A curated, public list of resources for biomechanics and human motion analysis: datasets, processing tools, software for simulation, educational videos, lectures, etc.

Package for analyzing human motion data (e.g. PA, gait)

Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

A research project that aims to detect Parkinson's disease in patients using Gait Analysis data. Subsequently, the project may make use of Gait Data Analysis to make powerful inferences which would help in genralizing the most common groups affected by this disease.

GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Networks

A Comprehensive Study on Cross-View Gait Based Human Idendification with Deep CNNs

Extract and visualize gait data

Gait recognition system based on deep learning models.

Code and Data used for the paper "Explaining Machine Learning Models for Clinical Gait Analysis"

[TIFS 2019] Skeleton-based Gait Recognition via Robust Frame-level Matching (RFM)

python Convention Gait Model

A step-counting model based on self-supervised learning for wrist-worn accelerometer data.

This project generates the motor control commands for exoskeleton.

Developing economical sensor systems for Gait data acquisition, analysis and visualization

MATLAB implementation of gait cycle validation and segmentation using inertial sensors.

Automatic muscle tendon junction tracking using deep learning 🦵🏼

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.

Deep Learning Models for the Early Detection of Parkinson’s Disease using the motor-based symptoms.

Raw dataset from "Signal Processing and Machine Learning for Diplegia Classification" and "Gait-Based Diplegia Classification Using LSMT Networks"

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