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Python-based tool to generate anthropometric human whole-body models in a URDF format

License: BSD 3-Clause "New" or "Revised" License

Python 100.00%
biomechanical-analysis biomechanics-analysis human-biomechanics human-modeling urdf urdf-model urdf-models

human-model-generator's Introduction

Human Model Generator (HMG) License

The Human Model Generator is a Python-based tool designed to generate anthropometric human whole-body models in the Unified Robot Description Format (URDF) standard, suitable in robotics for motion analysis and simulation applications.

Introduction

The movement of the human body is made possible through the synergistic action of the nervous, muscular, and skeletal systems. The nervous system sends signals to the muscles, causing them to contract and exert forces on the skeletal system. This intricate interaction between these systems enables complex and coordinated motions. The musculoskeletal models offer a valuable approximation of the human anatomy and they are used extensively in fields like biomechanics, robotics, and computer simulations to study and mimic human movement. The muscle modeling is essential for a comprehensive understanding of the human movement. This involves the simulation of the behavior of muscles, including their activation patterns, force production, and interaction with the skeletal system. Accurate muscle models can predict how the whole-body movement and performance are affected by changes in muscle strength, coordination, and fatigue. However, for a more accurate representation, additional data is required to customize models for different subjects, e.g., inertial parameters of body segments and anthropometric measurements. Therefore, the goal of the HMG is to develop an advanced human model that integrates both skeletal and muscular information. This model does not only include detailed anatomical and inertial data but also it is scalable to accommodate the specific features of each human subject.



Dependencies

This library requires the following dependencies:

Installation with conda (recommended)

  • Create and activate a brand new enviroment
conda create -n human_model_env
conda activate human_model_env
git clone https://github.com/icub-tech-iit/urdf-modifiers.git
cd urdf-modifiers
pip install .

Usage

git clone https://github.com/ami-iit/human-model-generator.git
cd human-model-generator/code
  • Open the file config.py with a text editor
  • Manually modify the parameters according to the human subject anthropometric measurements (see this file)
  • Generate the model by running python main.py
  • The URDF model will be saved in the folder models/humanModels

Maintainers

Lorenzo Fiori Claudia Latella

human-model-generator's People

Contributors

antonellatatarelli avatar claudia-lat avatar danielepucci avatar lorenzofiori avatar lrapetti avatar

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Forkers

jturner65

human-model-generator's Issues

Reduce the number of DoFs of the URDF model

In this issue, we will look at the degrees of freedom of the model created by HMG.
The model currently has 60 degrees of freedom and is hence overcomplicated.
One approach to reduce this complexity is to consider only the motions that the links can really make in space.

Release first version

We should release a first version of the software tool able to generate URDF models of subjects starting from anthropometric measurements.

While doing this we should structure the repository such that it can be easily installed and utilized.

TODO:

  • Open PR with the Python scripts that generates the model (#1)
  • Open PR for readme and documentation (#3)
  • Open PR with the Python project structure

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