Skip to main content

RETRACTED ARTICLE: Design and Implementation of Track and Field Training Information Collection and Feedback System Based on Multi-sensor Information Fusion

This article was retracted on 10 September 2022

This article has been updated


Track and field sports are known as the "mother of sports". Whether in the field of athletics, fitness, or education, modern track and field sports have developed rapidly. The field of athletics has reached the point where it challenges the limits of humans. The development of China is inseparable from the support of science and technology, and it is inseparable from human scientific research on track and field sports. In order to improve the scientific level of track and field training methods and develop our country's sports industry, this paper designs a track and field training information collection and feedback system based on multi-sensor information fusion. In the method part, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, using weight coefficient fusion method, D-S evidence theory algorithm and Kalman filter algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, and data record analysis on this system. By designing the experimental group and the control group, it can be seen that the average performance of the two groups of athletes in the 50-meter run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine.

1 Introduction

The development of modern competitive sports is closely related to sports science research. The scientific training method has become an important factor in improving performance. Scientific training methods can not only tap the potential of athletes, but also improve the energy utilization efficiency of athletes to complete technical movements, so that they can adopt more scientific and economical exercise modes. With the goal of improving the scientific level of training methods, athletes and coaches must have a deeper understanding of the essential laws of sports events, including kinematics and dynamics principles, physiological and biochemical related knowledge, and so on.

Information fusion technology is currently a relatively cutting-edge technology. In today’s information age, fusion technology has highlighted its own advantages. Information fusion can capture the relevant features of the target more accurately. Appropriate selection of sensors and complementary sensor advantages are the combined effects of sensors. Improve the overall environment description ability, resolution ability and operation efficiency, reliability and fault tolerance of the system, and reduce the system cost.

Against the above background, many scholars at home and abroad have conducted related research. Liu M believes that dynamic obstacle detection is the key to ensuring that agricultural robots can move autonomously in unstructured environments. By choosing compass equipment, inertial measurement unit and two-dimensional laser scanner as the external sensors of the system, he proposed a dynamic obstacle detection method based on multi-sensor information fusion. In his research, he used a method based on Kalman filter to fuse compass device data and inertial measurement to obtain the position of agricultural machinery. The experiment proves the effectiveness of the method and has certain significance for the realization of autonomous mobile robots. In this study, the author used multi-sensor information fusion technology to detect dynamic obstacles in the autonomous movement of the robot, but did not analyze the defects of this system [1].NH Rijken aims to investigate the effects of psychological counseling combined with electroencephalogram (EEG) alpha power feedback or heart rate variability (HRV) feedback on HRV, EEG terminals and self-report factors related to stress, performance, recovery ability and sleep quality. He designed the experiment to be carried out in two different cohorts, providing football players with four sessions of psychological counseling, combined with daily HRV biofeedback (group A); providing four sessions of psychological counseling for track and field athletes, and combining daily nerves Feedback (Group B). In group A, in 5 out of 7 EEG positions, the alpha power changed significantly over time (p <0.01–0.03). The LF/HF ratio increased significantly (p = 0.02), and the concentration of SIM-60 (p = 0.02) and the mood scale (p = 0.03) increased significantly (p = 0.04). In group B, the HRV low frequency power and recovery scale of REST-Q increased significantly (p = 0.02 and <0.01 relative). It is concluded that a mental coaching program combined with HRV or EEGα power feedback may increase HRV and α power, and may lead to better performance-related results and reduce stress. In this study, the author conducted a comparative analysis of two groups of experimental subjects, but did not elaborate on the related algorithms of psychological counseling and EEG [2].T Kitazawa introduced the effect of the e-learning system using the feedback system. This e-learning system is in a mixed learning environment of information and communication technology education at a Japanese university. Through the e-learning system, each student’s task performance and teacher’s comments are disclosed every time. The structural equation model shows that students who have an e-learning system and a feedback system in the class visit it more frequently than students who do not have an e-learning system in the class. The research results show that students’ classroom performance is affected by the feedback system. The use of e-learning system directly affects students' understanding of tasks. In addition, students’ self-efficacy has an indirect effect on their classroom performance. This research designed an e-learning system for information collection and feedback on the performance of students. The application prospects are very impressive, but it lacks a large amount of experimental data and contingency cannot be avoided [3].

