Size prediction of older women’s lower body based on shape classification

Accurately predicting lower body dimensions in older women is essential for improving garment fit and wearing comfort. However, challenges in data acquisition and the morphological variability among older adults complicate this task. To address these issues, this study adopts a classification followed by modeling strategy, using a small set of key anthropometric inputs to estimate critical lower-body measurements relevant to pants pattern construction. Anthropometric data were collected from 217 women aged 60–80 in Northeast China, capturing 34 lower body parameters. Principal component analysis was performed to reduce dimensionality and extract six primary body shape factors. A two-step clustering method was then applied to determine the optimal number of body types and to analyze their morphological characteristics. Subsequently, stepwise linear regression models were developed for each body type using stature, weight, and abdominal girth as predictor variables. The results demonstrate that the proposed models achieved good prediction accuracy. Overall, this research provides a data-driven foundation for customized pants pattern development and intelligent production of garments tailored for older women.