A Markerless Joint Detection through a Hand Geometric Representation

Aline Lemos, Nagy Vince

2020

Abstract

In several fields, such as man-machine interface and occupational therapy, the human hand-joint position is required. Traditional methods usually rely on image processing allied with marker placement, e.g. reflexive marker, which can be time-consuming and uncomfortable for the subject. For these reasons, scientific efforts are being made to create reliable and convenient joint tracking. This paper proposes a methodology that generates geometric figures to mimic the hand configuration. This process is made possible by an optimization algorithm, which finds the most suitable placement of these geometric shapes. One time the real hand and the created representation share similar features, the joints position can be estimated. Two optimization algorithms were employed: particle swarm optimization and genetic algorithm. In both cases, satisfactory results were obtained. Although, particle swarm optimization marginally outperformed the latter method.

Paper Citation

in Harvard Style

Lemos A. and Vince N. (2020). A Markerless Joint Detection through a Hand Geometric Representation. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 626-633. DOI: 10.5220/0008971706260633

in Bibtex Style

@conference{icaart20,
author={Aline Lemos and Nagy Vince},
title={A Markerless Joint Detection through a Hand Geometric Representation},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={626-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008971706260633},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Markerless Joint Detection through a Hand Geometric Representation
SN - 978-989-758-395-7
AU - Lemos A.
AU - Vince N.
PY - 2020
SP - 626
EP - 633
DO - 10.5220/0008971706260633