This article aims to find a scientific and efficient training method for athletes by designing an information collection and feedback system. This article first introduces the existing research on multi-sensor information fusion technology and information feedback system by domestic and foreign scholars. Subsequently, in the method section, a brief introduction is made to the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, and the weight coefficient fusion method involved in multi-sensor information fusion, DS evidence theory algorithm and Kalman are explained Filtering algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, data recording analysis, and related discussions on this system. The innovation of this paper is that it combines multi-sensor information fusion with data collection and feedback system, and it is applied to track and field sports, which is of great significance to the development of my country's sports industry and the improvement of athletes' own quality level.

2 Track and Field Training Information Collection and Feedback System Method Based on Multi-sensor Information Fusion

2.1 Athletics

Track and field sports include running, race walking, high jump, long jump, shot put, hammer throw, javelin and other sports [4]. Track and field is one of the ancient sports with a long history, and it is also one of the most popular sports in today's society. At present, the athletics level of track and field sports is very high. Many events are close to the limit of human beings. If you want to compete for gold and silver in the world competitions, you must have a deep grasp of the characteristics of the event and the theory of training competition. In other words, it is not only necessary to practice under the guidance of scientific theories, but also to be at the forefront of scientific research, in order to obtain the improvement of competitive ability. In a compound coaching team, the role of the research coach is to provide theoretical information and training suggestions [5]. In recent years, my country’s track and field sports have developed at a relatively fast pace. Excellent results have been achieved in men's sprints, sprints, high jumps, long jumps, triple jumps, race walking, women's race walking, and throwing. Among them are the contributions of scientific researchers.

2.2 Information Collection and Feedback

Movement information collection and feedback refers to the use of certain means to track and capture the human body's movement trajectory, obtain some of the parameters, and analyze and process these parameters, so as to draw the required data and conclusions to improve the level of exercise [6, 7]. For sports information collection, currently existing methods mainly include the following. Optical measurement method uses optical methods to collect human body motion information, mainly including high-speed photography, video recording, and photoelectric detection; non-electrical electrical measurement, which mainly uses sensors or sensing elements to be installed in The human body converts the mechanical motion of the human body into electricity and performs quantitative measurement; the bioelectric signal measurement method, modern research has shown that any behavior process of the human body will produce the corresponding bioelectric signal, this method is to use the electrode installed on the surface of the human body to collect EMG signal to analyze sports behavior [8]. Information feedback is a very important interactive feature to give users correct guidance information and help users make judgments and decisions. The forms of feedback are also multifaceted. Visual, auditory, tactile, positive, and negative are all conveying information to users. Good information feedback sometimes mobilizes the enthusiasm of users unexpectedly and gives users a sense of control.

2.3 Multi-sensor

(1) Multi-sensor definition

In a multi-sensor information system, multi-sensor information has various manifestations and huge information capacity. The correlation between various types of multi-sensor information is complicated, and the timeliness of information collection and processing is very high. This requires a An effective method is to collect and process the multi-sensor information obtained by the multi-level sensors in the multi-sensor system. Through the coordination and performance complementation among the multi-sensors, a comprehensive and correct understanding of the monitoring (detection target) object can be quickly and effectively obtained, and the multi-sensor information the acquisition technology is therefore produced. It makes full use of the complementarity of multi-source data and the scalability of computer interface resources to improve the quality and efficiency of multi-sensor information acquisition, which has important practical application research value [9, 10].

The types of multi-sensor information in the system mainly include radar information, photoelectric information, ground sensor information, and navigation and positioning information. The multi-sensor information acquisition system acquires various types of multi-sensor information in real time by adapting to different types of sensor acquisition interfaces, which the system needs to process The target types mainly include armed personnel, unarmed personnel, wheeled vehicles, tracked vehicles, ships, etc. [11]. The schematic diagram of multi-sensor information acquisition and access is shown in Fig. 1.

Fig. 1
figure 1

Schematic diagram of multi-sensor information collection(Pictures from Baidu picture)

(2) Multi-sensor information fusion

People are born with the ability to perceive the surrounding things based on the various organs of the business, such as eyes, ears, nose, touch, combined with previous experience and knowledge, the brain processes and analyzes the information obtained, and finally judges the characteristics of things or decision-making results, and issues instructions Ability [12], as shown in Fig. 2.

