There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSIs) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams have difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify the biomarkers of muscle injuries in professional soccer players through biomechanical analysis, employing several ML algorithms such as decision tree (DT) methods, discriminant methods, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble methods, boosted and bagged trees, artificial neural networks (ANNs), and XGBoost. In particular, XGBoost is also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regard to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion.

There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSI) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been a growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams encounter difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify biomarkers of muscle injuries in professional soccer players through a biomechanical analysis, employing several ML algorithms, such as Decision tree (DT) methods, Discriminant methods, Logistic regression, Naive Bayes, Support vector machine (SVM), K-nearest neighbor (KNN), Ensemble methods, Boosted and bagged trees, Artificial Neural Networks (ANN), and XGBoost. In particular, XGBoost was also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regards to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion.

During normal aging, a reduction in walking speed and changes in stability are observed. To characterize and compare gait kinematic parameters of young (YA) and older adults (OA) at a controlled speed. Gait angular kinematic parameters were analyzed in OA aged over 60 years and YA aged between 20 and 30 years. For data capture, six VICON Bonita cameras were used. Recording were made at a gait speed of 1.2 m/s. Kinematic data were normalized to 100% of the gait cycle using the Polygon 4.1 software. Time windows representing the phases of gait were created for analysis. At the hip-pelvis level, OA reduce their pelvic obliquity motion and increase hip abduction. At the knee level OA reduce movements in the frontal plane during the plantar support and half swing phases. At the ankle level OA reduce their maximum ranges of plantar flexion, during the toe-off and final swing phases. At a controlled speed, kinematic changes observed in OA allow an increase in body support base to maintain gait stability.

The handstand is an uncommon posture, highly demanding in terms of muscle and joint stability, used in sporting and artistic practices in a variety of disciplines. Despite its becoming increasingly widespread, there is no specific way to perform a handstand, and the neuromuscular organizational mechanisms involved are unknown. The objective of this study was to determine the muscle synergy of four handstand postures through a semblance analysis based on wavelets of electromyographic signals in the upper limbs of experienced circus performers between 18- and 35-year old. The results show that there is a large difference in positive and negative correlations depending on the posture, which suggests that the more asymmetrical the position of the lower limbs, the greater the number of strategies to maintain the posture. Although it is not a statistically significant data, it is observed that the posture 3 in particular, possesses the greatest number of positive correlations, which suggests it has the greatest synergy.