Posture-Driven Action Intent Inference for Playing style and Fatigue Assessment
This research addresses the challenge of data sensitivity in vision-based mental state inference by using sports settings, offering potential applications in sports analytics and human behavior analysis.
The paper tackled the problem of inferring human intent from posture in sports videos, specifically in cricket, by developing a posture-based method that achieved over 75% F1 score and over 80% AUC-ROC in discriminating aggressive and defensive shot intent.
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.