Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes

Li, Guanguan

doi:10.1155/2022/7792958

Cited by 3 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This adjustment process may involve heuristic rules or adaptive mechanisms based on the performance of candidate solutions. For example: If average fitness does not improve for a certain number of generations stated in equation (2) Increase mutation rate: 𝑝𝑚 = 𝑝𝑚 × 𝑓𝑎𝑐𝑡𝑜𝑟…”

Section: Figure 1: Flow Chart Of Evolutionary Algorithmmentioning

confidence: 99%

“…Optimizing athletes' training programs is a multifaceted endeavor that requires a deep understanding of various factors, including individual physiological characteristics, sport-specific demands, and long-term performance goals [1]. The first step in this process is to conduct a comprehensive assessment of the athlete, which may include evaluations of their strength, speed, agility, endurance, flexibility, and biomechanics [2]. This assessment helps identify strengths and weaknesses, allowing coaches and trainers to tailor the training program to address specific areas of improvement [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms

Li Zhang

2024

jes

View full text Add to dashboard Cite

The adjustment of optimizing athletes' training programs using machine learning involves leveraging data-driven approaches to enhance training regimens and performance outcomes for athletes. By analyzing various factors such as athletes' physiological data, training logs, performance metrics, and external conditions, machine learning algorithms can identify patterns, correlations, and optimal training strategies. These insights enable coaches and sports scientists to tailor training programs more effectively to individual athletes' needs, goals, and abilities. By continuously adapting and refining training plans based on real-time feedback and data analysis, machine learning helps optimize athletes' preparation, recovery, and overall performance, ultimately maximizing their potential and success in competitive sports. This paper explores novel methodologies and machine-learning techniques aimed at optimizing athletes' training programs. With the increasing demand for peak performance and injury prevention in sports, there is a growing need for data-driven approaches to tailor training regimens effectively. One such methodology, the Optimized Adjustment Evolutionary Computing Feature Selection (OA-EC-FS), is investigated for its ability to select relevant features crucial for enhancing athletes' performance across various sports disciplines. Additionally, machine learning algorithms are employed to classify athletes' training programs based on selected features, enabling coaches and trainers to make informed decisions to maximize performance outcomes.

show abstract

Section: Figure 1: Flow Chart Of Evolutionary Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms

Li Zhang

2024

jes

View full text Add to dashboard Cite

show abstract

“… Li (2022) provided that the extent of myocardial injury is studied with an intelligent optimization method. Comparing pre- and post-injury structural data can identify myocardial damage.…”

Section: Literature Surveymentioning

confidence: 99%

Research on the protection of athletes from injury by flexible conjugated materials in sports events

Liu,

Ren

2023

Front. Chem.

View full text Add to dashboard Cite

Sports are essential to everyone’s health because they assist athletes to establish physical and mental balance by strengthening muscles and ligaments. High-intensity training and low-quality equipment for sports tend to cause a wide range of injuries to the athlete. Higher education graduates’ regular education and lives are disrupted, either directly or indirectly, by sports injuries. Therefore, understanding the prevalence and root causes of college athletes’ injuries is crucial for enhancing student athletes’ performance and fostering healthy development. The ever-changing nature of injuries associated with sports and the patchy availability of rehabilitation facilities across India cause alarm. Inaccurately identifying players’ physical indications, uncomfortable clothing, and dissatisfaction with sports equipment are among the issues that can arise. The study investigates the potential of nanoparticles combined with sports flexible conjugate materials for injury prevention in athletes. The article proposed nanotechnology combined with flexible conjugated materials in sports events (Nano-FCM-SE) in sports training, explores the possibility of conjugated materials in enhancing the training effects of athletes, monitoring the status of sports, and bettering equipment. Sports equipment can help keep athletes safe by incorporating nanotechnology and flexible conjugated materials with superior optical, electrical, and other capabilities. Convenience, waterproof materials, flexibility, lightweight, aesthetics, breathability, and durability are evaluated for use in Nano-conjugated sports equipment materials. Evidence suggests that using flexible conjugated materials in athletic training can improve athlete performance and help the overall development of sports. The proposed method yields less negative results than MSI-TENG, TCM-MS, and RANSAC. The proposed damage severity model performs poorly relative to competitors (0.2). Compared to conventional models, the given models are effective on equipment. The sports injury protection system reported in this research has 5.17 percentage points greater detection efficiency than the current state of the art. Hierarchical strategies have the best RMSE for athlete safety. The findings of such methodologies in athlete safety on Nano conjugate materials and sports biology on sporting events and equipment underline the importance of precise data for athlete safety and performance.

show abstract

A study on the optimization of track and field training strategies under the integration of sports science and information technology

Huang,

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

With the development of sports science and information technology, how to use mining technology to analyze the correlation between various track and field training programs plays an important role in the improvement of track and field training. A decision support system for track and field training is constructed using the Apriori algorithm and decision tree ID3 algorithm. By mining and analyzing different sports data of track and field athletes, the system blends scientific training theory and advanced training methods to create a set of reasonable track and field training programs for athletes. Through empirical research, after the optimization of the track and field training decision support system, 72.2% of the athletes whose physical fitness test 30-meter run scores were between 80 and 100 had bar pull-up scores between 0 and 55. The average performance of students’ 3000-meter run improved by 81.35s.

show abstract

Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes

Cited by 3 publications

References 36 publications

Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms

Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms

Research on the protection of athletes from injury by flexible conjugated materials in sports events

A study on the optimization of track and field training strategies under the integration of sports science and information technology

Contact Info

Product

Resources

About