SmartGait

Gait Phase Estimation by employing Artificial Intelligence

This project aims to pave the way for the development of IMU-based gait phase estimation by employing the state-of-the-art technology, in particular deep learning algorithms.

This repository consists of:

1. "Design-models-from-scratch" directory, for those who would like to design these models on their own from scratch.
2. "Employ-trained-models" directory, please check Employ_trained_models directory if you want to use and test the trained models.

Moreover, the project will grow itselft step-by-step, therefore more trained models will be presented in this repo in the future. At this moment, the first SmartGait model, "SmartGait_01" is already trained by using data from only two individuals, which is still not a completed trained model for production, however, it is a proof of our concept for using LSTM algorithm to estimate three main gait phases, foot-off, mid-swing, and foot-contact.

Install Python

*) For this Repo the Python version 3.9.6 was used. Please download Python from: https://www.python.org/downloads/

Create a virtual environment for this project as follows:

Create a new virtual environment by choosing a Python interpreter and making a .\venv directory to hold it:

1. python -m venv .\venv

Activate the virtual environment:

2. .\venv\Scripts\activate

Install required libraries from the requirements.txt file

3. pip install -r requirements.txt

Note: To exit the virtual environment later:

-) deactivate # don't exit until you're done with your project

Related Articles/Publications:

1. Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis — Proof of Concept