Running Pace Adjustment and Training Distance Fitting with Fuzzy Logic and Machine Learning

Dziomdziora, Adam; Taibi, Davide

doi:10.1109/iscit55906.2022.9931228

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Periodization involves dividing the training program into distinct phases, each focusing on different aspects of physical development and performance enhancement [4]. These phases typically include a preparatory phase, a competitive phase, and a transition phase, each with its own set of training objectives and intensity levels [5]. During the preparatory phase, the emphasis is on building a solid foundation of strength, endurance, and mobility, as well as addressing any imbalances or weaknesses identified during the assessment phase [6].…”

Section: Introductionmentioning

confidence: 99%

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

Li Zhang

2024

jes

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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.

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Section: Introductionmentioning

confidence: 99%

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

Li Zhang

2024

jes

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Fuzzy Decision Support Systems to Improve the Effectiveness of Training Programs in the Field of Sports Fitness

2024

Int J Comput Intell Syst

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To maximize training programs and improve individual performance, the sports fitness profession is always looking for new and innovative solutions. Fuzzy decision support systems provide a strong basis for improving training regimen efficacy through flexible and adaptive decision-making. In sports fitness, doing things differently might affect the way one can train and even lead to injury. Particularly if exercises call for expert training regimens, determining if fitness requirements are sufficiently satisfied is a difficult task. Athletes participate in training programs focusing on strengthening their bodies and minds to perform better. Each sport has unique needs, and athletes should prepare to meet those demands while still meeting their sport’s general fitness activity requirements. A novel strategy for assisting athletes with fitness-related decision-making is presented in this study as the Fitness Mamdani Decision System. At the outset, the system provides an adaptive decision framework to improve the effectiveness of training programs by applying the principles of intuitionistic fuzzy numbers and fuzzy logic. The study wants to know how to train effectively, and it takes important factors like your mood, degree of preparation, sleep quality, and stress levels into account. Using language-specific terminology and triangle membership functions, the Mamdani fuzzy inference system generates rules based on analyzing these crucial elements. Regarding measures like adaptability index, training load capacity, long-term program efficiency, and participation ratio among sports fitness individuals, the system is guided by fuzzy rules that infer decisions.

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Running Pace Adjustment and Training Distance Fitting with Fuzzy Logic and Machine Learning

Cited by 2 publications

References 11 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

Fuzzy Decision Support Systems to Improve the Effectiveness of Training Programs in the Field of Sports Fitness

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