ALEX-GYM-1: A Novel Dataset and Hybrid 3D Pose Vision Model for Automated Exercise Evaluation

Ahmed Hassan, Abdelaziz Serour, Ahmed Gamea, Walid Gomaa, Walid Gomaa

2025

Abstract

Improper gym exercise execution often leads to injuries and suboptimal training outcomes, yet conventional assessment relies on subjective human observation. This paper introduces ALEX-GYM-1, a novel multi-camera view dataset with criterion-specific annotations for squats, lunges, and Romanian deadlifts, alongside a complementary multi-modal architecture for automated assessment. Our approach uniquely integrates: (1) a vision-based pathway using 3D CNN to capture spatio-temporal dynamics from video, and (2) a pose-based pathway that analyzes biomechanical relationships through engineered landmark features. Extensive experiments demonstrate the superiority of our Multi-Modal fusion architecture over both single-modality methods and competing approaches, achieving Hamming Loss reductions of 30.0% compared to Vision-based and 79.5% compared to Pose-based models. Feature-specific analysis reveals key complementary strengths, with Vision-based components excelling at contextual assessment (89% error reduction for back knee positioning) while Pose-based components demonstrate precision in specific joint relationships. The computational efficiency analysis enables practical deployment strategies for both real-time edge applications and high-accuracy cloud computing scenarios. This work addresses critical gaps in exercise assessment technology through a purpose-built dataset and architecture that establishes a new state-of-the-art for automated exercise evaluation in multi-camera view settings.

Paper Citation

in Harvard Style

Hassan A., Serour A., Gamea A. and Gomaa W. (2025). ALEX-GYM-1: A Novel Dataset and Hybrid 3D Pose Vision Model for Automated Exercise Evaluation. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 24-34. DOI: 10.5220/0013669400003982

in Bibtex Style

@conference{icinco25,
author={Ahmed Hassan and Abdelaziz Serour and Ahmed Gamea and Walid Gomaa},
title={ALEX-GYM-1: A Novel Dataset and Hybrid 3D Pose Vision Model for Automated Exercise Evaluation},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={24-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013669400003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - ALEX-GYM-1: A Novel Dataset and Hybrid 3D Pose Vision Model for Automated Exercise Evaluation
SN - 978-989-758-770-2
AU - Hassan A.
AU - Serour A.
AU - Gamea A.
AU - Gomaa W.
PY - 2025
SP - 24
EP - 34
DO - 10.5220/0013669400003982
PB - SciTePress