Motion-Print: A Biometric for Real-Time Pilot Identification using
Hierarchical Temporal Memory
Abstract
This study presents a novel biometric approach to identify operators,
given only streams of their control movements within a manual control
task setting. In the present task subjects control a simulated, remotely
operated robotic arm, attempting to dock onto a satellite in orbit. The
proposed methodology utilizes the Hierarchical Temporal Memory (HTM)
algorithm to distinguish operators by their unique control behaviors.
Results presented compare the identification performance of HTM with
Dynamic Time Warping (DTW) and Edit Distance on Real Sequences (EDR), in
both static and real-time data settings. The HTM method outperformed
both DTW and EDR in the real- time setting, and matched DTW in the
static setting. Observed superior performance of the HTM algorithm lays
the foundation for the extension of the proposed methodology to other
motion- monitoring applications, such as real-time workload assessment,
motion/simulator sickness onset or distraction detection.
The data gathered in the study was posted to IEEE-dataport, DOI:
10.21227/wpyf-r927