Geo-Magnetic field and WLAN dataset for indoor localisation from wristband and smartphone
Donated on 1/9/2017
A multisource and multivariate dataset for indoor localisation methods based on WLAN and Geo-Magnetic ï¬eld ï¬ngerprinting
Dataset Characteristics
Multivariate, Sequential, Time-Series
Subject Area
Computer Science
Associated Tasks
Classification, Regression, Clustering
Feature Type
Integer, Real
# Instances
153540
# Features
25
Dataset Information
Additional Information
Indoor localisation is a key topic for the Ambient Intelligence (AmI) research community. In this scenarios, recent advancements in wearable technologies, particularly smartwatches with built-in sensors, and personal devices, such as smartphones, are being seen as the breakthrough for making concrete the envisioned Smart Environment (SE) paradigm. In particular, scenarios devoted to indoor localization represent a key challenge to be addressed. Many works try to solve the indoor localization issue, but the lack of a common dataset or frameworks to compare and evaluate solutions represent a big barrier to be overcome in the ï¬eld. The unavailability and uncertainty of public datasets hinders the possibility to compare different indoor localization algorithms. This constitutes the main motivation of the proposed dataset described herein. We collected Wi-Fi and geo-magnetic ï¬eld ï¬ngerprints, together with inertial sensor data during two campaigns performed in the same environment. Retrieving syncronized data from a smartwatch and a smartphone worn by users at the purpose of create and present a public available dataset is the goal of this work.
Has Missing Values?
No
Variable Information
Pointsmapping.ods: A three column spreadsheet (ID,X,Y) which points mapping in local coordinates. Each ID represents an unique place on the map. The X-Y coordinates represents the local coordinates. For each measure: measure1(2)_timestamp_id.csv: Timestamp (Unixtime) of arrival on placeID, timestamp (Unixtime) of departure by placeID, Place ID identifier (0-324) measure1(2)_smartphone_sens.csv: According to measure1(2)_timestamp_id.csv, this csv contains the data sensors retrieved by the smartphone. Timestamp, AccelerationX, AccelerationY, AccelerationZ, MagneticFieldX, MagneticFieldY, MagneticFieldZ, Z-AxisAgle(Azimuth), X-AxisAngle(Pitch), Y-AxisAngle(Roll), GyroX, GyroY, GyroZ measure1(2)_smartwatch_sens.csv: According to measure1(2)_timestamp_id.csv, this csv contains the data sensors retrieved by the smartwatch. Timestamp, AccelerationX, AccelerationY, AccelerationZ, MagneticFieldX, MagneticFieldY, MagneticFieldZ, Z-AxisAgle(Azimuth), X-AxisAngle(Pitch), Y-AxisAngle(Roll), GyroX, GyroY, GyroZ measure1(2)_smartphone_wifi.csv: Each rows contains PlaceId (ascending order) and 127 column, with RSSI level for each different WAPs retrieved during the campaign. Not all the WAPs are detected in each scan. For these WAPs, the articial RSSI value is -100 (dbm).
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset geo_magnetic_field_and_wlan_dataset_for_indoor_localisation_from_wristband_and_smartphone = fetch_ucirepo(id=377) # data (as pandas dataframes) X = geo_magnetic_field_and_wlan_dataset_for_indoor_localisation_from_wristband_and_smartphone.data.features y = geo_magnetic_field_and_wlan_dataset_for_indoor_localisation_from_wristband_and_smartphone.data.targets # metadata print(geo_magnetic_field_and_wlan_dataset_for_indoor_localisation_from_wristband_and_smartphone.metadata) # variable information print(geo_magnetic_field_and_wlan_dataset_for_indoor_localisation_from_wristband_and_smartphone.variables)
Barsocchi,Paolo, Crivello,Antonino, Rosa,Davide, and Palumbo,Filippo. (2017). Geo-Magnetic field and WLAN dataset for indoor localisation from wristband and smartphone. UCI Machine Learning Repository. https://doi.org/10.24432/C5DW43.
@misc{misc_geo-magnetic_field_and_wlan_dataset_for_indoor_localisation_from_wristband_and_smartphone_377,
author = {Barsocchi,Paolo, Crivello,Antonino, Rosa,Davide, and Palumbo,Filippo},
title = {{Geo-Magnetic field and WLAN dataset for indoor localisation from wristband and smartphone}},
year = {2017},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5DW43}
}Creators
Paolo Barsocchi
Antonino Crivello
Davide Rosa
Filippo Palumbo
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.