Abstract
Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.
Highlights
Our society is more and more surrounded by devices—smartphones, tablets, wearables, “things”from the Internet of Things (IoT), etc.—which are rapidly transforming us, changing the way we live and interact with each other
Μ f E : the mean frequency of the vertical component of the projected acceleration; σ f E : the standard deviation of the previous mean frequency; Md f E : the median frequency of the vertical projected acceleration; Mo f E : the modal frequency of the vertical projected acceleration; Mo f : the modal frequency of the acceleration norm; κ f E : the kurtosis of the spectrum of the vertical projected acceleration. We evaluated this feature set with several classifiers: Random Forests, Support Vector Machines (SVM), Gradient Boosting Machines (GBM), k-Nearest Neighbors, Naïve Bayes and C5.0
We analyzed the performance of all the different proposals for walking recognition described in this paper: feature-based and shape-based classifiers, including the use of support vectors, partitioning around medoids (PAM) medoids or supervised summarization to get the mapping function described in the shape-based approach
Summary
Our society is more and more surrounded by devices—smartphones, tablets, wearables, “things”from the Internet of Things (IoT), etc.—which are rapidly transforming us, changing the way we live and interact with each other. In the particular case of pedestrian indoor localization, recognizing the activity of walking using inertial sensors is essential, since other alternatives such as the Global Navigation Satellite System (GNSS) do not work indoors. Other sensor modalities, such as infrared, ultrasound, magnetic field, WiFi or BlueTooth [12,13,14], have been used to detect the displacement of a person indoors, the combination of the information provided by these sensors together with the recognition of walking using the accelerometer, magnetometer and gyroscope (IMU) has been proved to be the best option to significantly increase the performance of indoor localization
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