发表机构
Yuan Ze University（元智大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对乒乓球智能球拍数据，提出多任务深度学习模型TTNet，结合CNN、ResNet和自注意力预测运动员四属性，获竞赛第二名。
AI 中文摘要
AI CUP 2025 乒乓球智能球拍数据精准分析竞赛引入了智能乒乓球拍，可收集大量运动员挥拍数据，从而支持乒乓球大数据研究。这些数据有助于深入分析运动员的回球技术和挥拍力度一致性，提高运动员技能评估的准确性。本研究聚焦于智能乒乓球拍采集的六轴传感器数据，提出了一种具有多任务学习能力的新型深度学习模型 TTNet，以推进乒乓球数据分析及相关应用。TTNet 结合了卷积神经网络（CNN）、残差网络（ResNet）和自注意力机制，可同时预测运动员的四个属性：性别、持拍手、运动年限和技能水平。我们采用两阶段训练策略，结合数据增强和任务特定损失函数，以改善不平衡数据上的泛化能力。我们的方法在官方竞赛排行榜上获得了第二名。
英文摘要
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.
Comments12 pages, 4 figures, 5 tables
