Inferring complex textile shape from an integrated carbon black-infused ecoflex-based bend and stretch sensor array

Garcia Garcia, Leonardo A, Valsamakis, George, Kreitmair, Paul M, Munzenrieder, Niko and Roggen, Daniel (2021) Inferring complex textile shape from an integrated carbon black-infused ecoflex-based bend and stretch sensor array. Ubicomp 2021, Online, September 21 - 26, 2021. Published in: UbiComp '21 conference. 298-303. Association for Computing Machinery, New York, NY, United States. ISBN 9781450384612

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Abstract

We demonstrate how an array of custom-made strain and bend sensors could be integrated into a stretchable sleeve to infer the textile deformation. The angles and elongation measured by the sensors can be used by an optimisation-based algorithm to infer the textile geometrical model by minimising a loss function. We evaluated this on 4 shapes highlighting different body-part characteristics. We demonstrated that a 3.11 mm reconstruction error on complex geometries can be reduced up to 0.08 mm with the computation of angles. This proves the potential of the proposed prototype for capturing the shape of a body parts, muscle density measurement, body shape acquisition, the fabrication of orthoses and prostheses, or to perform movement sensing for human activity recognition, where it could be included in sports leggings for biomechanical analysis, or in everyday garments for motion and gesture sensing.

Item Type: Conference Proceedings
Keywords: Smart textile, wearable sensing, textile sensing, activity recognition
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 06 Aug 2021 08:38
Last Modified: 12 Oct 2021 11:17
URI: http://sro.sussex.ac.uk/id/eprint/100955

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