Abstract
With the concept of quality education put forward, students’ sports activities have received extensive attention from society. As a result, sports injuries among students during sports have also aroused widespread concern, so it is an irreversible trend to detect sports injuries. The development of multimedia intelligent 3D image technology also provides technical support for sports injury detection, which makes it possible to automatically detect sports injuries. In this paper, an automatic detection system for sports injuries was designed based on multimedia intelligent three-dimensional image technology, and the related content was evaluated. In the investigation of the parts of students’ sports injuries, it was concluded that the injury rate of the students’ ankle joints was the highest; in the investigation of the types of sports injuries among students, it was concluded that students were more likely to suffer from joint sprains; in the project investigation of students’ sports injuries, it was concluded that students were more prone to sports injuries in ball games with a large amount of exercise; in the investigation of the causes of students’ sports injuries, it was concluded that the main reasons for students’ sports injuries were physical insufficiency and a bad venue environment; in terms of the performance evaluation of the sports injury detection system, it was concluded that the accuracy, effectiveness, authenticity, and efficiency of the sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology had been improved to different degrees compared with the traditional sports injury detection methods. Therefore, the detection efficiency of the sports injury automatic detection system proposed in this paper was improved by 5.7% compared with the traditional sports injury detection method.
1. Introduction
The causes of sports injuries are complex and diverse, and the degree of sports injuries varies. The human eye alone cannot detect the cause and degree of a sports injury, which largely delays the timing of sports injury treatment, resulting in irreversible consequences. However, the development of technology has made sports injury detection more possible. Based on this, it is necessary to apply multimedia intelligent 3D image technology to the process of sports injury detection.
In the course of sports, sports injuries occur from time to time, which has aroused widespread concern among the public and led many scholars to launch investigations into them. Herdy et al. analyzed injuries and associations between injury-related variables in young football players in the under-20high-performance category. It was concluded that the incidence and characteristics of lesions in young football players in different categories were higher with an increasing number of games, and the older group showed more lesions that were more similar to those in adults [1]. Linklater et al. studied imaging of acute capsular ligament sports injuries of the ankle and foot and found that diagnostic imaging was helpful in assessing persistent symptoms in subacute or chronic settings [2]. Khalil et al. explored the role of magnetic resonance musculoskeletal imaging in the assessment of knee sports injuries [3]. Yasuhiro explored gender-related differences in lower extremity alignment, range of motion, and history of lower extremity sports injuries among Japanese college athletes. Results showed that women experience lower extremity sports injuries more than men and that a significant percentage of these injuries involve the foot or ankle [4]. Gogoi et al. developed a statistical model to predict running-related lower extremity injuries in athletes. The findings showed that increased incline range, incline range, and rotation range were associated with an increased likelihood of future running-related lower extremity injuries [5]. Khalil et al. investigated whether performing sports injury-related surgery during the COVID-19 pandemic would have negative effects or consequences and showed that patients who underwent surgery did not develop an infection or acquire coronavirus disease after discharge from the hospital [6]. Grek aimed to characterize the injuries and illnesses of athletes participating in the Lausanne 2020 Winter Youth Olympic Games from 9 to 22 January 2020. Results showed that athlete injury and morbidity rates were similar compared to the most recent Youth Olympic Games [7]. The more people know about sports injuries, the more they can effectively avoid them in their lives so as to maintain their health.