Fig. 2
figure 2

Information fusion theory (pictures from Baidu picture)

Multi-sensor information fusion is actually a process that imitates humans. Sensors are like human sensory organs, which acquire valuable information required by the system. This process is similar to that human sensory organs contain or otherwise affect the surroundings. The fusion center imitates the human brain and uses prior knowledge to comprehensively process the complex information obtained by multiple sensors according to certain combination rules, reasoning and analysis, and obtain a consistent description and explanation of the observations. This is multi-sensor the principle of information fusion [13, 14].

Data layer fusion refers to the fusion of raw data collected by independent sensors. These data are merged without processing, so it is also called pixel-level fusion. Data layer fusion is a low-level fusion model. The characteristics of data layer fusion are: in the data layer fusion, all sensors must be of the same magnitude, so that the original field data can be retained to the greatest extent, and the subtle data that other fusions cannot provide can be provided. However, due to the huge amount of collected data, the large amount of data to be processed, and the earth increase the burden of the processor, resulting in long processing time and poor adaptability [15].

2.4 Multi-sensor Information Fusion Method

(1) Weight coefficient fusion method

The weighted coefficient fusion method is also called the weighted average fusion method. This fusion method is the simplest, and the processing of real-time information is also the most intuitive [16]. The weighted fusion algorithm formula of n sensor detection system is:

$$ \overline{Z}=\sum \limits_{j=1}^k{z}_j{y}_j $$

Among them, zjrepresents the output data of the jth sensor, represents the weighted value of the jth sensor, or called the weight, and \( \overline{Z} \)represents the weighted average fusion result. The weighted fusion method weights the output of each sensor, and the final result is the fusion value. The premise of this method is to do a comprehensive analysis of the detection system and sensors, and to determine the appropriate weight ratio of each sensor [17, 18].

(2) D-S evidence theory algorithm

D-S evidence theory is a complete theory to deal with uncertainty. It can not only emphasize the objectivity of things, but also emphasize the subjectivity of human estimation of things, so as to judge whether the hypothesis is valid. Evidence theory does not require prior information and conditional probability, and is suitable for fusion systems that contain ignorance and generate uncertainty, and is an intelligent method for the expression of uncertain information [19].

The D-S evidence theory fusion algorithm consists of the following parts:

Identification framework: A is a mutually exclusive non-empty finite set. It contains all possible assumptions Skfor judging an event. If there is a recognition framework, it can be expressed as:

$$ A=\left\{{S}_1,{S}_2,\dots, {S}_n\right\} $$

The basic probability distribution function is also called the b function, which satisfies

$$ b\left(\Phi \right)=0 $$
$$ \sum \limits_{i\subseteq A}b(I)=1 $$

The confidence function calculates the lower limit of the conclusion interval [20], which is defined as

$$ Bel(C)=\sum \limits_{D\subseteq C}m(D) $$

The likelihood function calculates the upper limit of the conclusion interval [21], which is defined as

$$ Pl(C)=\sum \limits_{C\cap D=\Phi}m(D) $$

(3) Kalman filter algorithm

Kalman is applied to the field of multi-sensor information fusion, which is suitable for dynamic environment operation and fusion of redundant information [22]. The advantage of Kalman filter is that it not only filters out the noise of the measurement signal, but also combines the previous estimation, which is proved to be the best estimation in the linear problem. The disadvantage is that only linear process models and measurement models can be accurately estimated, and the optimal estimation effect cannot be achieved in non-linear scenarios. Assume that the system equation of a linear discrete system is:

$$ {L}_t={\Phi}_{t,t-1}{L}_{t-1}+{\Gamma}_{t-1}{K}_{t-1} $$

The measurement equation is:

$$ {B}_t={M}_t{L}_t+{Z}_t $$

Among them, Ltrepresents the system state variable, Φt, t − 1represents the state transition matrix, Mtrepresents the system observation matrix, Btrepresents the system observation vector, Ktand Ztrepresents the process noise and measurement noise respectively. At the same time, the process noise Ktand the measurement noise Ztare both zero-mean Gaussian white noise without mutual interference [23], and satisfy

$$ F\left[{K}_t{Z_n}^D\right]=0 $$
$$ F\left[{Z}_t\right]=0,F\left[{Z}_t{Z_n}^D\right]={S}_t{\gamma}_{tn} $$
$$ F\left[{K}_t\right]=0,F\left[{K}_t{K_n}^D\right]={H}_t{\gamma}_{tn} $$

Where Htis the non-positive definite variance matrix of the process noiseKt,Stis the positive definite variance matrix of the measured noiseZt, andγtn is the Kronecker function.

