action_prediction

Project Description

This project is part of the final project of my PhD thesis. I collected 12 hours of varsity soccer players taking penalty shots at 16 unique targets (incorporating a difficulty manipulation).Then, I designed an experiment to test human participants on their ability to predict the direction of the ball (left or right) and I used behavioral performance and eye-tracking as metrics of learning. Next, I used deeplabcut (http://www.mackenziemathislab.org/deeplabcut) to perform markerless labeling of the players' effectors, and I trained a model on these features to determine accuracy of the model relative to human learners. This project is still a work in progress so the final model is still undetermined (hopefully soon!).

NOTE: I also collected 15 hours of actors engaging in social greetings: hugging, high-fiving, shaking hands in order to make action predictions in a social context. I'm currently analyzing these data.

Markerless labeling of soccer players taking penalty shots

I used deeplabcut to label in real-time the effectors (shoulder, knee, arm, leg etc.) of 3 varsity soccer players as they took directed penalty shots at 16 unique targets. One example video:

Plot poses tracking the player's body movements

The relative position of the effectors are plotted in 2D as the soccer player runs and takes a penalty shot. X and Y positions are given in pixels.

Research Assistants: Installing the Required Python Packages

This project uses pipenv for virtual environment and python package management. Make sure that you have installed pyenv and pipenv.

Install pyenv using Homebrew:

$ brew update
$ brew install pyenv

Add pyenv init to your shell:

$ echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init -)"\nfi' >> ~/.bash_profile
$ source ~/.bash_profile

Install the required version of python:

$ pyenv install 3.7.0

Ensure pipenv is installed globally:

$ brew install pipenv

Navigate to the top-level directory in action_prediction and install the packages from the Pipfile.lock. This will automatically create a new virtual environment for you and install all requirements using the correct version of python.

$ pipenv install

Activating the virtual environment:

$ pipenv shell

NOTE: To deactivate the virtual environment when you are done working, simply type exit

Activating the ipykernel

$ python -m ipykernel install --user --name action_prediction

Data Organization

├── data
│   ├── eyetracking    <- eyetracking data `group_eyetracking.csv`
│   └── behavior       <- behavioral data `group_behavior.csv`

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── Pipfile            <- The requirements file for reproducing the analysis environment
│
├── setup.py           <- makes project pip installable (pipenv install -e .) so src can be imported
├── analysis_code      <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
|   |
│   |── constants.py   <- Module to set default directories for project
│   │
|   |──scripts         <- folder that contains scripts
|   |
│   │── visualize_eye.py  <- Module to visualize eyetracking
│   │
│   └── visualize_behavior.py  <- Module to visualize behavior
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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