Correction published on 18 August 2020, see
Sensors 2020, 20(16), 4650.
A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors
Department of Computer Science and Information Technologies, University of A Coruna, 15071 A Coruna, Spain
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Author to whom correspondence should be addressed.
Sensors 2020, 20(8), 2200; https://doi.org/10.3390/s20082200
Received: 13 March 2020
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Revised: 31 March 2020
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Accepted: 7 April 2020
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Published: 13 April 2020
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes it particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need for other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes asupport vector machine (SVM) model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use, unlike other datasets, the development of a good model on such data is an open problem and a challenge for researchers. By doing so, we would be able to close the gap between the model and a real-life application.
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Keywords:
HAR; human activity recognition; sensors; smartphones; dataset; SVM
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Externally hosted supplementary file 1
Link: http://lbd.udc.es/research/real-life-HAR-dataset/
Description: The complete dataset, as well as the scripts used on our experiments.
MDPI and ACS Style
Garcia-Gonzalez, D.; Rivero, D.; Fernandez-Blanco, E.; Luaces, M.R. A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors. Sensors 2020, 20, 2200. https://doi.org/10.3390/s20082200
AMA Style
Garcia-Gonzalez D, Rivero D, Fernandez-Blanco E, Luaces MR. A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors. Sensors. 2020; 20(8):2200. https://doi.org/10.3390/s20082200
Chicago/Turabian StyleGarcia-Gonzalez, Daniel, Daniel Rivero, Enrique Fernandez-Blanco, and Miguel R. Luaces. 2020. "A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors" Sensors 20, no. 8: 2200. https://doi.org/10.3390/s20082200
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