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Motion-Print: A Biometric for Real-Time Pilot Identification using Hierarchical Temporal Memory

preprint
posted on 04.06.2020 by Sam Heiserman, Kirill Zaychik, Timothy Miller

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


Funding

NASA/New York Space Grant Consortium National Space College and Fellowship Program, sponsored by NASA Goddard Space Flight Center

History

Email Address of Submitting Author

sheiser1@binghamton.edu

ORCID of Submitting Author

https://orcid.org/0000-0002-0722-8230

Submitting Author's Institution

Watson School, Binghamton University

Submitting Author's Country

United States of America

Licence

Exports