Skip to content
Publish & Distribute
Publishing Solutions
Distribution Solutions
Library Services
Subjects
Architecture and Design
Arts
Business and Economics
Chemistry
Classical and Ancient Near Eastern Studies
Computer Sciences
Cultural Studies
Engineering
General Interest
Geosciences
History
Industrial Chemistry
Jewish Studies
Law
Library and Information Science, Book Studies
Life Sciences
Linguistics and Semiotics
Literary Studies
Materials Sciences
Mathematics
Medicine
Music
Pharmacy
Philosophy
Physics
Social Sciences
Sports and Recreation
Theology and Religion
Publications
Journals
Books
Proceedings
Publishers
Journal Matcher
Blog
Contact
Search
English
English
Deutsch
Polski
Español
Français
Italiano
Cart
Home
Journals
Applied Mathematics and Nonlinear Sciences
Volume 9 (2024): Issue 1 (January 2024)
Open Access
Deep Learning-Based Prediction and Optimized Path Planning for Sports Athletes’ Movement Trajectories
Diying Sun
Diying Sun
Physical Education Department, Henan Vocational College of Water Conservancy and Environment
Zhengzhou, China
Search for this author on
Sciendo
|
Google Scholar
Sun, Diying
Nov 25, 2024
Applied Mathematics and Nonlinear Sciences
Volume 9 (2024): Issue 1 (January 2024)
About this article
Previous Article
Next Article
Abstract
References
Authors
Articles in this Issue
Preview
PDF
Cite
Share
Download Cover
Published Online:
Nov 25, 2024
Received:
Jun 15, 2024
Accepted:
Oct 08, 2024
DOI:
https://doi.org/10.2478/amns-2024-3429
Keywords
Motion features
,
LSTM
,
GNN
,
Trajectory prediction
,
Path planning
© 2024 Diying Sun, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