With the development of information technology, three-dimensional image technology has been widely used and applied in many fields. Bayrak et al. developed an evaluation method for dynamic depth perception without reference to an objective metric, and the results showed that this method can provide end users with a better 3D video experience in time for future Internet multimedia services [8]. Daniele Maria introduced a method for superimposing faces between 3D models, which provided a topographic map of the face area modified by growth. The results showed that the stacking protocol can provide reliable images of facial growth with high sensitivity [9]. Chaker et al. developed an interactive 3D tool that allowed the individual body to experience and enhance the spatial representation of musculoskeletal functional anatomy [10]. Jun et al. proposed a 3D segmented guidance scheme for multiple drones in order to guarantee the simultaneous arrival and field of view constraints of multiple drones [11]. Based on X-ray computed tomography, Hu et al. proposed an image analysis method to extract 22 3D grain features [12]. Chu et al. proposed a computing framework for personalized design, which enhanced the practical value of 3D anthropometric data by realizing the human-centered design concept [13]. Chen et al. proposed a new color reconstruction method to improve the color reconstruction challenge of missing spectral bands. The 3D imaging results showed that the new method improved the reliability of the missing color reconstruction effect in the spectral band, thus expanding the application range of hyperspectral lidar measurements [14]. There are many studies on 3D image technology, but no investigation on the automatic detection of sports injuries. Based on this, this paper investigated the application of 3D image technology in the automatic detection of sports injuries.
In order to shorten the detection time of sports injury recovery and improve the detection efficiency, this paper designs a sports injury automatic detection system based on the technology of multimedia intelligent 3D image processing. The sports injury automatic detection system was compared with the traditional sports injury detection method. The relevant performance tests were carried out, and the feasibility conclusion was put forward. This paper provided a theoretical and practical basis for the detection of sports injuries.
2. Multimedia Intelligent 3D Image Processing Technology
2.1. Algorithm of Multimedia Intelligent 3D Image Processing Technology
Multimedia intelligent 3D image processing technology refers to a type of computer-based image processing and analysis technology that is adaptive to various applications. When the parallel ray x passes through the image interface with thickness y, its intensity weakening process satisfies formula (1)
Among them,
In formula (2), and 0 represent the ray intensity before and after passing through the object, respectively. λ is the linear attenuation coefficient, and α is the straight line along the ray path.
The algorithm for nonlinear grayscale function is as follows:
According to the formulas (1) and (2), the data of the three-dimensional image can be obtained, and then it can be analyzed and processed.
2.2. Flow of Multimedia Intelligent 3D Image Processing
Multimedia intelligent 3D image processing technology can use 3D image data for analysis to ensure the accuracy and authenticity of image data [15]. The specific process of multimedia intelligent 3D image processing can be seen in Figure 1.
It can be seen from Figure 1 that the processing process of the multimedia 3D image is mainly divided into three modules: multimedia data acquisition, data correction and 3D reconstruction, and 3D image processing and detection. After the multimedia data collection is completed, the data need to be corrected and reconstructed in three dimensions, and then the three-dimensional image must be processed and detected. This is the last module of image processing. This part of the work includes 3D image analysis and processing, image information detection, multimedia 3D image reconstruction operations, 3D image artifact suppression, multimedia 3D image enhancement processing, 3D image segmentation, and 3D detection and analysis, and each work is carried out in turn.
3. Sports Injury Collection Based on Multimedia Intelligent Three-Dimensional Image Technology
3.1. Collection of Sports Injury Data
It is assumed that the known multidimensional motion feature is . Matrix is formed through damage identification, and there are
The 3D image damage determination process is as follows:
The moving image sequence used in this paper includes data points, and each moving image includes F frames. The points designed in this way can be represented by elements in the matrix as Wj:
Among them, Wj represents the j-th injury feature point of the sports injury picture.
The sports injury attitude acquisition method based on 3D image analysis designed in this paper marks the constructed 3D injury image for point injury [16]. After substituting it with the assumed kinematic structure matrix, the transformed high-order matrix can be expressed as
Among them, ωj is the variable difference of the parameter function.
The difference between Wj and Tj in the function can be represented by the optical link matrix DE4
3.2. Sports Injury Posture Collection
Based on the Bayesian formula, for the human multimedia image T to be analyzed, the posterior probability of the state parameter L to be estimated is (L|T)∝ (T| L) (L). The human pose estimation problem is described by formula (8)
In formula (8), (T| L) is used to describe the observation model of the human body after setting the image likelihood of the detection results of different parts, and (L) is the prior information of human kinematics.
Based on the prior information of human kinematics and the human tree model, the expression of (L) can be obtained as
In formula (9), li is used to describe a human body part, and l0 is used to describe the root part. (li, lj) is used to describe geometric constraints between different limb parts.
