A pain free nociceptor: Predicting football injuries with machine learning

Lyubovsky, Andrew; Liu, Zhenming; Watson, Amanda; Kuehn, Scott; Korem, Erik; Zhou, Gang

doi:10.1016/j.smhl.2021.100262

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Numerous academic papers and articles have explored the utilization of machine learning models within the field of sports science, particularly in soccer, to address various injury-related challenges. These include tasks such as detecting injuries, assessing injury risks, monitoring performance, predicting injuries, and guiding rehabilitation processes [1, 21,22,24,25]. For example, a multi-dimensional approach combining GPS data and machine learning has been developed for injury forecasting in soccer.…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types

Malikov¹,

Kim²

2023

Preprint

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Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. Supervised classification machine learning models are applied in this paper. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

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Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types

Malikov¹,

Kim²

2023

Preprint

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A flexible pressure sensor based on electrospun fiber for gait monitoring in football training

Yan

2023

AIP Advances

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After football players receive high-intensity training, they often have muscle injuries. The development of a stretchable wearable sport sensor with high sensing performance will effectively solve this problem. In this work, we develop a flexible and stretchable pressure sensor based on graphene/ESTANE TPU nanofiber electrodes and the [C2OHMIM]Cl/ESTANE TPU nanofiber electrolyte. Owing to the microporous structure of electrospun film, the pressure sensor has the advantages of good air permeability and skin compatibility. The working mechanism of the pressure sensor is based on the supercapacitance sensing mechanism, which brings a wide detection range, high repeatability, high sensitivity, and fast response. Besides, the sensor installed at the knee can perform gait analysis, such as walking and running with the ball, in football. Furthermore, the sensor array developed can monitor the pressure distribution at the knee in football in real time. This research will promote the application of intelligent sports equipment in football training.

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