Posture-Driven Action Intent Inference for Playing style and Fatigue Assessment

TL;DR

This paper presents a posture-based method for inferring human shot intent in cricket, achieving high accuracy and demonstrating the potential of posture signals for intent detection and sports analytics.

Contribution

It introduces a novel approach using motion analysis and weak supervision to infer human intent from posture in sports videos, addressing data labeling challenges.

Findings

Achieves over 75% F1 score in intent classification

Over 80% AUC-ROC in discriminating shot types

Demonstrates posture signals contain strong intent information

Abstract

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.