It is assumed that the damage data of the three-dimensional sports damage image is within n (a, t), and the damage posture is represented by the superimposed data c + y. The expression of the damage matrix is
In formula (10), a, t represent the marker value of sports injury.
After normalization, it can be obtained
The invariant moment of the sports injury judgment matrix is defined as
In order to avoid image interference and data oscillation, the data pose is transformed as
The realization process of the sports injury posture acquisition method based on 3D image analysis designed in this paper is shown in Figure 2.
4. Construction of an Automatic Detection System for Sports Injuries
People often suffer from sports injuries when participating in sports, and the sites and degrees of injury are different for different people. Therefore, different solutions should be taken for sports injuries affecting different groups, which cannot be judged by the naked eye [17]. Multimedia intelligent 3D image processing technology can detect in real time whether people who exercise suffer from sports injuries and can also find the root cause of sports injuries. It saves manpower and material resources to a large extent and realizes the intelligence and humanization of sports injury analysis. A sports injury automatic detection system is constructed based on multimedia, intelligent, three-dimensional image processing technology. The function of the sports injury automatic detection system is shown in Figure 3.
4.1. Monitor the Number of Sports Injuries
In conventional sports, exercise is often not the responsibility of one person but is carried out by a group [18]. Groups of people engaging in physical exercise would inevitably encounter bumps, frictions, and other phenomena, and it is difficult to determine the number of sports injuries based only on human observation. The use of multimedia intelligent three-dimensional image processing technology can capture the situation of sports in real time and determine the number of sports injuries. In this way, the monitoring of sports personnel can be realized, and the occurrence of sports injuries can be avoided.
4.2. Monitor the Degree of Sports Injuries
The traditional way to check the degree of a sports injury is to wait until the athlete perceives the pain of the sports injury and then check the degree of the sports injury. There is a time difference between them. If the athlete does not perceive the sports injury, it would lead to the deepening of the athlete’s sports injury, which may have irreversible consequences [19]. However, with the support of technology, the sports injury automatic detection system can analyze the sports injury degree of athletes in time. It would also remind the athletes in real time so that their injuries can be effectively controlled and the serious consequences caused by the further aggravation of sports injuries can be avoided.
4.3. Analysis of the Causes of Damage in Use
The sports injury automatic detection system can timely analyze the cause of the athlete’s sports injury and remind the athlete in time. This function helps the medical staff deal with the injury degree of the athlete so as to prescribe the right medicine and avoid delays in the doctor’s diagnosis and treatment of the patient.
4.4. Suggestions on Sports Injuries
Different treatment methods should be adopted for different causes of sports injuries. The sports injury automatic detection system can give targeted suggestions according to the causes of sports injuries [20]. This function brings great convenience to the sports lives of the masses and can effectively alleviate the phenomenon that the masses are unable to start when they are injured in sports.
5. Automatic Detection of Sports Injuries
In order to reduce or avoid the occurrence of sports injuries, this paper selected 264 students from the Physical Education College of University A as the research subjects. By means of questionnaires and interviews, the detection of sports injuries among the students in the College of Physical Education after using the automatic detection system for sports injuries based on multimedia intelligent three-dimensional image processing technology was investigated [21, 22]. Then, it was compared with the detection of students' sports injuries under the traditional detection mode, and the measured data were analyzed by Excel software [23, 24].
5.1. Basic Information of the Subjects of Investigation
In order to ensure the authenticity of the sports injury investigation and avoid the interference of gender and the number of people, this paper analyzed the basic situation of the surveyed subjects. The basic information of the respondents is shown in Table 1.
It can be seen from Table 1 that the sports injury rate of boys was close to 80%, while the sports injury rate of girls was 45.6%, and the difference between the two was close to 25%. The reason was that boys naturally like more active sports and are more prone to sports injuries. In addition, boys generally do not pay much attention to sports injuries, and they would inevitably suffer sports injuries, which would lead to the occurrence of adverse events.
