Sizing and grading for wearable products

Sizing and grading are widely used to create products to fit selected populations. Currently, the sizing and grading rules are derived from anthropometric measures; however past researches have indicated that it is not very accurate. This study proposes a new technique to use principal component analysis (PCA) on 3D surface points for sizing and grading wearable products. The accuracy of the proposed method is illustrated by developing a sizing and grading rule for the feet. After developing a model using the feet data of 60 participants and validating using the feet data of 10 different participants, results showed that sizing and grading using PCA is more accurate than traditional techniques. Compared with traditional foot sizing, PCA based sizing and grading showed an improvement of about 25% in accuracy. In addition, results also indicated that the grading rule derived from PCA loading was better than the proportional grading. This research provides a new direction to consider when developing the sizing and grading rules. It can be extended to calculate the number of sizes and the size increment for various wearable products.

► Sizing and grading using principal component analysis (PCA) on 3D surface points.
► Sizing and grading using PCA is more accurate than traditional techniques.
► The grading rule derived from PCA loading is better than the proportional grading.