Python package to extract gait metrics

IntellEvent is a robust and accurate overground gait event detection algorithm for various pathologies. Here you can find a pipeline for Vicon Nexus to implement in your routine which automatically detects initial contact and foot off events during walking.

An open source gait analysis system with a 3D imaging system

Automatic muscle tendon junction tracking using deep learning 🦵🏼

The emerging domain of tele-rehabilitation aims to eliminate bulky equipment traditionally used in the rehabilitation of those with motor impairment. Our proposed setup of a compact, simplified module for tele-rehabilitation involves a depth camera which detects the joint coordinates of the subject and generate gait cycle charts.

Gait recognition system based on YOLOv8

[a.a. 23/24] Repo for FVAB course-project

A python DIgital Signal ProcEssing Library developed to standardize extraction of sensor-derived measures (SDMs) from wearables or smartphones data.

Fully automated (rodent) limb motion analysis toolbox for behavioral analysis with bodypart coordinate data, building upon markerless pose estimation.

python Convention Gait Model

Extract and visualize gait data

Gait recognition system based on deep learning models.

Code for the biomechanical analysis program for the group 13 TSE project.

Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

Some QA tests performed on the force plates in our Gait Lab

Computer-vision based monocular human pose-estimation and biomechanical gait analysis on elite runners. Using Yolov7 pose-estimation algorithm.

Gait analysis data pipeline for publication "TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis".

Scripting and anonymized datasets created through soft sensors research done by Athlete Engineering.

This project uses csv data obtained from an inertial measurement device, IMU to predict and model the human gait cycle. It can be used to calculate the number of steps taken, the type of motion i.e. walking, running and to identify errors and issues in the gait cycle.