5.2. The Site of the Student’s Sports Injury
Only by identifying the sports injuries of the students in time can the sports injuries of the students be dealt with. The sports injury automatic detection system constructed in this paper can detect the parts of students’ sports injuries and make statistics on the data of the students’ sports injuries. The data detected by the sports injury automatic detection system is shown in Figure 4.
It can be seen from Figure 4 that the injured parts of the students include the ankle joint, waist, hand, wrist joint, knee joint, thigh, foot, calf, shoulder joint, neck, elbow joint, hip joint, head, abdomen, brain, and internal organs. Among them, the proportion of students with ankle injuries was 24.4%, and the proportion of students with waist injuries was 10.3%; the proportion of students with hand injuries was 9.6%, and the proportion of students with wrist injuries was 6.4%; the proportion of students with knee injuries was 6.3%, and the proportion of students with thigh injuries was 5.6%; the proportion of students with foot injuries was 5.2%, and the proportion of students with calf injuries was 4.4%; the proportion of students with shoulder injuries was 4.3%, and the proportion of students with neck injuries was 4%; the proportion of students with elbow injuries was 3.9%, and the proportion of students with hip injuries was 3.8%; the proportion of students with head injuries was 3.5%, and the proportion of students with abdominal injuries was 3.2%; the proportion of students with brain injuries was 2.9%, and the proportion of students with visceral injuries was 2.2%. As a result, students had the highest rate of ankle injuries because most of the physical activity they engage in requires the use of the ankle, which can lead to ankle injuries if you are not careful.
5.3. Types of Sports Injuries of Students
The detection of the types of sports injuries that students sustain can help to understand the symptoms of these injuries. Targeted treatment according to its symptoms is of great significance to the rehabilitation of sports injuries. The types of sports injuries for students are shown in Figure 5.
It can be seen from Figure 5 that the probability of joint sprains in students’ sports activities was 23.4%, and the probability of muscle strains in students’ sports activities was 19.3%; the probability of scratches in students’ sports activities was 18.2%, and the probability of falls in students’ sports activities was 16.5%; the probability of contusions in students’ sports activities was 12.9%, and the probability of other sports injuries in students’ sports activities was 9.7%. From this, it can be concluded that students were more likely to suffer from joint sprains. Therefore, it is necessary to do warm-up exercises during student training and sports to prevent joint sprains.
5.4. Projects for Sports Injuries of Students
Understanding which sports students are more likely to suffer from sports injuries can find out the root cause of sports injuries so as to effectively avoid sports injuries. This paper investigated the projects in which students suffer from sports injuries, and the results are shown in Figure 6.
It can be seen from Figure 6 that the probability of students participating in basketball activities and suffering sports injuries was 26.45%, and the probability of students suffering sports injuries in football activities was 23.64%; the probability of students suffering sports injuries in track and field activities was 20.31%, and the probability of students suffering sports injuries in volleyball activities was 16.54%; the probability of students suffering sports injuries in tennis activities was 8.39%, and the probability of students suffering sports injuries in other activities was 4.67%. To sum up, students were more prone to sports injuries in ball games with a large amount of exercise. Therefore, when playing ball games, special attention should be paid to the protection of the body to avoid bumps and sports injuries.
5.5. Causes of Sports Injuries of Students
There are many reasons for sports injuries. Only by identifying the root cause of sports injuries can the occurrence of sports injuries be effectively avoided, providing better protection for athletes. Based on this, this paper investigates the causes of students’ sports injuries, and the survey results are shown in Figure 7.
From Figure 7, it can be concluded that the probability of sports injury due to physical exhaustion was 23.42%, the probability of sports injury due to poor venue conditions was 22.01%, the probability of sports injury caused by difficult technical movements was 18.64%, the probability of sports injury caused by being kicked by the ball was 15.24%, the probability of sports injury caused by inappropriate sports equipment was 13.94%, and the probability of sports injury caused by other reasons was 6.75%. It can be seen that the main reasons for students to suffer from sports injuries were a lack of physical strength and a poor venue environment. Therefore, it is necessary to strengthen physical training for students and at the same time avoid excessive exercise. In addition, it is also necessary to improve the sports environment, and students need to be equipped with special sports venues for different sports.
