The Investigation of Athlete Injuries Prediction Based on Machine Learning Models

DOI

10.2991/978-94-6463-512-6_63How to use a DOI?

Keywords

Machine learning; athlete injury; artificial intelligence

Abstract

This article provides a literature review on the application of machine learning in athlete pain detection. This review can help future researchers and learners gain a general understanding of the current research status and future expectations of machine learning in the field of athlete pain detection. This article also explains the commonly used machine learning methods in athlete pain detection, such as random forest, regression analysis, artificial neural networks, etc. It briefly describes the steps of machine learning to establish a pain detection model, including data collection, model building, and model detection. In the conclusion section of this article, the advantages of machine learning in accurately predicting pain through data and the difficulty in collecting large amounts of data, as well as the lack of high generalization in the constructed models, are mentioned. Based on these shortcomings, a prospect is made for inventing better data collection methods and building higher quality machine learning methods.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Junliang Lv
PY  - 2024
DA  - 2024/09/23
TI  - The Investigation of Athlete Injuries Prediction Based on Machine Learning Models
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 600
EP  - 605
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_63
DO  - 10.2991/978-94-6463-512-6_63
ID  - Lv2024
ER  -