Construction of a prediction model for body dimensions used in garment pattern making based on anthropometric data learning
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
The Journal of The Textile Institute
Abstract
Using artificial intelligence to predict body dimensions rather than measuring them physically is a new
research direction in apparel industry. If implemented, this technology can reduce costs and improve
efficiency. In this paper, we proposed a back propagation artificial neural network (BP-ANN) model to
predict pattern making-related body dimensions by inputting few key human body dimensions. In
order to construct the proposed model, anthropometric measurements of 120 young females from the
northeastern region of China were collected. The data were then used for training and the proposed
model. The results showed that the prediction of the developed BP-ANN model is more accurate and stable
than that of linear regression (LR) model. As great as the LR model was at pattern making, the BP-ANN
model is even better. In the future, the precision of the proposed model can be further improved if the size
of the learning data increases. The proposed method can be especially useful in making garment pattern
for form-fitting clothing.
Description
Keywords
Artificial intelligence (AI), Anthropometric measurement, 3D body scanning, Back propagation artificial neural network (BPANN), Linear regression (LR)
Citation
Kaixuan Liu, Jianping Wang, Edwin Kamalha, Victoria Li & Xianyi Zeng (2017): Construction of a prediction model for body dimensions used in garment pattern making based on anthropometric data learning, The Journal of The Textile Institute, DOI: 10.1080/00405000.2017.1315794