Analysis and design of dual-feature fusion neural network for sports injury estimation model

The intelligent football players' motion recognition system based on convolutional neural network and big data

This article focuses on evaluating the efficacy of intelligent image processing techniques using deep learning algorithms in the context of football, to present pragmatic solutions for enhancing the functional strength training of football players. The article commences by delving into the prevailing research landscape concerning image recognition in football. It then embarks on a comprehensive examination of the prevailing landscape in soccer image recognition research. Subsequently, a novel soccer image classification model is meticulously crafted through the fusion of Space-Time Graph Neural Network (STGNN) and Bi-directional Long Short-Term Memory (BiLSTM). The devised model introduces the potency of STGNN to extract spatial features from sequences of images, adeptly harnessing spatial information through judiciously integrated graph convolutional layers. These layers are further bolstered by the infusion of graph attention modules and channel attention modules, working in tandem to amplify salient information within distinct channels. Concurrently, the temporal dimension is adroitly addressed by the incorporation of BiLSTM, effectively capturing the temporal dynamics inherent in image sequences. Rigorous simulation analyses are conducted to gauge the prowess of this model. The empirical outcomes resoundingly affirm the potency of the proposed deep hybrid attention network model in the realm of soccer image processing tasks. In the arena of action recognition and classification, this model emerges as a paragon of performance enhancement. Impressively, the model notched an accuracy of 94.34 %, precision of 92.35 %, recall of 90.44 %, and F1-score of 89.22 %. Further scrutiny of the model's image recognition capabilities unveils its proficiency in extracting comprehensive features and maintaining stable recognition performance when applied to football images. Consequently, the football intelligent image processing model based on deep hybrid attention networks, as formulated within this article, attains high recognition accuracy and demonstrates consistent recognition performance. These findings offer invaluable insights for injury prevention and personalized skill enhancement in the training of football players.

An Overview of Machine Learning Applications in Sports Injury Prediction

Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.

Recurrent Neural Model to Analyze the Effect of Physical Training and Treatment in Relation to Sports Injuries

Artificial intelligence has rapidly grown and has made the scenario that no field can function without it. Like every field, it also plays a vital role in the sports field nowadays. In certain sports, injuries happen very often due to heavy training and sudden speedy actions, especially in athletics and football. Here arises a need to analyze the effect of physical training in sportsperson by collecting data from their daily training. With the help of artificial intelligence, a recurrent neural model is developed to analyze the effect of physical training and treatment concerning sports injury. A Recurrent Neural Network (RNN) can be a subsection of Artificial Neural Networks (ANN) that uses the neural nodes connected in a temporal sequence. The temporal sequence is one of the essential terms in this research, which denotes a data sequence of events in a given timeframe. The recurrent neural model is an intelligent machine learning method that comprises a neural schema replicating humans. This neural schema studies the data it collects from the athletes/players and processes it by analyzing previous injuries. Sports injuries have to be analyzed because, in some cases, it becomes more dangerous to the sportsperson that they may even lose their career due to disability. Sometimes it may cause a massive loss to the club or company that hired the sportsperson for the sport. The prediction process can give the player rest until he recovers, thus becoming the safest approach in sports. Therefore, it is essential to analyze the sportsperson's track data to keep an eye on his health. In this research, RNN model is compared with the existing Support Vector Machine (SVM) in concerning to the effect of physical training and treatment for sports. The results show that the proposed model has achieved 99% accuracy, which is higher than the existing algorithm.

Identification of Sports Athletes' High-Strength Sports Injuries Based on NMR

In order to study the high-strength sports injury in sports, this paper proposes a method based on NMR to identify the high-strength sports injury of sports athletes. This method carries out a questionnaire survey and research on the athletes who are excellent in sports dance major from 2019 to 2021 in the Institute of Physical Education. The athletes' age range is 18-25 years, and the training period of sports dance is 3-5 years. The results show that compared with other recognition methods, the recognition method based on NMR has higher accuracy and efficiency. The method of this study is helpful to improve the recognition efficiency and accuracy. Athletes are very easy to get injured during sports. In order to reduce the degree of injury of athletes, we should strictly follow the action standards in the training process to avoid serious injury.

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.

Intra-Articular Injection of PRP in the Treatment of Knee Osteoarthritis Using Big Data

Observing data on the characteristics of intra-articular injection of sodium citrate for knee osteoarthritis is an important reference value for human safety and evacuation design. To address the problems of slow data collection and poor accuracy of results of knee osteoarthritis behavior, under intensive conditions of intra-articular injection for knee osteoarthritis, this paper designs a data mining-based feature extraction system for intra-articular injection of sodium citrate for knee osteoarthritis. Using the Hadoop architecture, we extract the basic data of human behavior in the two-dimensional plane by storing and stitching the collected continuous data and discriminate the behavioral categories of knee osteoarthritis. We collected a real dataset from 84 patients with knee osteoarthritis treated in our hospital from October 2019 to October 2020. The dataset was divided into 42 patients in the tretinoin group and 42 patients in the sodium glutamate group according to the randomized number table method. The trimethoprim group was treated with intra-articular injection of trimethoprim, and the sodium citrate group was treated with intra-articular injection of sodium citrate. The clinical efficacy, joint mobility, intra-articular fluid volume, Lysholm score of knee joint, numerical pain intensity scale (NRS) score, and adverse effects of the two groups were compared before and after treatment. In our experiments, we observed that, compared with triamcinolone acetonide intra-articular injection, sodium hyaluronate intra-articular injection is more effective in the treatment of knee osteoarthritis. It can effectively improve knee function and reduce pain and adverse reactions.

Clinical Observation on the Treatment of Rotator Cuff Injury with Modified Buyang Huanwu Decoction and Rotator Cuff Repair

In this paper, we have evaluated the clinical efficacy of rotator cuff surgery combined with Chinese medicine Buyang Huanwu Decoction (adding medicine) in the treatment of patients with rotator cuff injury. For this purpose, sixty patients with rotator cuff injury and shoulder arthroscopic surgery were selected in this hospital (where 57 cases were finally screened). The patients were divided into a control group (28 cases) and a study group (29 cases) by the envelope method. The control group received conventional treatment after the operation, whereas the study group was combined with Buyang Huanwu Decoction after the operation. The clinical efficacy of the two groups, particularly after treatment, was compared in terms of self-care ability and Constant-Murley scores before and after treatment, that is, 4 w, 8 w, and 12 w. The total effective rate of treatment in the study group was significantly higher than that of the control group after 4 weeks of treatment (P < 0.05). There was no significant difference in the FIM self-care scores of the two groups before treatment (P > 0.05). In the study group patients, after treatment for 4 w and 8 w, the FIM self-care score was significantly improved (P < 0.05). The FIM self-care score of the patients in the study group, after 12 w of treatment, had no significant difference compared with the control group (P > 0.05). The Constant-Murley scores of the two groups were compared before treatment where no significant difference is observed (P > 0.05) and the Constant-Murley score of the study group patients was significantly higher than that of the control group, after 4 w and 8 w treatment (P < 0.05). Additionally, Constant-Murley score of the study group was not significantly higher than that of the control group after 12 w of treatment difference (P > 0.05). The proposed combined treatment program has value of promotion and implementation in the clinical treatment of patients with rotator cuff injury.