通过姿态分析判断击球意图，可用于评估运动员风格与疲劳状态
Posture-Driven Action Intent Inference for Playing style and Fatigue Assessment
- 基于姿态动作分析识别击球意图，无需复杂传感器
- 在板球数据上实现75%以上F1分数和80%以上AUC-ROC
- 利用弱监督解决标注数据不足问题，适合体育与行为分析场景
基于姿态的心理状态推断在诊断疲劳、预防损伤和提升表现方面具有重要潜力。然而，由于人类受试者数据的敏感性，此类视觉诊断面临严峻挑战。为此，我们提出将体育场景作为收集多样情绪状态数据的可行替代方案。以板球为例，我们构建了一种基于姿态的动作意图推断方法，通过运动分析识别进攻与防守击球意图。该方法在数据存在固有噪声的情况下仍实现了超过75%的F1分数和超过80%的AUC-ROC。结果表明，姿态能泄露强烈的意图信号。此外，我们利用现有数据统计作为弱监督验证结论，为缓解标注数据不足提供了可能解决方案。本研究为体育分析提供了可泛化的技术路径，并拓展了人类行为分析在多个领域的应用前景。
原文摘要 · Abstract (English)
Posture-based mental state inference has significant potential in diagnosing fatigue, preventing injury, and enhancing performance across various domains. Such tools must be research-validated with large datasets before being translated into practice. Unfortunately, such vision diagnosis faces serious challenges due to the sensitivity of human subject data. To address this, we identify sports settings as a viable alternative for accumulating data from human subjects experiencing diverse emotional states. We test our hypothesis in the game of cricket and present a posture-based solution to identify human intent from activity videos. Our method achieves over 75\% F1 score and over 80\% AUC-ROC in discriminating aggressive and defensive shot intent through motion analysis. These findings indicate that posture leaks out strong signals for intent inference, even with inherent noise in the data pipeline. Furthermore, we utilize existing data statistics as weak supervision to validate our findings, offering a potential solution for overcoming data labelling limitations. This research contributes to generalizable techniques for sports analytics and also opens possibilities for applying human behavior analysis across various fields.
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