A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors
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A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone SensorsFecha
2020-04-13Cita bibliográfica
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
Resumen
[Abstract]
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.
Palabras clave
HAR
Human activity recognition
Sensors
Smartphones
Dataset
SVM
Human activity recognition
Sensors
Smartphones
Dataset
SVM
Versión del editor
Derechos
Atribución 4.0 Internacional
ISSN
1424-8220