arXiv：cs.LG· Ko-Hsun Chen, Xiang-Wei Ke, Hsien-Cheng Huang, Shang-Kuan Chen·· 4 小时前AI 评分24
TTNet：用智能乒乓球拍数据做球员分析的多任务深度学习模型
TTNet: Multi-Task Deep Learning for Table Tennis Player Analysis with Smart Racket
AI 导读
TTNet 是一个结合 CNN、ResNet 与自注意力机制的多任务深度学习模型，可基于智能乒乓球拍的六轴传感器数据同时预测球员性别、持拍手、球龄和技能水平四项属性。该模型采用两阶段训练策略，引入数据增强与任务特定损失函数以提升不平衡数据上的泛化能力，在 AI CUP 2025 乒乓球智能球拍数据精准分析竞赛官方榜单上获得第二名。
正文
Abstract:The AI CUP 2025 Precise Analysis of Table Tennis Smart Racket Data Competition introduced smart table tennis rackets that collect extensive player swing data, enabling research on table tennis big data. These data support in-depth analysis of players' return techniques and swing-force consistency, improving the accuracy of player skill assessment. This study focuses on six-axis sensor data collected by smart table tennis rackets and proposes TTNet, a novel deep learning model with multitask learning capabilities, to advance table tennis data analysis and related applications. TTNet combines convolutional neural networks (CNNs), residual networks (ResNet), and self-attention mechanisms to simultaneously predict four player attributes: gender, playing hand, years of experience, and skill level. We adopt a two-stage training strategy that incorporates data augmentation and task-specific loss functions to improve generalization on imbalanced data. Our approach achieved second place on the official competition leaderboard.
| Comments: | 12 pages, 4 figures, 5 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.07823 [cs.LG] |
| (or arXiv:2610.07823v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07823 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shang-Kuan Chen [view email]
[v1]
Tue, 6 Oct 2026 06:18:52 UTC (626 KB)
来源：arXiv：cs.LG · arxiv.org