arrow_back TMD-CAPTIMOVE
Dataset usage instructions
Datasheet - Transportation Mode Detection using Inertial and Pressure Sensors
TMD-CAPTIMOVE
================================================================================================================================================================
Authors:
F. TAIA-ALAOUI, Hassen FOURATI, Nicolas VUILLERME, Alain KIBANGOU, Bogdan ROBU, Christophe VILLEMAZET
================================================================================================================================================================
Contact:
fadoua.taia-alaoui@gipsa-lab.fr ; hassen.fourati@gipsa-lab.fr
================================================================================================================================================================
Acknowledgements:
This work is part of the project CAPTIMOVE funded by the IDEX of University Grenoble Alpes (call for projects "Initiatives de Recherche Stratégiques (IRS)")
================================================================================================================================================================
Data description:
This database called "TMD-CAPTIMOVE" provides transportation mode labelled data collected by 34 volunteers for a total time duration of around 48 hours.
Considered transportation modes are: On-foot (Walking, Stairs, Elevators), Bike, Scooter, Bus, Tram.
The number of labels is 11: Still, Walk, Upstairs, Downstairs, Elevator up, Elevator down, Bike, Electric scooter, kick scooter, Bus, Tram.
Sensor data are: Acceleration (m/s²), angular rate (°/s), atmospheric pressure (hPa), heart rate (beat per minute (BPM)).
The sampling frequency for all data is 32 Hertz.
Files are stuctured as follows:
-
The main folder called "Labelled_data" contains 34 sub-folders, each one corresponding to one user.
-
Each sub-folder contains several files, each one corresponding to a continuous signal frame.
-
Inside each file, each second (1s) of data is separated from the following one by a row of zeros (0).
================================================================================================================================================================
Reading files:
-
3 first columns: 3 Dimensional acceleration data in body frame (Acc_X, Acc_Y, Acc_Z)
-
3 following columns: 3 Dimensional angular rate data in body frame (Gyr_X, Gyr_Y, Gyr_Z)
-
7th column: 1 dimensional pressure data
-
8th columns: Acceleration magnitude
-
9th columns: Angular rate magnitude
-
10th column: Smoothed pressure
-
11th column: Heart rate
-
12th column: Sensor placement
-
13th column: Label
================================================================================================================================================================
Label and sensor placement data coding:
Because machine learning processes are more convenient with arrays, sensor placement and label data were coded into discrete real numbers as follows:
Class labels: 0: Still, 1: Walk, 2: Bike, 3: Elevator up, 4: Elevator down, 5: Upstairs , 6: Downstairs, 7: Bus, 8: Tram, 11: Kick scooter, 12: Electric scooter
Sensor placement data: 1: Hand, 2: Wrist, 3: Pocket, 4: Foot, 5: Waist
================================================================================================================================================================
Publications:
If you use this dataset for publication purposes, please cite us as follows:
F. TAIA ALAOUI,Hassen FOURATI,Nicolas VUILLERME,Alain KIBANGOU,Ioan Bogdan ROBU,Christophe VILLEMAZET, TMD-CAPTIMOVE, 2020.
================================================================================================================================================================