mmGait10 - Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds

The adoption of mmWave radar for human sensing has gained significant attention due to its efficiency, robustness to environmental conditions, and privacy-preserving nature. In particular, gait recognition using radar point clouds presents new opportunities for unobtrusive and resilient biometric authentication and activity analysis. Unlike more traditional representations employed in the radar sensing literature (e.g. micro-Doppler signatures), point cloud data is well-suited for edge computing applications due to its compactness, but pose new challenges due to their noisy and sparse nature.

This dataset was collected to build and validate an original neural network model for open-set gait recognition (OSGR) from sparse radar point clouds. 

The dataset comprises approximately 5 hours of radar point cloud measurements collected from 10 subjects with diverse physical characteristics (sex, height, and weight) to ensure a realistic variety of walking patterns. The captures were recorded in a 7.81 × 7.26 m indoor environment, where each participant was instructed to walk freely along random trajectories.

To enhance diversity and realism, gait recordings were performed with three different walking manners:

Walking freely

Walking while holding a smartphone

Walking with hands in pockets

Each subject was recorded for approximately 30 minutes, with 10 minutes per walking condition, providing a rich and balanced variability in gait patterns.

All the measurements were collected with a commercial Texas Instruments MMWCAS-RF-EVM FMCW Radar, operating in the 77-81 GHz frequency band at a frame rate of 10 Hz. 

For additional details and information about the dataset, the user is redirected to our paper "Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds", available at this link.

This dataset is intended to support research in gait analysis, human activity recognition, and radar-based sensing applications.