Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation

TL;DR

This paper introduces a real-time feedback system for isometric pose assessment, supported by a large dataset and benchmarking of models, aiming to improve home workout safety and effectiveness.

Contribution

It releases the largest multiclass isometric exercise video dataset and benchmarks state-of-the-art models with a new evaluation metric for pose classification and localization.

Findings

Enhanced pose classification accuracy

Effective mistake localization in exercises

Improved confidence estimation for model predictions

Abstract

Isometric exercises appeal to individuals seeking convenience, privacy, and minimal dependence on equipments. However, such fitness training is often overdependent on unreliable digital media content instead of expert supervision, introducing serious risks, including incorrect posture, injury, and disengagement due to lack of corrective feedback. To address these challenges, we present a real-time feedback system for assessing isometric poses. Our contributions include the release of the largest multiclass isometric exercise video dataset to date, comprising over 3,600 clips across six poses with correct and incorrect variations. To support robust evaluation, we benchmark state-of-the-art models-including graph-based networks-on this dataset and introduce a novel three-part metric that captures classification accuracy, mistake localization, and model confidence. Our results enhance the feasibility of intelligent and personalized exercise training systems for home workouts. This expert-level diagnosis, delivered directly to the users, also expands the potential applications of these systems to rehabilitation, physiotherapy, and various other fitness disciplines that involve physical motion.