The rigorous derivation of the Kalman filter equation can be achieved by orthogonal projection, innovation theory and Bayesian estimation [24, 25]. Here are the five steps of the Kalman filter algorithm directly, as shown below, and \( \overset{\wedge }{L_t} \) represents the estimation of Lt.

First, the state is further predicted

$$ \overset{\wedge }{L_{t/t-1}}={\Phi}_{t,t-1}\overset{\wedge }{L_{t-1}} $$

Second, the mean square error of prediction

$$ {V}_{t/t-1}={\Phi}_{t,t-1}{V}_{k-1}{\Phi_{t,t-1}}^D+{\Gamma}_{t-1}{H}_{k-1}{\Gamma_{t-1}}^D $$

Third, filter gain update

$$ {T}_t={V}_{t/t-1}{M}_t^D{\left({M}_t{V}_{t/t-1}{M}_t^D+{S}_t\right)}^{-1} $$

Fourth, state estimation

$$ \overset{\wedge }{L_t}=\overset{\wedge }{L_{t/t-1}}+{T}_t\left({B}_t-{M}_t\overset{\wedge }{L_{t/t-1}}\right) $$

Fifth, estimate the mean square error

$$ {V}_t=\left(1-{T}_t{M}_t\right){V}_{t/t-1} $$

3 Track and field training information collection and feedback system experiment based on multi-sensor information fusion

3.1 Experimental Design

For track and field training information, there are a variety of acquisition and feedback systems on the market, such as using high-speed image analysis systems to obtain information during training, or setting sensors on the runway for real-time monitoring, and accurate data such as contact between the soles of the feet and the runway and time. Obtain various indicators of training [26].

In this design, the motion sensing part of the signal acquisition module is composed of three categories of sensors, namely: three-axis gyroscope sensor to measure rotational angular velocity, three-axis acceleration sensor to measure linear acceleration, and three-axis electronic compass to measure the direction of true north Declination. The selected sensor model is shown in Table 1.

Table 1 Sensor model table

A typical multi-sensor information acquisition system has a three-level hierarchical structure: several monitoring stations (including monitoring front-ends), multiple monitoring sub-centers (regional level), and one monitoring center (global level); multiple monitoring centers can also be up one Layers gather to form a higher level of surveillance center. A single monitoring station is connected to several multi-sensors for monitoring. The types of multi-sensors include radar sensors, photoelectric sensors, and ground sensors. Because the multi-level multi-sensor information acquisition system above three levels is the same as the three-level multi-sensor information acquisition system at the structural level.

In this experiment, a track and field training information collection and feedback system is designed based on multi-sensor information fusion. A three-axis gyroscope, accelerometer, and electronic compass are used as information collection sensors, combined with gait feature extraction, where gait refers to the actions of walking, running, and standing. When an athlete moves, different signals are generated on the three sensors, and the corresponding signals of the sensors are used to detect the type of human movement. This chapter will analyze the human motion signal based on the data collected by the sensor, and then derive the motion feature extraction and recognition algorithm in the system according to the frequency distribution and stability of the signal, and lay the foundation for subsequent system development, as shown in Fig. 3. The mode of the system.

Fig. 3
figure 3

Schematic diagram of the hierarchical structure of the multi-sensor information acquisition system

3.2 Experimental Subjects

Twenty athletes (including 10 male athletes and 10 female athletes) perform various track and field sports, including 50-meter running, 2 km running, race walking, standing long jump, shot throwing, 110-meter hurdles, etc. All athletes are in good physical condition and have no recurrence of injuries. Athletes must have physical qualities such as strength, speed, endurance, agility, coordination and flexibility.

3.3 Experimental Method

The traditional high-speed image analysis system and the track and field training information collection and feedback system of multi-sensor information fusion are used for comparison. For the convenience of wearing during the experiment, the sensor is placed in a position that is vertically above the heel. Placing this position can make the experiment effect more accurate, and the designed sensor has a smaller mass, which has almost no effect on the athlete's training. Before the experiment, the slave machine of the motion information acquisition system needs to be installed on the point to be measured on the human body.

