Metaverse Gait Authentication Dataset (MGAD)

This dataset contains gait-based biometric data collected from 5,000 users in a simulated environment for gait authentication in the Metaverse. It includes 16 key gait features extracted using OpenPose and MediaPipe and processed with feature engineering techniques for improved usability.

The dataset is valuable for gait-based authentication, user identification, and biometric security applications. It can be used for machine learning models, deep learning, and anomaly detection in gait recognition research.

Features include:

Stride length, step frequency, stance phase duration, swing phase duration

Hip, knee, and ankle joint angles

Ground reaction forces (GRFs), cadence variability, foot clearance

Gait symmetry index and more

Format: CSVLicense: CC BY 4.0 (Attribution Required)Citation: If using this dataset, please cite:Sandeep Ravikanti (2024). "Metaverse Gait Authentication Dataset (MGAD)." Zenodo. DOI: [10.5281/zenodo.14847773]