https://github.com/ami-iit/human-model-generator
Python-based tool to generate anthropometric human whole-body models in a URDF format
Science Score: 39.0%
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Low similarity (12.8%) to scientific vocabulary
Keywords
Repository
Python-based tool to generate anthropometric human whole-body models in a URDF format
Basic Info
Statistics
- Stars: 23
- Watchers: 17
- Forks: 3
- Open Issues: 1
- Releases: 4
Topics
Metadata Files
README.md
Human Model Generator (HMG) 
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. 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. Muscle modeling is essential for a comprehensive understanding of 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 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, such as inertial parameters of body segments and anthropometric measurements. For a more accurate representation of the human torso, the model was designed by following the spinal cord, dividing it into four areas made up of a series of vertebrae. The four links were constructed by first selecting the vertebrae to highlight and then building the links based on a table that defines the length of each vertebra in proportion to the total length of the spinal cord. Therefore, the goal of the HMG is to develop an advanced human model that integrates both skeletal and muscular information. This model not only includes detailed anatomical and inertial data, but it is also scalable to accommodate the specific features of each human subject. The HMG also incorporates meshes for both the links and muscles, which are modeled to enhance the visual and physical representation of the human body. Additionally, to fully visualize the model, meshes for the spinal cord were added, extending from the pelvis to the neck.
Dependencies
This library requires the following dependencies:
Installation with conda (recommended)
Create and activate a brand new enviroment with the required dependencies:
conda create -n hmgenv python numpy urchin idyntree urdf-modifiers
conda activate hmgenv
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
Citing this work
If you find this work useful, please use the following bibtex as a reference:
@inproceedings{HMGsiamoc,
title = {An automatic anthropometric model generation tool for scalable human whole-body musculoskeletal modeling},
url = {https://doi.org/10.6092/unibo/amsacta/7898},
doi = {10.6092/unibo/amsacta/7898},
series = {Proceedings {SIAMOC}},
booktitle = {Proceedings {XXIV} Congresso {SIAMOC} 2024},
author = {Fiori, Lorenzo and Latella, Claudia and Tatarelli, Antonella and Pucci, Daniele},
date = {2024}
}
License
The meshes for the links are derived from the Blendswap model under the CC-BY license, whereas the meshes for the muscles are derived from BodyParts3D and Blendswap, both under the CC-BY-SA license. All the meshes were trimmed, morphed, and totally or partially reconstructed to achieve the desired shape and topology.
Owner
- Name: Artificial and Mechanical Intelligence
- Login: ami-iit
- Kind: organization
- Location: Italy
- Website: https://ami.iit.it/
- Repositories: 111
- Profile: https://github.com/ami-iit
GitHub Events
Total
- Issues event: 4
- Watch event: 14
- Delete event: 3
- Issue comment event: 16
- Push event: 22
- Pull request review comment event: 4
- Pull request review event: 10
- Pull request event: 5
- Fork event: 2
- Create event: 3
Last Year
- Issues event: 4
- Watch event: 14
- Delete event: 3
- Issue comment event: 16
- Push event: 22
- Pull request review comment event: 4
- Pull request review event: 10
- Pull request event: 5
- Fork event: 2
- Create event: 3
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 8
- Total pull requests: 18
- Average time to close issues: 4 months
- Average time to close pull requests: 5 days
- Total issue authors: 5
- Total pull request authors: 5
- Average comments per issue: 3.63
- Average comments per pull request: 0.5
- Merged pull requests: 18
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 3
- Pull requests: 9
- Average time to close issues: 15 days
- Average time to close pull requests: 3 days
- Issue authors: 2
- Pull request authors: 4
- Average comments per issue: 4.67
- Average comments per pull request: 0.56
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- AntonellaTatarelli (2)
- lrapetti (2)
- LudovicaDanovaro (2)
- claudia-lat (1)
- LorenzoFiori (1)
Pull Request Authors
- LorenzoFiori (13)
- claudia-lat (7)
- LudovicaDanovaro (4)
- traversaro (2)
- lrapetti (1)