3.4 Experimental Procedure

  1. (1).

    Install multiple sensors in the athlete's sports shoes.

  2. (2).

    Turn on the handheld terminal of the system and set the distance of each training session, the number of athletes to be monitored, the test mode and other related parameters.

  3. (3).

    Turn on the left and right foot signal collectors and check whether the connecting wires are normal.

  4. (4).

    Install a signal collector on the outside of the sports shoes, and connect the collector with the sensor.

  5. (5).

    The athlete starts to exercise.

  6. (6).

    The trainer uses the handheld terminal to set up the nodes, and at the same time sends instructions to the sensors, divides the collected signals according to the time nodes, and sends relevant data.

  7. (7).

    The handheld terminal receives the data packet, performs processing and analysis, and displays the result.

  8. (8).

    After the training is completed, remove the left and right foot signal collectors on the outside of the sports shoes.

  9. (9).

    Connect the signal collector to the handheld terminal, and connect the handheld terminal to the computer via USB. Import the data into the database for analysis, and generate data reports and images.

4 Results and discussion

4.1 Demand Analysis

Because there is no scientific record and feedback on the training data of track and field athletes, the athletes will have certain injuries. Injuries are mostly divided into three types: acute injury, chronic injury and acute to chronic injury. In general, acute injury occurs when the limbs are exerted excessively in a short period of time. The symptoms are more obvious, but the recovery time is faster. For example, in the long jump, once the landing on one leg is too strong, it will cause ligament strain. When chronic injuries occur, the injured parts of the body are generally caused by repeated injuries. This kind of damage has a long incubation time, and it will aggravate sharply if it is not treated in the later stage. The most severe acute injury mostly occurs when the chronic injury is severe, that is, when the body's possession is subjected to the most violent impact. When athletes' training arrangements are not reasonable enough, injuries are prone to occur.

It can be seen from Fig. 4 that the sports injuries of running events in track and field sports are concentrated in chronic injuries, while the incidence of acute injuries is relatively the least. Among them, in running events, acute injuries accounted for 19.6%, chronic injuries accounted for 45.4%, and acute to chronic injuries accounted for 35.0%. In jumping events, acute injuries accounted for the largest proportion at 48.9%, followed by acute to chronic injuries, accounting for 28.6%, and chronic injuries accounting for the smallest proportion at 22.5%. In throwing events, the main types are acute to chronic. It can be seen from the above data that there are obvious differences in the nature of sports injuries in different events, so it is very important for the recording, collection and feedback of track and field sports data.

Fig. 4
figure 4

The nature of sports injury of track and field athletes

4.2 Comparative Analysis

The 50m run mainly tests the speed and explosiveness of the athletes. In order to ensure the smooth progress of the experiment, before the experiment, the difference in the 50m performance of the two groups of athletes was tested, and it showed that P>0.06. It can be seen that there is no significant difference between the two groups before the experiment. As shown in Table 2 is a statistical diagram of the comparison of 50m running before and after the experiment.

Table 2 Comparison table of 50m running results before and after the experiment

It can be seen from Table 2 that the data collection and feedback system based on multi-sensor information fusion has improved the training level of athletes through the 8-week operation. From the data in the table, the average performance of the two groups of athletes in the 50m run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine.

The following 8 weeks of content four groups of athletes for the 50-meter running, standing long jump, shot throwing and race walking the four content of training.

After 8 weeks of experiments, the physical indicators of the control group and the experimental group have been effectively improved. After using this information collection and feedback system, it is of great help to the athletes' own quality. It can be seen from Fig. 5 that the improvement of the physical fitness of the athletes in the experimental group is higher than that in the control group. In the 50-meter race, male athletes in the experimental group increased by 7%, while the average performance of male athletes in the control group increased by 6%. And it can be seen from Fig. 4 that in most track and field sports, the average performance of male athletes is higher than that of female athletes. Because of the differences in physical fitness between male and female athletes, their performance in different events is different. Except for shot put, the reason is that most female athletes are more explosive. The adaptability is stronger.