5.6. Performance Evaluation of Sports Injury Detection System
In addition to testing the basic functions of the sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology, this paper also tested the sports injury automatic detection system. At the same time, it was compared with the traditional sports injury detection method, and the results are shown in Figure 8. A represents the traditional sports injury detection method, and B represents the sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology.
It can be seen from Figure 8 that the detection accuracy, effectiveness, authenticity, and efficiency of the sports injury automatic detection system based on multimedia intelligent 3D image processing technology have been improved to varying degrees compared with the traditional sports injury detection methods. Among them, the detection accuracy of the sports injury automatic detection system proposed in this paper was 9.3% higher than that of the traditional sports injury detection method; the detection effectiveness of the sports injury automatic detection system proposed in this paper was improved by 17.7% compared with the traditional sports injury detection method; the detection authenticity of the sports injury automatic detection system proposed in this paper was improved by 18.3% compared with the traditional sports injury detection method; and the detection efficiency of the sports injury automatic detection system proposed in this paper was improved by 5.7% compared with the traditional sports injury detection method. It can be concluded that the system performance of the sports injury automatic detection system based on the multimedia intelligent three-dimensional image processing technology has greatly improved.
6. Solutions for Sports Injuries
6.1. Strengthening of Safety Education
It is necessary to strengthen safety education for students and improve their awareness of self-protection. Sports injuries occur from time to time, so it is important to popularize the basic knowledge of sports injuries among students. It is necessary to teach students to identify the type and extent of sports injuries and to explain the causes of sports injuries. At the same time, it is also needed to teach students to adopt different solutions for different sports injuries so that they can start from themselves and avoid the occurrence of adverse events. By using examples to explain to students, they can truly feel the harm of sports injuries and the importance of preventing sports injuries so as to avoid sports injuries.
6.2. Warm up before Exercise
Students are required to warm up before engaging in physical activities to fully stretch their bodies and maintain a relaxed state. Different warm-up methods should be adopted for different parts, and large-scale, intense exercise should be avoided during the warm-up process. The intensity of the activity should be gradually increased from weak to strong.
6.3. Improvement of the Sports Environment
Improper sports environments often lead to student sports injuries, so it is necessary to improve the sports environment for students. Sports facilities with potential safety hazards in the sports environment should be removed; the runway should be paved with a special rubber track to avoid knee injuries; and the sports facilities used must meet national standards to prevent sports injuries caused by noncompliant sports equipment.
6.4. Reasonable Training Methods
The rationality of training methods affects the effectiveness and safety of training. The selection of scientific and rational training methods is an effective strategy to prevent sports injuries. The training of students should adopt reasonable training methods. It is necessary to carry out targeted training for students to prevent them from training beyond their own load, thereby causing sports injuries.
7. Conclusions
This paper designed a sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology and evaluated the basic functions and performance of the system. The following conclusions were drawn: the injury rate of the ankle joint of the students was the highest; the probability of a joint sprain in the sports activities of the students was the highest; students were more prone to sports injuries in ball games with a lot of exercises; the main reasons for students to suffer sports injuries were lack of physical strength and bad venue environment; and the detection accuracy, effectiveness, authenticity, and efficiency of the sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology had been improved to different degrees compared with the traditional sports injury detection methods. In summary, the use of this automatic sports injury detection system facilitated the detection of sports injuries in life and improved the detection efficiency of sports injuries.
Data Availability
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Conflicts of Interest
The author declares that he has no conflicts of interest.
Acknowledgments
This work was supported by the Research on the Cooperative Training Mechanism of Social Sports Professionals from the Integration of Industry and Education, the Research on the Guarantee Mechanism of the Precise Supply of Public Stadium Services under the Vision of Co-Construction, Co-Governance and Sharing (grant no. 22YBA066), and the Research on the Tort and Legal Protection of Sports Events Communication in the “One-Net across the Country” Era (grant no. 21A0505).