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Article

Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas

Telematics Engineering Department, Carlos III University of Madrid, 28911 Madrid, Spain
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Technologies 2020, 8(4), 58; https://doi.org/10.3390/technologies8040058
Received: 30 September 2020 / Revised: 19 October 2020 / Accepted: 22 October 2020 / Published: 26 October 2020
Nowadays, our mobile devices have become smart computing platforms, incorporating a wide number of embedded sensors such as accelerometers, gyroscopes, barometers, GPS receivers, and magnetometers. Smartphones are valuable devices for gathering user-related data and transforming it into value-added information for the user. In this study, a novel mechanism to process sensor data from mobile devices in order to detect the type of area the user is crossing while walking in an urban setting is presented. The method is based on combining outlier data analysis and classification techniques from data collected by several pedestrians while traversing an urban environment. A theoretical framework, composed of methods for detecting multivariate outliers combined with supervised classification techniques, has been proposed in order to identify different situations and physical barriers while walking. Each type of element to be detected is characterized by using a feature vector computed based on the outliers detected. Finally, a radial SVM is used for the classification task. The classifier is trained in a supervised way with data from 20 different segments containing several physical barriers and used later to assign a class to new un-labelled data. The results obtained with this approach are very promising with an average accuracy around 95% when detecting different types of physical barriers. View Full-Text
Keywords: multivariate outliers; machine learning; SVM; mobile sensor data multivariate outliers; machine learning; SVM; mobile sensor data
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MDPI and ACS Style

Ruiz Blázquez, R.; Muñoz-Organero, M. Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas. Technologies 2020, 8, 58. https://doi.org/10.3390/technologies8040058

AMA Style

Ruiz Blázquez R, Muñoz-Organero M. Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas. Technologies. 2020; 8(4):58. https://doi.org/10.3390/technologies8040058

Chicago/Turabian Style

Ruiz Blázquez, Ramona, and Mario Muñoz-Organero. 2020. "Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas" Technologies 8, no. 4: 58. https://doi.org/10.3390/technologies8040058

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