Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear

Martijn ten Bhömer; Hai Ning Liang; Difeng Yu

doi:10.1007/978-3-030-23528-4_30

Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear

Martijn ten Bhömer^*
, Hai Ning Liang
, Difeng Yu

^*Corresponding author for this work

Department of Mechanical, Materials and Manufacturing Engineering

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The relationship between visual appearance and structure and technical properties of a knitted fabric is subtle and complex. This is an area that has been traditionally problematic within the knitting sector, understanding between technologists and designers is hindered which limits the possibility of dialogues from which design innovation can emerge. Recently there has been interest from the Human-Computer Interaction (HCI) community to narrow the gap between product design and knitwear. The goal of this article is to show the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: “How can designers benefit from intelligent design software for the manufacturing of personalized advanced functionalities in textile products?”. In particular we explored how to design interactions and interfaces that use intelligent predictive algorithms through the analysis of a case study, in which several predictive algorithms were compared in the practice of textile designers.

Original language	English
Title of host publication	HCI International 2019 - Posters - 21st International Conference, HCII 2019, Proceedings
Editors	Constantine Stephanidis
Publisher	Springer Verlag
Pages	212-219
Number of pages	8
ISBN (Print)	9783030235277
DOIs	https://doi.org/10.1007/978-3-030-23528-4_30
Publication status	Published - 2019
Event	21st International Conference on Human-Computer Interaction, HCI International 2019 - Orlando, United States Duration: 26 Jul 2019 → 31 Jul 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1033
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	21st International Conference on Human-Computer Interaction, HCI International 2019
Country/Territory	United States
City	Orlando
Period	26/07/19 → 31/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Free Keywords

Knitwear
Machine learning
User Interface

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-23528-4_30

Cite this

ten Bhömer, M., Liang, H. N., & Yu, D. (2019). Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear. In C. Stephanidis (Ed.), HCI International 2019 - Posters - 21st International Conference, HCII 2019, Proceedings (pp. 212-219). (Communications in Computer and Information Science; Vol. 1033). Springer Verlag. https://doi.org/10.1007/978-3-030-23528-4_30

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title = "Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear",

abstract = "The relationship between visual appearance and structure and technical properties of a knitted fabric is subtle and complex. This is an area that has been traditionally problematic within the knitting sector, understanding between technologists and designers is hindered which limits the possibility of dialogues from which design innovation can emerge. Recently there has been interest from the Human-Computer Interaction (HCI) community to narrow the gap between product design and knitwear. The goal of this article is to show the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: “How can designers benefit from intelligent design software for the manufacturing of personalized advanced functionalities in textile products?”. In particular we explored how to design interactions and interfaces that use intelligent predictive algorithms through the analysis of a case study, in which several predictive algorithms were compared in the practice of textile designers.",

keywords = "Knitwear, Machine learning, User Interface",

author = "\{ten Bh{\"o}mer\}, Martijn and Liang, \{Hai Ning\} and Difeng Yu",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 21st International Conference on Human-Computer Interaction, HCI International 2019 ; Conference date: 26-07-2019 Through 31-07-2019",

year = "2019",

doi = "10.1007/978-3-030-23528-4\_30",

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series = "Communications in Computer and Information Science",

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}

ten Bhömer, M, Liang, HN & Yu, D 2019, Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear. in C Stephanidis (ed.), HCI International 2019 - Posters - 21st International Conference, HCII 2019, Proceedings. Communications in Computer and Information Science, vol. 1033, Springer Verlag, pp. 212-219, 21st International Conference on Human-Computer Interaction, HCI International 2019, Orlando, United States, 26/07/19. https://doi.org/10.1007/978-3-030-23528-4_30

Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear. / ten Bhömer, Martijn; Liang, Hai Ning; Yu, Difeng.
HCI International 2019 - Posters - 21st International Conference, HCII 2019, Proceedings. ed. / Constantine Stephanidis. Springer Verlag, 2019. p. 212-219 (Communications in Computer and Information Science; Vol. 1033).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear

AU - ten Bhömer, Martijn

AU - Liang, Hai Ning

AU - Yu, Difeng

PY - 2019

Y1 - 2019

N2 - The relationship between visual appearance and structure and technical properties of a knitted fabric is subtle and complex. This is an area that has been traditionally problematic within the knitting sector, understanding between technologists and designers is hindered which limits the possibility of dialogues from which design innovation can emerge. Recently there has been interest from the Human-Computer Interaction (HCI) community to narrow the gap between product design and knitwear. The goal of this article is to show the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: “How can designers benefit from intelligent design software for the manufacturing of personalized advanced functionalities in textile products?”. In particular we explored how to design interactions and interfaces that use intelligent predictive algorithms through the analysis of a case study, in which several predictive algorithms were compared in the practice of textile designers.

AB - The relationship between visual appearance and structure and technical properties of a knitted fabric is subtle and complex. This is an area that has been traditionally problematic within the knitting sector, understanding between technologists and designers is hindered which limits the possibility of dialogues from which design innovation can emerge. Recently there has been interest from the Human-Computer Interaction (HCI) community to narrow the gap between product design and knitwear. The goal of this article is to show the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: “How can designers benefit from intelligent design software for the manufacturing of personalized advanced functionalities in textile products?”. In particular we explored how to design interactions and interfaces that use intelligent predictive algorithms through the analysis of a case study, in which several predictive algorithms were compared in the practice of textile designers.

KW - Knitwear

KW - Machine learning

KW - User Interface

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DO - 10.1007/978-3-030-23528-4_30

M3 - Conference contribution

AN - SCOPUS:85069657407

SN - 9783030235277

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BT - HCI International 2019 - Posters - 21st International Conference, HCII 2019, Proceedings

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PB - Springer Verlag

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ER -

Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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