Constructing an artificial intelligence strategy algorithm for the identification of talented rowing athletes

Liu, Jingwei; Chen, Sheng-Hsiang; Chen, Che-Hsiu

doi:10.1007/s00500-021-06050-3

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In connection with its rapid processing and analysis of large amounts of data, AI may reveal patterns, trends, relationships, and other insights not immediately apparent to human observers ( 16 – 20 ).…”

Section: Strengthsmentioning

confidence: 99%

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Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis

Sperlich,

Düking,

Leppich

et al. 2023

Front. Sports Act. Living

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Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.

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Section: Strengthsmentioning

confidence: 99%

“…By analyzing large amounts of data concerning the determinants of performance, including physical attributes, and other potential indicators of success for individual athletes, AI can potentially help identify new talent ( 19 , 20 ).…”

Section: Opportunitiesmentioning

confidence: 99%

Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis

Sperlich,

Düking,

Leppich

et al. 2023

Front. Sports Act. Living

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“…These programs are informed by data analysis, including performance metrics, biomechanical assessments, and psychological profiles. Coaches and trainers work closely with athletes to refine their skills, address weaknesses, and optimize their overall athletic potential [6]. Central to the talent cultivation process is ongoing support and guidance for athletes as they progress through their careers [7].…”

Section: Introductionmentioning

confidence: 99%

Construction and Optimization of Sports Athlete Selection and Talent Cultivation System Based on Data Analysis

Zhuoxiao Liu

2024

jes

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Talent cultivation is the deliberate and systematic process of identifying, nurturing, and developing individuals' innate abilities and potential across various domains. It involves providing structured opportunities, resources, and guidance to help individuals flourish and excel in their chosen fields. This process encompasses education, mentorship, training programs, and practical experiences tailored to the specific needs and interests of each individual. This paper presents a novel approach to the construction and optimization of a Sports Athlete Selection and Talent Cultivation System through the integration of data analysis techniques, with a particular emphasis on the Anthropometric Invariance Gait Recognition for Pattern (AIGRP) system. Leveraging advanced algorithms, the AIGRP system enables precise characterization of athletes' biomechanical profiles based on anthropometric measurements and gait characteristics. By analyzing data collected from athletes, including age, gender, height, weight, and specific performance metrics, such as speed, strength, and endurance, the system facilitates personalized athlete selection and talent cultivation strategies. Furthermore, the objective and data-driven nature of the AIGRP system reduces biases inherent in traditional selection methods, leading to more efficient and effective talent identification. By analyzing data collected from athletes, including age, gender, height (mean: 175 cm), weight (mean: 70 kg), and specific performance metrics, such as speed (mean: 6.5 m/s), strength (mean: 140 kg), and endurance (mean: 47 min), the system facilitates personalized athlete selection and talent cultivation strategies.

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Ensuring athlete physical fitness using Cyber-Physical Systems (CPS) in training environments

Zhou,

Daud

2024

THC

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BACKGROUND: Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete’s physical fitness status. RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete’s physical fitness and overall performance to a considerable extent. CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.

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Constructing an artificial intelligence strategy algorithm for the identification of talented rowing athletes

Cited by 3 publications

References 14 publications

Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis

Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis

Construction and Optimization of Sports Athlete Selection and Talent Cultivation System Based on Data Analysis

Ensuring athlete physical fitness using Cyber-Physical Systems (CPS) in training environments

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