Machine Learning to Define Anthropometric Landmarks for Relevant Product Design 2D Blueprint Measures

Abstract

Functional designers use 3D body scan measurements to create 2D pattern blueprints, to develop products that size and fit bodies appropriately - to enable safety, comfort, and activity-related performance. To gather measures, surface anthropometric landmarks are critical, to enable accuracy and consistency between scans. However, many 3D scan databases do not include data with anthropometric landmarks, making bodies challenging to measure. Therefore, the purpose of this research was to develop a machine learning (ML) model for the automatic landmarking of 3D body scans from raw point clouds. A deep neural network model was developed, using the Civilian American and European Surface Anthropometry Resource (CAESAR) scan dataset (2002) for training. The model enabled 3D scans from any device that outputs in color to be used for landmark automation. Results of this work have also demonstrated that ML landmarking can enable bulk processing of 3D body scan point cloud data more efficiently compared to traditional manual landmarking methods.