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 - 10:01
DOI:
10.21227/et00-br41
Data Format:
*.tsv(zip)
License:
Creative Commons Attribution
617 Views
Categories:
Artificial Intelligence
Signal Processing
IoT
Sensors
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.

Instructions: 

All necessary information can be found in the provided README file.

Comments

HI

Submitted by Shubham Gupta on Sun, 02/05/2023 - 11:30

Thanks.

Submitted by Tigang Jiang on Sat, 03/11/2023 - 02:59

thanks

Submitted by omer cebeci on Sun, 03/12/2023 - 00:44

Hi

Submitted by Carolina Corella on Sat, 03/18/2023 - 00:05

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