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.
Highlights
Giving birth to the knowledge area called human activity recognition (HAR), the accurate identification of different human activities has become a hot research topic
With the implementation of a simple support vector machine (SVM) model, we present a first model as proof of concept to detect the main activities in the more realistic dataset
In [19], the results show that these methods might be the future for HAR, as their results are very hopeful, at least in the non-stationary activities such as walking or running, as SVM still reigns in short-timed activities such as standing or laying down
Summary
Giving birth to the knowledge area called human activity recognition (HAR), the accurate identification of different human activities has become a hot research topic. This area tries to identify the action performed by a subject based on the data records from a set of sensors. The recording of these sensors is carried out while the subject performs a series of well-defined movements, such as nodding, raising the hand, walking, running or driving In this sense, wearable devices, such as activity bracelets or smartphones, have become of great use as sources of this sort of data. The result from the intersection between the widespread sensing all over the world, due to the smartphones and the models developed from that continuous recording, is a research area that has attracted increasing attention in recent years [2]
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