Predicting Clothing Sensory Comfort with Artificial Intelligence Hybrid Models

Wong, Antony King Fung; Li, Yi; Yeung, P.K.W.

doi:10.1177/004051750407400103

Cited by 62 publications

(39 citation statements)

References 10 publications

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“…Seven items of subjective attributes were listed as bipolar descriptors on a 7-point Likert scale, which is widely used to measure subjective perceptions (Wong et al, 2004;Chan et al, 2013b). The meanings of scale 1-7 of each attribute were represented as A1 -from hot to cool, A2 -from damp to dry, A3 -from clammy to dry[2], A4 -from airtight to breathable, A5 -from thick and heavy to thin and light, A6 -from work performance interfered to non-job performance interfered, and overall comfort -from uncomfortable to comfortable.…”

Section: Wearing Comfortmentioning

confidence: 99%

“…To measure the performance of the above models, the root mean square error (RMSE) and correlation coefficient (r-value) of each model were calculated using developing and simulation data sets, respectively (Jeguirim et al, 2011;Wong et al, 2004;Hui and Ng, 2009) (Table V). The RMSE indicates the model precision between the predicting results and the actual data (Psikuta et al, 2012), whereas the r-value measures the strength and direction of their correlations.…”

Section: Prediction Performance Of the Modelsmentioning

confidence: 99%

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Wearing comfort of two construction work uniforms

Chan

Yang

Wong

et al. 2015

Construction Innovation

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Purpose – The aim of this study is to investigate wearing comfort of summer work uniforms judged by construction workers. Design/methodology/approach – A total of 189 male construction workers participated in a series of wear trials and questionnaire surveys in the summer of 2014. They were asked to randomly wear two types of work uniforms (i.e. uniforms A and B) in the two-day field survey and the subjective attributes of these uniforms were assessed. Three analytical techniques, namely, multiple regression, artificial neural network and fuzzy logic were used to predict wearing comfort affected by the six subjective sensations. Findings – The results revealed that fuzzy logic was a robust and practical tool for predicting wearing comfort in terms of better prediction performance and more interpretable results than the other models. Pressure attributes were further found to exert a greater effect than thermal–wet attributes on wearing comfort. Overall, the use of uniform B exhibited profound benefits on wearing comfort because it kept workers cooler, drier and more comfortable with less work performance interference than wearing uniform A. Originality/value – The findings provide a fresh insight into construction workers’ needs for work clothes, which further facilitates the improvement in the clothing tailor-made design and the enhancement of the well-being of workers.

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Section: Wearing Comfortmentioning

confidence: 99%

Section: Prediction Performance Of the Modelsmentioning

confidence: 99%

Wearing comfort of two construction work uniforms

Chan

Yang

Wong

et al. 2015

Construction Innovation

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show abstract

“…Compared with statistical modeling techniques, the neural network was a fast, flexible, predictive tool with a selflearning ability for clothing comfort perceptions. Wong et al, (2004) investigated the process of human psychological perceptions of clothing related sensations and comfort to develop an intellectual understanding of and methodology for predicting clothing comfort performance from fabric physical properties. Various hybrid models were developed using different modeling techniques by studying human sensory perception and judgement processes.…”

Section: Review Of Application Of Artificial Neural Network In Textimentioning

confidence: 99%

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

Chiu¹,

Fun²,

Ip³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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“…The coefficient of determination (R 2 ) value between actual and predicted comfort property is 0.844 and 0.815 for smaller and larger data sets, respectively. In a subsequent piece of research, Wong et al (2004) predicted the clothing comfort using 3 different hybrid artificial intelligence methods as shown in Figure 2. Three different methods; namely, linear modelling, neural network and fuzzy logic, were implemented in the final stage to predict the clothing comfort from the sensory factors.…”

Section: Fabric Comfortmentioning

confidence: 99%

Soft Computing Applications in Fabrics and Clothing: A Comprehensive Review

Majumdar

Mitra

Banerjee

et al. 2010

Research Journal of Textile and Apparel

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. ABSTRACTThis paper presents a comprehensive review of soft computing applications in the domain of fabrics and clothing. In the last two decades, soft computing techniques, such as artificial neural network, fuzzy logic and genetic algorithm, have been used abundantly for fabrics and clothing modelling, manufacturing, quality control and marketing. This review is aimed at presenting a compendium of research work done so far on the applications of soft computing techniques in fabrics and clothing science and engineering. In the beginning of the paper, a brief introduction of soft computing techniques is provided. Then, the applications of soft computing methods in fabric property modelling (tensile, bending, shear, drape, handle, comfort, thickness and compression, air permeability, porosity, etc.) are provided. In the subsequent sections, soft computing applications for fabric defect identification in static and dynamic conditions, fabric classification, fabric engineering, machine control and marketing, are discussed. The scope of future applications is included in the concluding section.

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Predicting Clothing Sensory Comfort with Artificial Intelligence Hybrid Models

Cited by 62 publications

References 10 publications

Wearing comfort of two construction work uniforms

Wearing comfort of two construction work uniforms

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

Soft Computing Applications in Fabrics and Clothing: A Comprehensive Review

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