Liu, KaixuanWang, JianpingKamalha, EdwinLi, VictoriaZeng, Xianyi2022-12-042022-12-042017Kaixuan 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: 10.1080/00405000.2017.131579410.1080/00405000.2017.1315794https://nru.uncst.go.ug/handle/123456789/5813Using 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.enArtificial intelligence (AI)Anthropometric measurement3D body scanningBack propagation artificial neural network (BPANN)Linear regression (LR)Article