Upper limb anthropometric parameter estimation through convolutional neural network systems and image processing

Abstract

Anthropometry is a versatile tool for evaluating the human body proportions. This tool allows the orientation of public health policies and clinical decisions. But in order to optimize the obtaining of anthropometric measurements, different methods have been developed to determine anthropometry automatically using artificial intelligence. In this work, we apply a convolutional neural network to estimate the upper limb's anthropometric parameters. With this aim, we use the OpenPose estimator system and image processing for segmentation with U-NET from a complete uncalibrated body image. The parameter estimation system is performed with total body images from 4 different volunteers. The system accuracy is evaluated through a global average percentage of 71% from the comparison between measured values and estimated values. A fine-tuning of algorithm hyper-parameters will be used in future works to improve the estimation.