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Authors: Patrick Alvim 1 ; Jonathan C. F. da Silva 1 ; Vicente Amorim 1 ; Pedro Lazaroni 2 ; Mateus Silva 1 and Ricardo Oliveira 1

Affiliations: 1 Departamento de Computação - DECOM, Universidade Federal de Ouro Preto - UFOP, Ouro Preto, Brazil ; 2 Núcleo de Ortopedia e Traumatologia(NOT), Belo Horizonte, Brazil

Keyword(s): Sensors, Wearable, App, Mobile, AI.

Abstract: The ability to faithfully reproduce the real world in the virtual environment is crucial to provide immersive and accurate experiences, opening doors to significant innovations in areas such as simulations, training, and data analysis. In such a way that actions in the virtual environment can be applied, which would be challenging in the real world due to issues of danger, complexity, or feasibility, enabling the study of these actions without compromising these principles. Additionally, it is possible to capture real-world data and analyze it in the virtual environment, faithfully reproducing real actions in the virtual realm to study their implications. However, the volatility of real-world data and the accurate capture and interpretation of such data pose significant challenges in this field. Thus, we present a system for real data capture aiming to virtually reproduce and classify walking and running activities. By using gyroscope data to capture the rotation of axes in the lower human limbs movement, it becomes possible to precisely replicate the motion of these body parts in the virtual environment, enabling detailed analyses of the biomechanics of such activities. In our observations, in contrast to quaternion data that may have different scales and applications depending on the technology used to create the virtual environment, gyroscope data has universal values that can be employed in various contexts. Our results demonstrate that, by using specific devices such as sensors instead of generic devices like smartwatches, we can capture more accurate and localized data. This allows for a granular and precise analysis of movement in each limb, in addition to its reproduction. This system can serve as a starting point for the development of more precise and optimized devices for different types of human data capture and analysis. Furthermore, it proposes creating a communication interface between the real and virtual worlds, aiming to accurately reproduce an environment in the other. This facilitates data for in-depth studies on the biomechanics of movement in areas such as sports and orthopedics. (More)

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Paper citation in several formats:
Alvim, P.; C. F. da Silva, J.; Amorim, V.; Lazaroni, P.; Silva, M. and Oliveira, R. (2024). The Power of Gyroscope Data: Advancing Human Movement Analysis for Walking and Running Activities. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7; ISSN 2184-4992, SciTePress, pages 510-519. DOI: 10.5220/0012702600003690

@conference{iceis24,
author={Patrick Alvim. and Jonathan {C. F. da Silva}. and Vicente Amorim. and Pedro Lazaroni. and Mateus Silva. and Ricardo Oliveira.},
title={The Power of Gyroscope Data: Advancing Human Movement Analysis for Walking and Running Activities},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={510-519},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012702600003690},
isbn={978-989-758-692-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - The Power of Gyroscope Data: Advancing Human Movement Analysis for Walking and Running Activities
SN - 978-989-758-692-7
IS - 2184-4992
AU - Alvim, P.
AU - C. F. da Silva, J.
AU - Amorim, V.
AU - Lazaroni, P.
AU - Silva, M.
AU - Oliveira, R.
PY - 2024
SP - 510
EP - 519
DO - 10.5220/0012702600003690
PB - SciTePress