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

arXiv: https://arxiv.org/abs/2507.11642 · HTML: https://mlsanity.com/paper/2507.11642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [Am I a Baller? Basketball Performance Assessment from First-Person Videos](https://mlsanity.com/paper/1611.05365/) (2016)
- [Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games](https://mlsanity.com/paper/2107.05617/) (2021)
- [Using Learnable Physics for Real-Time Exercise Form Recommendations](https://mlsanity.com/paper/2310.07221/) (2023)
- [Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification](https://mlsanity.com/paper/2409.13643/) (2024)
- [Analyzing sports commentary in order to automatically recognize events and extract insights](https://mlsanity.com/paper/2307.10303/) (2023)

## Abstract (arXiv)

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.