Talking Tennis：基于3D生物力学动作识别的语言反馈
Talking Tennis: Language Feedback from 3D Biomechanical Action Recognition
AI总结：
针对现有网球击球分析系统无法将生物力学洞见转化为易懂可执行语言反馈的问题，本研究提出基于CNN-LSTM提取生物力学特征、结合LLM生成反馈的框架，经THETIS数据集验证，兼具准确性与可解释性，打通了可解释AI与运动生物力学的壁垒。
AI中文摘要：
随着生物力学运动线索与深度学习技术的融合，自动化网球击球分析取得了显著进展，提升了击球分类准确率和运动员表现评估水平。尽管有这些进步，现有系统往往无法将生物力学洞见与对运动员和教练既易懂又有意义的可执行语言反馈关联起来。本研究项目通过开发一个新型框架解决这一差距，该框架使用基于卷积神经网络长短期记忆（CNN-LSTM）的模型从运动数据中提取关键生物力学特征（如关节角度、肢体速度和动力链模式）。研究分析这些特征之间影响击球有效性和损伤风险的关联，以此为基础利用大语言模型（LLM）生成反馈。借助THETIS数据集和特征提取技术，我们的方法旨在生成技术上准确、有生物力学依据且对终端用户可执行的反馈。实验设置从分类性能和可解释性两方面评估该框架，搭建起可解释AI与运动生物力学之间的桥梁。
英文摘要：
Automated tennis stroke analysis has advanced significantly with the integration of biomechanical motion cues alongside deep learning techniques, enhancing stroke classification accuracy and player performance evaluation. Despite these advancements, existing systems often fail to connect biomechanical insights with actionable language feedback that is both accessible and meaningful to players and coaches. This research project addresses this gap by developing a novel framework that extracts key biomechanical features (such as joint angles, limb velocities, and kinetic chain patterns) from motion data using Convolutional Neural Network Long Short-Term Memory (CNN-LSTM)-based models. These features are analyzed for relationships influencing stroke effectiveness and injury risk, forming the basis for feedback generation using large language models (LLMs). Leveraging the THETIS dataset and feature extraction techniques, our approach aims to produce feedback that is technically accurate, biomechanically grounded, and actionable for end-users. The experimental setup evaluates this framework on classification performance and interpretability, bridging the gap between explainable AI and sports biomechanics.
