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A low-cost non-intrusive spatial hand tracking pipeline for product-process interaction

Published online by Cambridge University Press:  16 May 2024

James Gopsill ,
Aman Kukreja ,
Christopher Michael Jason Cox  and
Chris Snider
James Gopsill*
Affiliation:
University of Bristol, United Kingdom
Aman Kukreja
Affiliation:
University of Bristol, United Kingdom
Christopher Michael Jason Cox
Affiliation:
University of Bristol, United Kingdom
Chris Snider
Affiliation:
University of Bristol, United Kingdom
*
james.gopsill@bristol.ac.uk

Abstract

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Hands are the sensors and actuators for many design tasks. While several tools exist to capture human interaction and pose, many are expensive and require intrusive measurement devices to be placed on participants and often takes them out of the natural working environment. This paper reports a novel workflow that combines computer vision, several Machine Learning algorithms, and geometric transformations to provide a low-cost non-intrusive means of spatially tracking hands. A ±3mm position accuracy was attained across a series of 3-dimensional follow the path studies.

Keywords

user-centred designvirtual reality (VR)interaction designmachine learningprototyping
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2069 - 2078
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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