AIMC Topic: Athletes

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Key personality and training factors influencing athletes' mental health - based on machine learning.

PloS one
Athletes face a higher risk of mental health disorders compared to the general population, and prior theoretical and empirical work suggests that personality traits and training-related factors may play important roles in shaping athletes' mental hea...

Evaluation of various traditional machine learning techniques for predicting the acute effect of different hamstring muscle stretching methods among male soccer players.

Scientific reports
This study investigated the acute effects of static (SS), dynamic (DS), and ballistic (BS) hamstring stretching on performance in male soccer players and applied machine learning (ML) to predict protocol efficacy. A total of 249 players with and with...

Enhancing psychological resilience and decision-making in basketball players through emerging technologies.

Scientific reports
The combination of sports psychology and new wearable technology is allowing experts to assess psychological and cognitive performance in elite basketball more accurately. This study investigates the application of Human Activity Recognition (HAR) us...

SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.

Scientific reports
Sports injury prediction is crucial for university football player health, yet existing research predominantly focuses on professional athletes and lacks interpretability. Using the Kaggle "University Football Injury Prediction Dataset" (800 Chinese ...

Efficient elastic tissue motions indicate general motor skill.

Scientific reports
Insights into the general nature of motor skill could fundamentally change how we develop movement abilities, with implications for musculoskeletal well-being and injury. Here, we sought to identify indicators of general motor skill-those shared by e...

Consistency verification and interpretation of explainable AI for predicting annual home runs of professional baseball players from sensor data.

Scientific reports
This study aimed to verify and interpret a model for predicting the number of home runs per year using sensor data from professional baseball players during batting practice. A machine learning model was constructed using Random Forest from the bat k...

Lightweight early detection of knee osteoarthritis in athletes.

Scientific reports
Osteoarthritis (OA) is a prevalent condition among athletes, characterized by the progressive degradation of joint cartilage, particularly in weight-bearing joints such as the knees. Early detection is critical for effective management and prevention...

Internet of things enabled deep learning monitoring system for realtime performance metrics and athlete feedback in college sports.

Scientific reports
This study presents an Internet of Things (IoT)-enabled Deep Learning Monitoring (IoT-E-DLM) model for real-time Athletic Performance (AP) tracking and feedback in collegiate sports. The proposed work integrates advanced wearable sensor technologies ...

Fatigue and stamina prediction of athletic person on track using thermal facial biomarkers and optimized machine learning algorithm.

Scientific reports
Athletic person's fatigue and stamina prediction plays a vital role for improving the overall performance in the sports. Identification of the athletic person's facial expression on track and field using image, is still a challenge task. The complex ...

Assessment of Recommendations Provided to Athletes Regarding Sleep Education by GPT-4o and Google Gemini: Comparative Evaluation Study.

JMIR formative research
BACKGROUND: Inadequate sleep is prevalent among athletes, affecting adaptation to training and performance. While education on factors influencing sleep can improve sleep behaviors, large language models (LLMs) may offer a scalable approach to provid...