Fig. 5
figure 5

Changes in various physical fitness

Figure 6 is the development diagram of the performance changes of the two groups of experimental subjects in eight weeks. Whether it is the traditional high-speed image analysis system or the multi-sensor information fusion track and field training information collection and feedback system used in this study, it is significant in the first week. The performance of the athletes has been improved. This is because the athletes can quickly discover their own shortcomings and shortcomings by using assistive technology in the first week, and the training program formulated by the coach is more scientific and effective. In the second week, there was a downward trend. The male control group even dropped 2.4% in performance, the female experimental group fell 1.6%, the control group fell 1.9%, and the male experimental group was the most stable, only dropping 1.2%. This is because no matter which data collection method is used, some errors will inevitably occur when it is first put into use. It may be because the stability of the machine is not high, or the installation position is wrong, etc.. But starting from the third week, the average scores of the two groups of subjects began to rise and were relatively stable. It can be clearly seen from Fig. 6 that the experimental group based on multi-sensor information fusion improves the quality of athletes better than the control group.

Fig. 6
figure 6

Athletes' eight-week overall fixed-base growth trend chart

4.3 Data Record Analysis

Here, a data collection and feedback system using multi-sensor information fusion for men's 2km running is analyzed. The first issue in the men's 2km race is to complete the entire race. It can be seen from Fig. 7 that the speeds of the five athletes in the first three segments (600m) are exactly the same, and differentiation begins to appear in the fourth segment. At this stage, the speed of the top three athletes was significantly faster than the fourth and fifth athletes, and after this segment, the speed of these five athletes all began to increase. The first, second and last athletes reached the highest speeds in the ninth segment, 5.82 meters per second, 5.65 meters per second and 5.6 meters per second respectively. The third and fourth runners were in the eighth segment. The highest speed is 5.68 meters per second and 5.59 meters per second respectively. From these data, it can be seen that there are three decisive factors for the men's 2 km race: the complete distance; the explosive ability after 1.4km: keeping the lead at the beginning is more advantageous than accelerating later.

Fig. 7
figure 7

Men's 2km race top 5 segment speed chart

It can be seen from Table 3 that when athletes do the same action, they have great similarities in the energy distribution. Even different people have similar energy distribution characteristics, but there are certain differences in the energy distribution of different actions. For example, in Band 1, the walking motion is basically 0.3-0.4. However, the movement of running is at 0.2-0.3.

Table 3 Athlete movement characteristic energy distribution

As shown in Figure 8, in the walking state, the energy is mainly distributed in the low frequency band, and the energy is higher in the frequency bands 1, 2, and 4. When the athlete runs, the energy begins to move to the high frequency band, and the high frequency components decomposed by the wavelet packet begin to become larger, and the energy begins to increase significantly in the three frequency bands of 3.5.7. By analyzing the energy distribution of athletes' different actions, it shows that the information collection system of multi-sensor information fusion can give feedback to the athlete's state, so as to better adjust the sports state and replenish energy in time.

Fig. 8
figure 8

Energy distribution in different training state

Table 4 shows the error analysis of the multi-sensor information collection system on a 110-meter straight track. After several times of segmented data recording, it can be seen that the maximum relative error is 0.43%, the minimum is 0.07%, and the overall error does not exceed 0.5%. The main reason for this error is that the acquisition frequency of the sensor acquisition unit is occasionally slightly lower, and the speed change process cannot be completely acquired, but this is a normal error and can be ignored.

Table 4 Statistics of experiment data of 110-meter straight track

4.4 Discussion

In the above analysis, this research analyzes the needs of information collection and feedback systems using multi-sensor information fusion. At present, there are indeed many athletes in the sports industry who have retired early from their sports career due to acute or chronic injuries. Through the analysis of sports injuries of athletes, the attention of the sports industry to the realization of scientific training of athletes can be increased. In the subsequent comparative analysis, this study compared the traditional image recording method with the system designed in this study for comparative analysis. By recording the athlete’s physical improvement in different types of sports and the development of their performance, it can be seen that the training information The accurate records of the coaches really help the coaches to prescribe the right medicine for the training of these athletes, and significantly improve the training efficiency of the athletes, and avoid injuries caused by unscientific training methods. Finally, this study has done a certain analysis of the system’s own situation. By recording the speed growth of men’s 2,000-meter running, it can help athletes and coaches understand the determinants of athletes’ desire to achieve high rankings or improve their level in a short period of time. And this system can also record the energy distribution of athletes in different bands, which helps athletes adjust their state in time.

