The aim of this repository is to develop S.E.N.T.I.E.N.T. library (Sensor Enhanced Network Technology Integrating Evolving Neural Tools). sentient library includes multi-sensor data methods using SOTA (State-Of-The-Art) AI models for skills assessment in surgery, sports, and robotics. Hence, this repository contains sentient's related material with dependencies, sample-data, scripts, unit tests, docs and references.

Please refer to discussion for questions, ideas, polls, etc. Feel free to open issues instead.

Getting started

You can run notebooks for data analysis. See AI-enabled models (work in progress). If interested in cada collection, please see sensor fusion and video_devices.

Installation

conda create -n "sentientVE" python=3.10 pip
conda activate sentientVE
pip install --editable . # Install the package in editable mode
pip install .[test]
pip install .[learning]
#pip uninstall sentient
#conda deactivate
#conda remove -n sentientVE --all

Pre-commmit

conda activate sentientVE
pre-commit run -a

Testing sensor data

Just test default local camera id 0.

cd $HOME/repositories/sentient
conda activate sentientVE
#export PYTHONPATH=$HOME/repositories/sentient
#export PYTHONPATH="${PYTHONPATH}:$HOME/repositories/sentient"
python -m pytest -v -s tests/
python -m pytest -v -s tests/test_video_capture.py::test_simple_list_of_available_video_devices
python -m pytest -v -s tests/test_video_capture.py::test_capture_video

Notebooks

conda activate sentientVE
cd $HOME/repositories/sentient/notebooks
#export PYTHONPATH="${PYTHONPATH}:$HOME/repositories/sentient"
jupyter notebook --browser=firefox

Clone repository

• Generate your SSH keys as suggested here (or here)
• Clone the repository by typing (or copying) the following line in a terminal at your selected path in your machine:

cd && mkdir -p $HOME/repositories && cd  $HOME/repositories
git clone [email protected]:mxochicale/sentient.git

Publications

@misc{leung-xochicale-rami-icra2023,
      author={
            Tsz Yan Leung and 
            Miguel Xochicale},
      title={
            Towards a Simple Framework of Skill Transfer Learning for 
            Robotic Ultrasound-guidance Procedures}, 
      year={2023},
      eprint={2305.04004},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      howpublished = "\url{https://arxiv.org/abs/2305.04004}"},
      url = {https://github.com/mxochicale/rami-icra2023}
}

Contributors

Thanks goes to all these people (emoji key):

ADD NAME SURNAME 🔬 🤔

Sujon Hekim 🔬 🤔

Miguel Xochicale 💻 📖 🔧

This work follows the all-contributors specification.
Contributions of any kind welcome!