报告详情
Multimodal Data Fusion for Jump Performance Prediction Using Machine Learning: A Survey
编号：62
访问权限：仅限参会人
更新：2026-11-10 16:34:58 浏览：7次
Online
报告开始：2026年11月21日 13:15（Asia/Kolkata）
报告时间：15min
所在会场：[S4] Artificial Intelligence and Intelligent Computing [S4-9] Artificial Intelligence and Intelligent Computing
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摘要
Jump performance assessment supports sports biomechanics, athletic training, and physical performance monitoring, yet conventional assessment relies on force platforms and laboratory-based motion analysis that are difficult to use in practical sports environments. This paper surveys machine-learning approaches to jump performance analysis, drawing on 42 sources that cover biomechanical assessment, wearable and physiological sensing, computer vision, multimodal data fusion, graph and hypergraph learning, and generative data augmentation. It compares the input modalities, prediction targets, and validation designs of direct jump studies and keeps them separate from methods developed for other sports, general graph learning, and other application domains. The review identifies recurring challenges: limited jump-specific multimodal datasets, synchronization of heterogeneous data, small and narrow participant samples, inconsistent targets and evaluation protocols, and the need for participant-independent validation. It also outlines research opportunities, including a systematic comparison of concatenation, graph, and hypergraph fusion with modality ablation, evaluation of synthetic data under strict train–test separation, and a proposed video and physiological-signal framework. No new model is implemented or validated in this paper.
关键词
Jump performance prediction, machine learning, sports biomechanics, computer vision, physiological sensing, multimodal data fusion, graph neural networks, hypergraph learning.
报告人
Mrs. Kiruthiga Duraipandi
Research Scholar Anna University稿件作者
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重要日期
-
会议日期
11月20日
2026
至11月22日
2026
-
10月31日 2026
初稿截稿日期
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11月07日 2026
提前注册日期
-
11月22日 2026
注册截止日期
主办单位
United Societies of Science
承办单位
Pondicherry University
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICon INDIA 2026
- ic******@usssociety.org
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