FERA: A Pose-Based Framework for Rule-Grounded Multimedia Decision Support with a Foil Fencing Case Study

1. 1Separate canonical participant tracking from pose estimation
2. 2Apply pose estimation to track fast bilateral motion
3. 3Integrate rule-based decision logic to make explicit state estimates
4. 4Audit and consume decision outputs for downstream logic

Machine learning engineers and computer vision experts can use FERA to develop decision support systems, while product managers and analysts can apply it to various domains such as sports analytics

💡 FERA enables explicit state estimates that can be checked against rules and audited by humans

🤺 Introducing FERA, a pose-based framework for rule-grounded multimedia decision support in foil fencing! 🤖