Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning
Context-aware computing has become a certainty due to the widespread use of smartphone devices equipped with sensors. A wide range of services, such as vehicular traffic monitoring and smart parking, can be accomplished with the help of awareness of user mobility. Transportation mode detection (TMD)...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9162035/ |
| _version_ | 1819173035218829312 |
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| author | Bandar Alotaibi |
| author_facet | Bandar Alotaibi |
| author_sort | Bandar Alotaibi |
| collection | DOAJ |
| description | Context-aware computing has become a certainty due to the widespread use of smartphone devices equipped with sensors. A wide range of services, such as vehicular traffic monitoring and smart parking, can be accomplished with the help of awareness of user mobility. Transportation mode detection (TMD) using machine learning algorithms and the data captured from smartphone embedded sensors have attracted research community attention. In this research, ensemble learning is utilized to differentiate between transportation modes, including walking, standing, riding a train, driving a car, and riding a bus. The ensemble learning consists of three classifiers; each classifier votes independently on the instances, and the majority vote is applied for robust generalization. The proposed method was validated using three datasets; the samples included in these datasets were gathered by smartphone sensors (belonging to heterogeneous users), such as rotation vector sensors, accelerometers, uncalibrated gyroscopes, linear acceleration, orientation, speed, game rotation vector, sound, and gyroscopes. The proposed ensemble learning method achieves an accuracy of 89%, 93%, and 95% on the first, second, and third datasets, respectively, when 10% and 90% of the data are used for testing and training, respectively. On the other set of experiments, in which 30% and 70% of the data are used for testing and training, respectively, the proposed method yields accuracies of 86.8%, 92.1%, and 94.9% on the first, second, and third datasets, respectively. The proposed method shows promising results compared to existing human activity recognition (HAR) methods. |
| first_indexed | 2024-12-22T20:16:40Z |
| format | Article |
| id | doaj.art-d403a1ee41974a39b3c73d97020ba179 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:16:40Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d403a1ee41974a39b3c73d97020ba1792022-12-21T18:13:57ZengIEEEIEEE Access2169-35362020-01-01814555214556310.1109/ACCESS.2020.30149019162035Transportation Mode Detection by Embedded Sensors Based on Ensemble LearningBandar Alotaibi0https://orcid.org/0000-0001-9956-2027Department of Information Technology, University of Tabuk, Tabuk, Saudi ArabiaContext-aware computing has become a certainty due to the widespread use of smartphone devices equipped with sensors. A wide range of services, such as vehicular traffic monitoring and smart parking, can be accomplished with the help of awareness of user mobility. Transportation mode detection (TMD) using machine learning algorithms and the data captured from smartphone embedded sensors have attracted research community attention. In this research, ensemble learning is utilized to differentiate between transportation modes, including walking, standing, riding a train, driving a car, and riding a bus. The ensemble learning consists of three classifiers; each classifier votes independently on the instances, and the majority vote is applied for robust generalization. The proposed method was validated using three datasets; the samples included in these datasets were gathered by smartphone sensors (belonging to heterogeneous users), such as rotation vector sensors, accelerometers, uncalibrated gyroscopes, linear acceleration, orientation, speed, game rotation vector, sound, and gyroscopes. The proposed ensemble learning method achieves an accuracy of 89%, 93%, and 95% on the first, second, and third datasets, respectively, when 10% and 90% of the data are used for testing and training, respectively. On the other set of experiments, in which 30% and 70% of the data are used for testing and training, respectively, the proposed method yields accuracies of 86.8%, 92.1%, and 94.9% on the first, second, and third datasets, respectively. The proposed method shows promising results compared to existing human activity recognition (HAR) methods.https://ieeexplore.ieee.org/document/9162035/Context-aware computingembedded sensorsensemble learninghuman activity recognitionInternet of Things (IoT)transportation mode detection |
| spellingShingle | Bandar Alotaibi Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning IEEE Access Context-aware computing embedded sensors ensemble learning human activity recognition Internet of Things (IoT) transportation mode detection |
| title | Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning |
| title_full | Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning |
| title_fullStr | Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning |
| title_full_unstemmed | Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning |
| title_short | Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning |
| title_sort | transportation mode detection by embedded sensors based on ensemble learning |
| topic | Context-aware computing embedded sensors ensemble learning human activity recognition Internet of Things (IoT) transportation mode detection |
| url | https://ieeexplore.ieee.org/document/9162035/ |
| work_keys_str_mv | AT bandaralotaibi transportationmodedetectionbyembeddedsensorsbasedonensemblelearning |