Bluetooth Sensor Data for Gait-Based Person Identification

Citation Author(s):: Gerrit Maus (University of Wuppertal)

Mustafa Biyikli (University of Wuppertal)

Salih Can Erşahin (University of Wuppertal)

Stefan Janicke (University of Wuppertal)

Arda Erten (University of Wuppertal)
Submitted by:: Gerrit Maus
Last updated:: Thu, 03/30/2023 - 14:01
DOI:: 10.21227/et00-br41
Data Format:: *.tsv(zip)

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Categories:

Keywords:

Bluetooth

Gait Recognition

person identification

convolutional neural network

Deep Learning

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Abstract

The human gait is unique and so is the impact of a walking human on the propagation of wireless signals within a wireless network. Using appropriate pattern recognition techniques, a person can thus be identified just from a time series of Received Signal Strength (RSS) measurements. This dataset holds bidirectional RSS measurements recorded within a mesh network of four Bluetooth sensor devices. During the measurements, a total of 14 subjects walked individually through the setup. A total of more than 10,000 recordings are provided.