GaitCube: Deep Data Cube Learning for Human Recognition With Millimeter-Wave Radio

Monitoring and identifying gait has recently emerged as a promising solution candidate for unobtrusive human recognition. In order to enable ubiquitous and reliable application, a gait recognition system must be robust to environment changes and easy to use without requiring too much user cooperation and recalibration, while maintaining high accuracy, which is often not satisfied in conventional approaches. In this article, we present
$\boldsymbol {GaitCube}$
, a high-accuracy gait recognition system with the minimal training requirement using a single commodity millimeter-wave (mmWave) radio. To reduce the training overhead, we propose
gait data cube
, a novel 3-D joint-feature representation of micro-Doppler and micro-range signatures over time that can comprehensively embody the physical relevant features of one’s gait. With a pipeline of signal processing,
$\boldsymbol {GaitCube}$
can automatically detect and segment human walking and effectively extract the
gait data cubes
. We implement and evaluate
$\boldsymbol {GaitCube}$
through experiments conducted at six different locations in a typical indoor space with ten subjects over a month, resulting in >50000 gait instances. The results show that
$\boldsymbol {GaitCube}$
achieves an accuracy of 96.1% with a single gait cycle using one receive antenna, and the accuracy increases to 98.3% when combining all the receive antennas. Further, it achieves an average recognition accuracy of 79.1% for testing over different times and unseen locations by using only 2 min of training data collected in a single location, enabling a practical and ubiquitous gait-based identification.