Analysis of the Squat Exercise from Visual Data

Fatma Youssef, Ahmed Zaky, Ahmed Zaky, Walid Gomaa, Walid Gomaa

2022

Abstract

Squats are one of the most frequent at-home fitness activities. If the squat is performed improperly for a long time, it might result in serious injuries. This study presents a multiclass, multi-label dataset for squat workout evaluation. The dataset collects the most typical faults that novices make when practicing squats without supervision. As a first step toward universal virtual coaching for indoor exercises, the main objective is to contribute to the creation of a virtual coach for the squat exercise. A 3d position estimation is used to extract critical points from a squatting subject, then placed them in a distance matrix as the input to a multilayer convolution neural network with residual blocks. The proposed approach uses the exact match ratio performance metric and is able to achieve 94% accuracy. The performance of transfer learning as a known machine learning technique is evaluated for the squat activity classification task. Transfer learning is essential when changing the setup and configuration of the data collection process to reduce the computational efforts and resources.

Paper Citation

in Harvard Style

Youssef F., Zaky A. and Gomaa W. (2022). Analysis of the Squat Exercise from Visual Data. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-585-2, pages 79-88. DOI: 10.5220/0011347900003271

in Bibtex Style

@conference{icinco22,
author={Fatma Youssef and Ahmed Zaky and Walid Gomaa},
title={Analysis of the Squat Exercise from Visual Data},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2022},
pages={79-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011347900003271},
isbn={978-989-758-585-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Analysis of the Squat Exercise from Visual Data
SN - 978-989-758-585-2
AU - Youssef F.
AU - Zaky A.
AU - Gomaa W.
PY - 2022
SP - 79
EP - 88
DO - 10.5220/0011347900003271