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arXiv · 2105.10067

Towards Automatic Sizing for PPE with a Point Cloud Based Variational Autoencoder

Jacob A. Searcy·Susan L. Sokolowski
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Abstract

Sizing and fitting of Personal Protective Equipment (PPE) is a critical part of the product creation process; however, traditional methods to do this type of work can be labor intensive and based on limited or non-representative anthropomorphic data. In the case of PPE, a poor fit can jeopardize an individual's health and safety. In this paper we present an unsupervised machine learning algorithm that can identify a representative set of exemplars, individuals that can be utilized by designers as idealized sizing models. The algorithm is based around a Variational Autoencoder (VAE) with a Point-Net inspired encoder and decoder architecture trained on Human point-cloud data obtained from the CEASAR dataset. The learned latent space is then clustered to identify a specified number of sizing groups. We demonstrate this technique on scans of human faces to provide designers of masks and facial coverings a reference set of individuals to test existing mask styles.

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Jacob A. Searcy, Susan L. Sokolowski. 2021-05-21. Towards Automatic Sizing for PPE with a Point Cloud Based Variational Autoencoder. https://arxiv.org/abs/2105.10067

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