Similarly, this study also has some shortcomings. In the selection of experimental subjects, due to the limitations of funds and ability, the selected subject sample is not large. In the analysis part, there is not much analysis on the performance of the multi-sensor itself, and the analysis should be combined with filtering. I hope that with the following in-depth study of multi-sensor information fusion, I can have a more comprehensive understanding of this content, so as to further improve the research of this article.

5 Conclusion

Nowadays, the importance of science and technology in promoting the development of sports has become increasingly prominent. With the rapid development of computer technology, intelligent sports based on digitization, networking as the condition, and intelligence as the core are becoming a hot spot. Collect and analyze data to help athletes train scientifically and improve their sports level, and assist coaches and managers to guide and manage. This paper designs a data collection and feedback system based on multi-sensor information fusion, and conducts data collection and feedback for track and field training. In the method section, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, and clarifies the weight coefficient fusion method, DS evidence theory algorithm and Kalman involved in multi-sensor information fusion Filtering algorithm. This article designs an information collection and feedback system based on multi-sensor information fusion, and analyzes this system, and concludes that using this system does help athletes improve their training efficiency and their own level. By designing such a system, scientific calculation and analysis of the athlete’s actual state and potential can be made, and reasonable and effective predictions can be made. This can not only improve the efficiency of coaches, but also ensure that athletes receive scientific guidance and training, and avoid manpower. Waste of material resources.

Availability of data and materials

Please contact the author for a data request.

Change history





Heart Rate Variability


  1. M. Liu, J. Chen, X. Zhao, et al., Dynamic obstacle detection based on multi-sensor information fusion. IFAC-PapersOnLine 51(17), 861–865 (2018)

    Article  Google Scholar 

  2. N.H. Rijken, R. Soer, E. Maar, et al., Increasing Performance of Professional Soccer Players and Elite Track and Field Athletes with Peak Performance Training and Biofeedback: A Pilot Study. Appl Psychophysiol Biofeedback 41(4), 421–430 (2016)

    Article  Google Scholar 

  3. T. Kitazawa, M. Nagai, J. Ueno, Effects of an E-Learning System with a Feedback System in Blended Learning Environments of Information and Communication Technology Education at a Japanese University. Educ Technol Res 34(1), 181–193 (2017)

    Google Scholar 

  4. Xiangyu, Xu, Mei et al., An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion. J Comput Commun 05(3), 102–115 (2017)

    Article  Google Scholar 

  5. B.L. Han, T. Zhang, Q.S. Luo, et al., Obstacle avoidance technology of bionic quadruped robot based on multi-sensor information fusion. J Beijing Inst Technol (English Edition) 25(4), 448–454 (2016)

    Google Scholar 

  6. C. Zong, Z. Ji, Y. Yu, et al., Research on Obstacle Avoidance Method for Mobile Robot Based on Multisensor Information Fusion. Sensors Mater 32(4), 1159–1170 (2020)

    Article  Google Scholar 

  7. Y. Jian, R. Qie, T. Li, et al., Nondestructive Detection Method of Egg Quality Based on Multi-Sensor Information Fusion Technology. J Comput Theor Nanosci 13(9), 5932–5937 (2016)

    Article  Google Scholar 

  8. P. Gong, Y. Fei, L. Song, Road Recognition Method of Wheel-Tracked Robot Based on Multi-sensor Information Fusion. Shanghai Jiaotong Daxue Xuebao J Shanghai Jiaotong Univ 51(4), 398–402 (2017)

    Google Scholar 

  9. M. Mosalanejad, M.M. Arefi, UKF-based soft sensor design for joint estimation of chemical processes with multi-sensor information fusion and infrequent measurements. Sci Meas Technol IET 12(6), 755–763 (2018)

    Article  Google Scholar 

  10. C. Yuan, Y. Yang, Y. Liu, Sports decision-making model based on data mining and neural network. Neural Comput Applic 33, 3911–3924 (2021)

    Article  Google Scholar 

  11. K.R. Hirsch, A.E. Smith-Ryan, E.T. Trexler, et al., Body Composition and Muscle Characteristics of Division I Track and Field Athletes. J Strength Cond Res 30(5), 1231–1238 (2016)

