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Clustering of the body shape of the adult male by using principal component analysis and genetic algorithm–BP neural network

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Abstract

In order to improve the efficiency and accuracy of human body shape prediction, principal component analysis method (PCA) is proposed to reduce the dimension of related variables and eliminate the multicollinearity among variables. Then, the transformed variables are input into genetic algorithm and BP neural network, and a new method of human body shape prediction is designed. To avoid the problems that slow convergence speed and easy falling into local minima of BP neural network, the genetic algorithm is used to optimize the weights and thresholds of BP neural network. Moreover, to prove the superiority of PCA–GA–BP model, the prediction results are compared with those of other algorithms. Body sizes of 18–25-year-old, 26–44-year-old and 45–59-year-old males were selected as experimental data to analyze these models. The prediction results of GA–BP, PCA–BP, BP, SVM and K-means were compared with PCA–GA–BP neural network. The results show that the prediction effect of PCA–GA–BP neural network is significantly better than that of GA–BP, PCA–BP, BP, SVM and K-means prediction models, which can accurately predict and cluster the human body shape. The model has better prediction and classification and simpler structure.

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Abbreviations

H :

Height

Bnh:

Back neck height

Wh:

Waist height

Hh:

Hip height

Bh:

Bust height

Ph:

Perineum point height

Ah:

Abdominal height

Kh:

Knee height

HBh:

Hip bone height

Ng:

Neck girth

Bg:

Bust girth

UAg:

Upper-arm girth

Wg:

Waist girth

Ag:

Abdominal girth

Hg:

Hip girth

UHg:

Upper-hip girth

Tg:

Thigh girth

Mtg:

Mid-thigh girth

Cg:

Calf girth

Ang:

Ankle girth

Kg:

Knee girth

Sg:

Shoulder tips distance

Cb:

Chest breadth

Bb:

Back breadth

Wb:

Waist breadth

Hb:

Hip breadth

HBb:

Hip bone breadth

Th:

Torso height

Wbl:

Waist back length

Aml:

Arm length

UAl:

Upper-arm length

SNWl:

Side neck point to waist level

Sbr:

Straight body rise

TCl:

Total crotch length

Tl:

Thigh length

SWH:

Side waist to hip

Abul:

Abdominal bulge

Hbul:

Hip bulge

HBbul:

Hip bone bulge

Bd:

Bust depth

Ad:

Abdominal depth

Wd:

Waist depth

Hd:

Hip depth

Td:

Thigh depth

AHd:

Abdomen-to-hip depth

Ss:

Shoulder slope

Bi:

Back inclination

Ai:

Abdominal inclination

upHi:

Upward inclination of posterior hip bulge

dpHi:

Downward inclination of posterior hip bulge

usHi:

Upward inclination of hip side

Gi:

Gluteal inclination

Wt:

Weight

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Acknowledgements

The authors would like to acknowledge the financial support from the Fundamental Research Funds for the Central Universities (223 + 2019 + G-08) and national key research and development plan “science and technology in Winter Olympic Games” (2019YFF0302100).

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Authors and Affiliations

  1. College of Fashion and Design, Donghua University, Shanghai, China

    Pengpeng Cheng & Jianping Wang

  2. Clothing and Design Faculty, Minjiang University, Fuzhou, China

    Daoling Chen

  3. Shanghai Institute of Design and Innovation, Tongji University, Shanghai, China

    Jianping Wang

  4. Key Laboratory of Clothing Design and Technology (Donghua University), Ministry of Education, Shanghai, China

    Jianping Wang

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  1. Pengpeng Cheng
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Correspondence to Jianping Wang.

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Cheng, P., Chen, D. & Wang, J. Clustering of the body shape of the adult male by using principal component analysis and genetic algorithm–BP neural network. Soft Comput 24, 13219–13237 (2020). https://doi.org/10.1007/s00500-020-04735-9

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