Gait-based Person Identification using Multiple Inertial Sensors

Osama Adel, Yousef Nafea, Ahmed Hesham, Walid Gomaa

2020

Abstract

Inertial sensors such as accelerometers and gyroscopes have gained popularity in recent years for their use in human activity recognition. However, little work has been done on using these sensors for gait-based person identification. Gait-based person identification turns out to be important in applications such as where different people share the same wearable device and it is desirable to identify who is using the device at a given time while walking. In this research, we present the first multi-sensory gait-based person identification dataset EJUST-GINR-1 and present our work on gait-based person identification using multi-sensory data, by mounting 8 wearable inertial sensory devices on different body locations and use this data to identify the person using it. Two of these sensors are smart watches worn on both wrists. We explore the correlation between each body location and the identification accuracy, as well as exploring the effect of fusing pairs of sensory units in different locations, on the final classification performance.

Paper Citation

in Harvard Style

Adel O., Nafea Y., Hesham A. and Gomaa W. (2020). Gait-based Person Identification using Multiple Inertial Sensors.In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-442-8, pages 621-628. DOI: 10.5220/0009791506210628

in Bibtex Style

@conference{icinco20,
author={Osama Adel and Yousef Nafea and Ahmed Hesham and Walid Gomaa},
title={Gait-based Person Identification using Multiple Inertial Sensors},
booktitle={Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2020},
pages={621-628},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009791506210628},
isbn={978-989-758-442-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Gait-based Person Identification using Multiple Inertial Sensors
SN - 978-989-758-442-8
AU - Adel O.
AU - Nafea Y.
AU - Hesham A.
AU - Gomaa W.
PY - 2020
SP - 621
EP - 628
DO - 10.5220/0009791506210628