Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Rigamonti, Lia; Estel, Katharina; Gehlen, Tobias; Wolfarth, Bernd; Lawrence, James B.; Back, David Alexander

doi:10.1186/s13102-021-00243-x

Cited by 16 publications

(9 citation statements)

References 25 publications

(27 reference statements)

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“…Through example learning, AI enables the automation of biomarker identification, i.e., locating higher order feature areas that connect to specific phenomena. Deep learning finds patterns through the use of artificial neural networks [ 8 ]. Once upon a time, the most elegant neural network in our brain was shaped in this way.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Zhang

Feng-jun

2022

Computational Intelligence and Neuroscience

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Athletes have had to deal with significant shifts in the way they think about psychology and emotion before and after attending a match in their respective fields. It has become increasingly difficult for players of any sport to overcome these differences due to massive technological advancements that aid in analyzing the difficulties of an athlete. The trainer can use the results of the analysis to help motivate and prepare the athletes for the upcoming competitions. The analysis in this study is based on information about the athletes who competed in the Tokyo Olympics. Deep learning models were used to evaluate the study. Image feature detection can be accomplished through the application of a machine learning technique known as deep learning. It employs a neural network, a computer system that mimics the human brain’s multiple layers. One or more unique features can be extracted from each layer. A deep learning model called the behavior recognition algorithm is used for the research. The questionnaire from the dataset was used to generate the results of the analysis.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Zhang

Feng-jun

2022

Computational Intelligence and Neuroscience

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“…O aplicativo em questão se mostrou uma poderosa ferramenta que pode ser utilizada não apenas por médicos, mas também por treinadores, em situações que exigem medidas rápidas, seja em treinamentos ou competições. Contudo, é importante salientar que tais aplicativos não possuem a capacidade de substituir os profissionais qualificados, mas representam mais um recurso para eles, promovendo um cenário que traz mais facilidade e dinamismo ao atendimento (Rigamonti et al, 2021). 2019) enfoca áreas como a previsão do resultado de partidas, tomada de decisões táticas, investimento em jogadores, "fantasy sports" e prevenção de lesões.…”

Section: Métodosunclassified

Aplicação Da Inteligência Artificial Na Educação Física Voltadas À Saúde Ao Treinamento

Amaral,

Damatto

2024

FIEP-Bulletin

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Introdução: Dado o rápido crescimento da Inteligência Artificial em diversas áreas, seria natural esperar a utilização de seus recursos na Educação Física. No entanto, as aplicações nesse cenário ainda são pontuais. Apesar disso, o potencial para essa área é gigantesco, e especialistas têm uma visão otimista sobre seu desenvolvimento. Objetivo: Este estudo tem como objetivo explorar o panorama atual da Inteligência Artificial na Educação Física, mais especificamente sua utilização na Avaliação e Prescrição de Treinos, abrangendo objetivos como ganho de desempenho, manutenção da saúde e acompanhamento do treinamento. Métodos: No contexto de uma revisão de literatura, as buscas foram conduzidas em bases de dados de acesso público, utilizando descritores que tinham como objetivo a seleção de trabalhos que estivessem alinhados com o objetivo da pesquisa. Não houve critérios de exclusão baseados na língua ou data de publicação. Os trabalhos selecionados deveriam estar relacionados ao tema da pesquisa. Ao final, foram selecionados sete trabalhos. Resultados: Os estudos selecionados, apesar de em número reduzido, revelaram um cenário que já demonstra resultados positivos e expectativas otimistas para o futuro da área. Conclusão: Conclui-se que a inteligência artificial está em rápido desenvolvimento e que há necessidade de adaptação por parte dos profissionais de Educação Física, que devem estar prontos para utilizar as novas ferramentas proporcionadas pela inteligência artificial a seu favor.

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“…The scientic challenge in practice with this framework has been the proper parametrisation of 1) the training load [40,55,654,883]; 2) the recovery state and preparedness to train [322,394,537]; 3) injury risk prediction [356,357,601,755,881]. Out of these three goals, training load and recovery state are within practical reach at current technological maturity level, whereas injury prediction seems overly challenging [315,356,357,385,386,599,678] 1 , mainly due to inherent low prevalence of injuries in athletic population [101,105,344,377,397,627,686,743,878], which subsequently leads to injury prediction models with poor predictive power [348,504].…”

Section: Precision Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Cited by 16 publications

References 25 publications

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Aplicação Da Inteligência Artificial Na Educação Física Voltadas À Saúde Ao Treinamento

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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