AI-Based Player Fatigue and Workload Monitoring Systems DOI
Shashank Mittal, Ajay Chandel, Ta Huy Hung

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 15 - 40

Опубликована: Май 13, 2025

The Present chapter examines AI-based player fatigue and workload monitoring systems in optimizing the performance of athletes while preventing them from burning out. These are harnessed advanced models machine learning, analyzing physiological, biomechanical, mental health data to improve training recovery strategies. Advances wearable technologies will dramatically change way coaches approach world management by real-time predictive analytics. This also deals with some difficulties, including accuracy, privacy ethical issues, associated athlete autonomy. Other possible future trends relate cognitive metrics, multisport sharing, involvement virtual or augmented reality environments. goal at end road for is optimal athletic performance, but all this should be done aim supporting long-term well-being.

Язык: Английский

Advancing Water Quality Assessment and Prediction Using Machine Learning Models, Coupled with Explainable Artificial Intelligence (XAI) Techniques Like Shapley Additive Explanations (SHAP) For Interpreting the Black-Box Nature DOI Creative Commons
Randika K. Makumbura, Lakindu Mampitiya, Namal Rathnayake

и другие.

Results in Engineering, Год журнала: 2024, Номер 23, С. 102831 - 102831

Опубликована: Сен. 1, 2024

Water quality assessment and prediction play crucial roles in ensuring the sustainability safety of freshwater resources. This study aims to enhance water by integrating advanced machine learning models with XAI techniques. Traditional methods, such as index, often require extensive data collection laboratory analysis, making them resource-intensive. The weighted arithmetic index is employed alongside models, specifically RF, LightGBM, XGBoost, predict quality. models' performance was evaluated using metrics MAE, RMSE, R2, R. results demonstrated high predictive accuracy, XGBoost showing best (R2 = 0.992, R 0.996, MAE 0.825, RMSE 1.381). Additionally, SHAP were used interpret model's predictions, revealing that COD BOD are most influential factors determining quality, while electrical conductivity, chloride, nitrate had minimal impact. High dissolved oxygen levels associated lower indicative excellent pH consistently influenced predictions. findings suggest proposed approach offers a reliable interpretable method for prediction, which can significantly benefit specialists decision-makers.

Язык: Английский

Процитировано

29
Current methods in explainable artificial intelligence and future prospects for integrative physiology DOI Creative Commons
Bettina Finzel

Pflügers Archiv - European Journal of Physiology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 25, 2025

Abstract Explainable artificial intelligence (XAI) is gaining importance in physiological research, where now used as an analytical and predictive tool for many medical research questions. The primary goal of XAI to make AI models understandable human decision-makers. This can be achieved particular through providing inherently interpretable methods or by making opaque their outputs transparent using post hoc explanations. review introduces core topics provides a selective overview current physiology. It further illustrates solved discusses open challenges existing practical examples from the field. article gives outlook on two possible future prospects: (1) provide trustworthy integrative (2) integrating expertise about explanation into method development useful beneficial human-AI partnerships.

Язык: Английский

Процитировано

2
Ensemble deep learning model for protein secondary structure prediction using NLP metrics and explainable AI DOI Creative Commons

Uvarani Vignesh,

R. Parvathi,

Keerthi Ram

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103435 - 103435

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

6
Harnessing explainable artificial intelligence (XAI) for enhanced geopolymer concrete mix optimization DOI Creative Commons

Bh Revathi,

R. Gobinath, Govindasamy Bala

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103036 - 103036

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

3
Machine Learning Model Construction and Practice for Personalized Training Programs in Athletics Training DOI Open Access
Xi Chen, Yaosheng Zhang, Rong Qing Jia

и другие.

Applied Mathematics and Nonlinear Sciences, Год журнала: 2025, Номер 10(1)

Опубликована: Янв. 1, 2025

Abstract Big data science is a complexity produced in the new era, and machine learning models belong to its main branch, which has characteristic methodological features provides ideas scientifically solve personalized formulation of training programs track field training. In this paper, firstly, athletes’ sports are collected by installing sensors key parts athletes, then real-time state estimation given Kalman filtering, optimized microelectromechanical technology. The obtained solution results inputted into important movement joint model human body so as realize motion capture athletes. Based on this, for been constructed using an ant colony algorithm. generation plan varied optimization problem with constraints, containing discrete continuous variables. Then, method adaptation evaluation constraints updating related solutions were proposed, thus completing construction model. experimental group improved much more events than control group, 24.96% girls’ shot put. It shows that program developed through based line different students’ own needs, generated can provide athletes efficient guidance, verifies effectiveness paper practice design experiment.

Язык: Английский

Процитировано

0
A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results DOI Creative Commons

Messaoud Bendiaf,

Hakima Khelifi, Djamila Mohdeb

и другие.

International Journal of Computer Science in Sport, Год журнала: 2025, Номер 24(1), С. 56 - 72

Опубликована: Фев. 1, 2025

Abstract Football match result prediction is a challenging task that has been the subject of much research. Traditionally, predictions have made by team managers, fans, and analysts based on their knowledge experience. However recently there an increased interest in predicting outcomes using statistical techniques machine learning. These algorithms can learn from historical data to identify complex relationships between different variables, then make about outcome future matches. Accordingly, forecasting plays pivotal role assisting managers clubs making well-informed decisions geared toward securing victories leagues tournaments. In this paper, we presented approach, which generally applicable all areas sports, forecast football results three stages. The first stage involves identifying collecting occurred events during match. As multiclass classification problem with classes, each possible outcomes. Then, applied multiple learning compare performance those models, choose one performs best. final step, study goes through critical aspect model interpretability. We used SHapley Additive exPlanations (SHAP) method decipher feature importance within our best model, focusing factors influence predictions. Experiment indicate Multilayer Perceptron (MLP), neural network algorithm, was effective when compared various other models produced competitive prior works. MLP achieved 0.8342 for accuracy. particular significance lies use SHAP explain model. Specifically, exploiting its graphical representation illustrate dataset

Язык: Английский

Процитировано

0
Implementing machine learning algorithms to optimize sprint performance and biomechanical analysis of track and field athletes DOI
Tian Gao, Xiangwei Chen

Journal of Computational Methods in Sciences and Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

Sprint performance is a crucial component of athletic performance, especially in sports like track and field, football, rugby, which require quick bursts peak effort over short durations. Understanding the biomechanics sprinting essential for enhancing preventing injuries, creating effective training plans. Traditional research on sprint evaluation often focuses discrete measures while neglecting intricate interactions between variables that evolve throughout sprint. This study addresses these challenges by applying machine learning (ML) algorithm, specifically Polar Bear-tuned Multi-Source Kernel Support Vector Machine (PB-MKSVM), to predict optimize field athletes. The system analyzes biomechanical characteristics such as muscle activation patterns, joint angles, ground reaction forces, stride length. Data were collected using wearable sensors motion capture systems during standardized trials, various parameters recorded. Standard preprocessing steps including noise removal outlier detection applied data. Power Spectral Density (PSD) was employed extract features from preprocessed results demonstrate proposed method outperforms traditional algorithms predicting efficiency identifies complex, phase-specific changes movement patterns. model effectively sprinters’ movements differentiate skill levels. Using Python software, achieved impressive metrics, accuracy (94.5%), precision (92.7%), recall (93.6%), F1-score (92.1%), R 2 (0.92), AUC (0.91), highlighting its robust predictive ability. illustrates how models can advance mechanics provide insightful information athletes coaches seeking improve performance.

Язык: Английский

Процитировано

0
AI-Based Player Fatigue and Workload Monitoring Systems DOI
Shashank Mittal, Ajay Chandel, Ta Huy Hung

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 15 - 40

Опубликована: Май 13, 2025

The Present chapter examines AI-based player fatigue and workload monitoring systems in optimizing the performance of athletes while preventing them from burning out. These are harnessed advanced models machine learning, analyzing physiological, biomechanical, mental health data to improve training recovery strategies. Advances wearable technologies will dramatically change way coaches approach world management by real-time predictive analytics. This also deals with some difficulties, including accuracy, privacy ethical issues, associated athlete autonomy. Other possible future trends relate cognitive metrics, multisport sharing, involvement virtual or augmented reality environments. goal at end road for is optimal athletic performance, but all this should be done aim supporting long-term well-being.

Язык: Английский

Процитировано

0