Journal of Applied Science and Engineering

Published by Tamkang University Press

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AI-Powered Predictive Analytics for Injury Prevention in Sports: Integrating Wearable Sensors and Data-Driven Insights

 2026

 Volume 34

Lidan Song

Institute of Physical Education, Anyang Normal University, Anyang, Henan 455000, China

Received: May 07, 2026
Accepted: June 26, 2026
Publication Date: August 22, 2026

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Informer encoder–decoder model for athlete injury risk prediction

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202611_34.062  

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Sports injuries are increasingly common among athletes, impacting performance and recovery. Current models struggle to simulate the complex interactions of physiological, biomechanical, and workload factors, limiting their predictive accuracy. This study proposes a robust framework for accurately predicting sports injuries through multimodal monitoring. By integrating physiological, biomechanical, environmental, and workload parameters, the model processes data using algorithms for missing values, normalization, and noise reduction, followed by Principal Component Analysis. An Informer-based Deep Learning model with a ProbSparse attention mechanism effectively captures complex feature interactions. Experimental results show the model achieves accuracy, precision, recall, F1-score, and ROC-AUC of 99.63%, 99.63%, 99.63%, 99.62%, and 99.95%, respectively, outperforming traditional models like Logistic Regression and Support Vector Machine. This framework enhances early injury detection, supporting better athlete health management and training optimization.

Keywords: ports Injury Prediction, Informer Model, Wearable Sensors, Predictive Analytics, Athlete Monitoring

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