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Authors: Fatma Youssef 1 ; Ahmed B. Zaky 2 ; 3 and Walid Gomaa 4 ; 5

Affiliations: 1 Computer Science Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt ; 2 Computer Science and Information Technology Programs (CSIT), Egypt Japan University of Science and Technology, Egypt ; 3 Shoubra Faculty of Engineering, Benha University, Benha, Egypt ; 4 Cyber Physical Systems Lab, Egypt Japan University of Science and Technology, Egypt ; 5 Faculty of Engineering, Alexandria University, Alexandria, Egypt

Keyword(s): Exercise Assessment, Deep Learning, Transfer Learning.

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 t he setup and configuration of the data collection process to reduce the computational efforts and resources. (More)

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Paper citation in several formats:
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 - ICINCO; ISBN 978-989-758-585-2; ISSN 2184-2809, SciTePress, pages 79-88. DOI: 10.5220/0011347900003271

@conference{icinco22,
author={Fatma Youssef. and Ahmed B. 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 - ICINCO},
year={2022},
pages={79-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011347900003271},
isbn={978-989-758-585-2},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Analysis of the Squat Exercise from Visual Data
SN - 978-989-758-585-2
IS - 2184-2809
AU - Youssef, F.
AU - Zaky, A.
AU - Gomaa, W.
PY - 2022
SP - 79
EP - 88
DO - 10.5220/0011347900003271
PB - SciTePress

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