    Article  Google Scholar 

  12. S. Tenqchen, Y.J. Su, K.P. Chen, Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor -the Case Study. Artif Intell Eng 01(2), 38–43 (2019)

    Google Scholar 

  13. C.C. Lo, Y.H. Kuo, Traffic-Aware Routing Protocol with Cooperative Coverage-Oriented Information Collection Method for VANET. IET Commun 11(3), 444–450 (2017)

    Article  Google Scholar 

  14. Z. Huang, C. Chen, M. Pan, Multiobjective UAV Path Planning for Emergency Information Collection and Transmission. IEEE Internet Things J 7(8), 6993–7009 (2020)

    Article  Google Scholar 

  15. H. Liang, Evaluation of fitness state of sports training based on self-organizing neural network. Neural Comput Applic 33, 3953–3965 (2021)

    Article  Google Scholar 

  16. L. Bickman, S.R. Douglas, A.R.V. De Andrade, et al., Implementing a Measurement Feedback System: A Tale of Two Sites. Admin Pol Ment Health 43(3), 410–425 (2016)

    Article  Google Scholar 

  17. A.A. Gleacher, S.S. Olin, E. Nadeem, et al., Implementing a Measurement Feedback System in Community Mental Health Clinics: A Case Study of Multilevel Barriers and Facilitators. Admin Pol Ment Health 43(3), 426–440 (2016)

    Article  Google Scholar 

  18. M. Kuzlu, C. Fair, O. Guler, Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity. Discov Internet Things 1, 7 (2021)

    Article  Google Scholar 

  19. V. Nguyen, D. Ellerbusch, J. James, et al., SU-E-J-196: Implementation of An In-House Visual Feedback System for Motion Management During Radiation Therapy. J Appl Clin Med Phys 17(1), 421–427 (2016)

    Article  Google Scholar 

  20. G. Jorge-Botana, Luzon, et al., Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system. Inf Process Manag 52(3), 359–373 (2016)

    Article  Google Scholar 

  21. U. Jan-Friedrich Ehlenbröker, L.V. Mönks, Sensor defect detection in multisensor information fusion. J Sensors Sensor Syst 5(2), 337–353 (2016)

    Article  Google Scholar 

  22. B.J. Liu, Q.W. Yang, W.U. Xiang, et al., Application of Multi-sensor Information Fusion in the Fault Diagnosis of Hydraulic System. Int J Plant Eng Manage 22(01), 12–20 (2017)

    Google Scholar 

  23. P. Braca, R. Goldhahn, G. Ferri, et al., Distributed Information Fusion in Multistatic Sensor Networks for Underwater Surveillance. IEEE Sensors J 16(11), 4003–4014 (2016)

    Article  Google Scholar 

  24. L. Peng, L. Bo, Z. Wen, et al., Predicting Drug–Target Interactions With Multi-Information Fusion. IEEE J Biomed Health Inform 21(2), 561–572 (2017)

    Article  Google Scholar 

  25. P. Marimuthu, V. Perumal, V. Vijayakumar, Intelligent Personalized Abnormality Detection for Remote Health Monitoring. Int J Intell Inf Technol 16(2), 87–109 (2020)

    Article  Google Scholar 

  26. Y. Ding, P. Zhan, Y. Zhou, et al., Design and experiment of motion controller for information collection platform in field with Beidou positioning. Nongye Gongcheng Xuebao Trans Chin Soc Agric Eng 33(12), 178–185 (2017)

    Google Scholar 

Download references


The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations



Ling Li: Writing - editing Data curation. Chengliang Li: data analysis, Investigation. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Chengliang Li.

Ethics declarations

Ethics approval and consent to participate

The 20 athletes in this experiment volunteered to participate in this experiment.

Ethical approval. All procedures performed in research involving human participants comply with the ethical standards of the institution and/or the National Research Council, and comply with the 1964 Declaration of Helsinki and its subsequent amendments or similar ethical standards.

Consent for publication

The external pictures involved in this article are all from Baidu pictures and have been approved by the author.

Competing interests

The author declares no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail:

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, L., Li, C. RETRACTED ARTICLE: Design and Implementation of Track and Field Training Information Collection and Feedback System Based on Multi-sensor Information Fusion. EURASIP J. Adv. Signal Process. 2021, 51 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: