of network training, for hyperparameters such as network learning rate and dropout probability, this article first manually adjusts based on small-scale training data. After roughly determining the appropriate parameter range by using the dichotomy method, the direct loop method is used to finally refine the specific parameters. The value is taken, and then the determined network parameters are used for large-scale dataset training to obtain the final network parameters. The final network learning rate is determined to be 0.003, and the dropout probability is 40%.


## 4.3. Experimental Results of Different Methods

Experimental results to verify the proposed system are presented in this section. This paper presents experimental findings to verify the system proposed. Empirical experiments on 1,000 pieces of soccer player health information collected in this paper have been conducted to ensure the effectiveness of this method. We compared our method with existing mainstream methods, including the traditional method, SVM, BP, and CNN, to prove the effectiveness of the WD-RNN algorithm in this paper. A quantitative comparison of various methods is presented in Table2. Table 3 shows that our method may achieve less time for training while maintaining precision at a level similar to other methods. The experimental results show that the algorithm in this paper has achieved the latest precision. In short, the method based on recurrent neural networks is acceptable and runs very quickly.Table 2
Comparison of the accuracy of different methods.
MethodsAccuracy (%)Traditional method0.42SVM0.50BP0.64CNN0.73WD-RNN (ours)0.81Table 3
Calculation time of each method.
MethodsTimeTraditional method1 daySVM0.53 sBP0.34 sCNN0.33 sWD-RNN (ours)0.21 s


## 4.4. Visualization of Results

We selected 100 football players for experimental testing. All of these 100 football players will undergo normal sports training within a week. Our wearable sensors will collect the physical health information of the athletes and then use the collected training feedback. Enter the recurrent neural network for training, and finally, we compared the output results of the neural network with the comprehensive results of the team of doctors and coaches. From Figure6, the prediction of the health index of every football player is good, which once again proves the effectiveness of the algorithm in this paper.Figure 6
Visualization of test results.


## 4.5. Ablation Experiments

In order to further verify the effectiveness of our algorithm, we set up an ablation experiment in this section. We setF to be the upload frequency of the wearable sensor data. The frequency is set to once in 10 seconds, once in 1 minute, and once in 5 minutes. The results are shown in Table 4.Table 4
Results of ablation experiments.
Frequency (s)Accuracy (%)100.80600.813000.72As can be seen from Table4, when the frequency is set to 1 minute, the calculation cost will be increased, while the model performance will remain almost unchanged. At the same time, if the frequency is increased to 5 minutes, the model performance will decline significantly. Therefore, the wearable sensor’s frequency set to 1 minute is the best.


## 5. Conclusion

This paper proposes a smart football player health prediction algorithm based on the wearable device recurrent neural network. First of all, this article uses wearable sensors to obtain the health information data of football players in order to obtain big data of the athletes’ health. Second, the time step data are input into the recurrent neural network to extract deep features, and finally, the health prediction results are obtained. This article selected 100 football players to conduct experiments. The experimental results reached an accuracy of 81%, which is significantly better than other methods. It proves the effectiveness and superiority of the algorithm in this paper. In addition, the algorithm in this paper can provide a basis for the scientific training plan of football teams and coaches and can bring certain reference significance to the competitive sports industry.



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*Source: 2613300-2021-07-31.xml*