Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/8/4/58 |
| _version_ | 1797549831229538304 |
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| author | Ramona Ruiz Blázquez Mario Muñoz-Organero |
| author_facet | Ramona Ruiz Blázquez Mario Muñoz-Organero |
| author_sort | Ramona Ruiz Blázquez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:20:58Z |
| format | Article |
| id | doaj.art-affed505006f42e4a4c588f47f819835 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-10T15:20:58Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-affed505006f42e4a4c588f47f8198352023-11-20T18:31:51ZengMDPI AGTechnologies2227-70802020-10-01845810.3390/technologies8040058Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban AreasRamona Ruiz Blázquez0Mario Muñoz-Organero1Telematics Engineering Department, Carlos III University of Madrid, 28911 Madrid, SpainTelematics Engineering Department, Carlos III University of Madrid, 28911 Madrid, SpainNowadays, 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.https://www.mdpi.com/2227-7080/8/4/58multivariate outliersmachine learningSVMmobile sensor data |
| spellingShingle | Ramona Ruiz Blázquez Mario Muñoz-Organero Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas Technologies multivariate outliers machine learning SVM mobile sensor data |
| title | Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas |
| title_full | Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas |
| title_fullStr | Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas |
| title_full_unstemmed | Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas |
| title_short | Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas |
| title_sort | using multivariate outliers from smartphone sensor data to detect physical barriers while walking in urban areas |
| topic | multivariate outliers machine learning SVM mobile sensor data |
| url | https://www.mdpi.com/2227-7080/8/4/58 |
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