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Trajectory Prediction Analysis of Basketball Players' Movements Based on DSM Optimization Algorithm.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Liu, Yang; Zhang, Wei; Liu, Shaohua; Lyu, Hang 3 of 3

Abstract

In this paper, the action trajectory of basketball players is predicted and analyzed by the DSM optimization algorithm. Data on the movements of basketball players, including position, speed and acceleration are collected. The motion trajectory is predicted by the DSM optimization algorithm. At present, computer technology is more and more applied in sports training. Based on this, this paper studies the simulation of basketball players' motion trajectory prediction. After describing the research background and significance, this paper constructs a human joint model and then proposes the motion process DSM algorithm to simulate the jump shot action of basketball players. In this paper, the algorithm and model are tested, and the experiment adopts the run-jump fatigue model to produce fatigue. The infrared high-speed motion capture system and three-dimensional force measuring table are used to collect the kinematics and dynamics data of the sudden stop jump shot before and after fatigue. The experimental results show that the trajectory prediction simulation proposed in this paper is feasible. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/09, Vol. 34, Issue 3, p1
  • Document Type:Article
  • Subject Area:Technology
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156425400415
  • Accession Number:185074665
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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