EarGait - The Gait Analysis Package for Ear-Worn IMU Sensors !

EarGait provides a set of algorithms and functions to process IMU data recorded with ear-worn IMU sensors and to estimate characteristic gait parameters.

Getting started

Installation

Easily install eargait via pip: pip install eargait

or add it to your project with poetry: poetry add eargait

Newest version 1.2.0 should be installed.

Prerequisites

EarGait only supports Python 3.8 and newer. First, install a compatible version of Python.

Help with setting up a virtual environment

We recommend installing the packages in a virtual environment (e.g. conda/Anaconda/miniconda). For more information regarding Anaconda, please visit Anaconda.com.
If you want to install the packages directly on the local python version, directly go to Install Packages

If you are familiar with virtual environments you can ``also use any other type of virtual environment. Furthermore, you can also directly install the python packages on the local python version, however, we would not recommend doing so.

In PyCharm
See documentation.

Shell/Terminal
First, verify that you have a working conda installation. Open a terminal/shell and type conda env list If an error message similar to the one below is displayed, you probably do not have a working conda version installed. conda: command not found In the shell/terminal: conda create --no-default-packages -n gait_analysis python=3.8 gait_analysis is the name of the virtual environment. This environment can now also be included in PyCharm, as described See here by using the existing environment option.
To check, whether the virtual environment has been created successfully, run again: conda env list The environment gait_analysis should now be displayed.
Activate conda environment and install packages (see below).

conda activate gait_analysis

For more help: Conda Documentation

Install Package in virtual environment

If you are using the conda environment, activate environment (in shell/terminal) (see above). Update pip and install eargait. pip install --upgrade pip pip install eargait

Check successful installation

To check whether the installation was successful, run the following line directly after installing eargait in the same shell/terminal: python examples/check_installation/check_installation.py Should return: Installation was successful!

Learn More

Documentation, User Guide, Coordinate Systems

Dev Setup

We are using poetry to manage dependencies and poethepoet to run and manage dev tasks.

To set up the dev environment including the required dependencies for using EarGait run the following commands: git clone https://github.com/mad-lab-fau/eargait cd eargait poetry install Afterwards you can start to develop and change things. If you want to run tests, format your code, build the docs, ..., you can run one of the following poethepoet commands

CONFIGURED TASKS format lint Lint all files with Prospector. check Check all potential format and linting issues. test Run Pytest with coverage. docs Build the html docs using Sphinx. bump_version by calling poetry run poe <command name>

Citing EarGait

If you use Eargait in your work, please report the version you used in the text. Additionally, please also cite the corresponding paper:

``` [1] Seifer, Ann-Kristin, et al., "EarGait: estimation of temporal gait parameters from hearing aid integrated inertial sensors." Sensors 23(14), 2023. DOI: 10.3390/s23146565.

[2] Seifer, Ann-Kristin, et al. "Step length and gait speed estimation using a hearing aid integrated accelerometer: A comparison of different algorithms." IEEE Journal of Biomedical and Health Informatics (2024). DOI: 10.1109/JBHI.2024.3454824.

[3] Seifer, Ann-Kristin, et al. "Fully automated gait analysis with earables: Evaluation of an End2End pipeline with hearing-aid integrated accelerometers." 47th Annual Inter- national Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025. --> ACCEPTED, SOON TO BE PUBLISHED ```

Links:
[1] Seifer et al., (2023), Temporal Parameter Paper
[2] Seifer et al., (2024); Spatial Parameter Paper
[3] Seifer et al., (2025); End2End Pipeline Paper

Acknowledgement

EarGait is part of a research project from the Machine Learning and Data Analytics Lab, Friedrich-Alexander Universitt Erlangen-Nrnberg. The authors thank WS Audiology, Erlangen, Germany and Lynge, Denmark for funding the work and their support which made this contribution possible.

Contribution

The entire development is managed via GitHub. If you run into any issues, want to discuss certain decisions, want to contribute features or feature requests, just reach out to us by opening a new issue.