Name
Title | ||
|---|---|---|
A Data-Driven Approach Towards The Full Anthropometric Measurements Prediction Via Generalized Regression Neural Networks |
Abstract | ||
|---|---|---|
Anthropometry is a science concerning human body dimension measurements and proportions that is widely applied in multiple disciplines, particularly in apparel and "human-centered" product design. Designers and engineers generally use percentile anthropometric data that leads to significant errors in practice. Although adopting non-statistic body dimensions could resolve this issue, gaining detailed body measurements is a challenging task as neither manual measurement nor 3D scanning is efficient in a cost-effective manner. With the rapid development of artificial neural networks, predicting body sizes and shapes, instead of measuring them, has become a new trend. This work presents a unique Generalized Regression Neural Network architecture that is capable of accurately predicting 76 detailed body measurements from seven easily measured body features with high tolerance to input measurement errors. The proposed model outperforms the existing regression models and can be easily implemented in the design process. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.asoc.2021.107551 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Anthropometric dimension, Data-driven, Generalized Regression Neural Network | Journal | 109 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lining Wang | 1 | 0 | 0.34 |
| Tien Ju Lee | 2 | 0 | 0.34 |
| Jan Bavendiek | 3 | 0 | 0.34 |
| Lutz Eckstein | 4 | 21 | 13.16 |