ABSTRACT
We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.
- L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. In Proceedings of the 2nd International Conference on Pervasive Computing (PERVASIVE), volume 3001 of Lecture Notes in Computer Science, pages 1--17. Springer-Verlag, 2004.Google ScholarCross Ref
- G. Bieber, J. Voskamp, and B. Urban. Activity recognition for everyday life on mobile phones. In Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction (UAHCI), pages 289--296, 2009. Google ScholarDigital Library
- A. Bolbol, T. Cheng, I. Tsapakis, and J. Haworth. Inferring hybrid transportation modes from sparse gps data using a moving window svm classification. Computers, Environment and Urban Systems, 31;6:526--537, 2012.Google Scholar
- T. Brezmes, J.-L. Gorricho, and J. Cotrina. Activity recognition from accelerometer data on a mobile phone. In Workshop Proceedings of the 10th International Work-Conference on Artificial Neural Networks (IWANN), pages 796--799, 2009. Google ScholarDigital Library
- S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, I. Smith, and J. A. Landay. Activity sensing in the wild: a field trial of ubifit garden. In CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 1797--1806, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- D. Figo, P. Diniz, D. Ferreira, and J. Cardoso. Preprocessing techniques for context recognition from accelerometer data. Personal and Ubiquitous Computing, 14-7:645--662, 2010. Google ScholarDigital Library
- Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the Second European Conference on Computational Learning Theory, 1995. Google ScholarDigital Library
- Y. Freund and R. E. Schapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771--780, 1999.Google Scholar
- J. Froehlich, T. Dillahunt, P. Klasnja, J. Mankoff, S. Consolvo, B. Harrison, and J. A. Landay. Ubigreen: investigating a mobile tool for tracking and supporting green transportation habits. In Proceedings of the 27th international conference on Human factors in computing systems (CHI), pages 1043--1052. ACM, 2009. Google ScholarDigital Library
- M. C. González, C. A. Hidalgo, and A.-L. Barabási. Understanding individual human mobility patterns. Nature, 453:779--782, 2008.Google ScholarCross Ref
- F. Ichikawa, J. Chipchase, and R. Grignani. Where's the phone? a study of mobile phone location in public spaces. In Proceedings of the 2nd International Conference on Mobile Technology, Applications and Systems, pages 1--8. IEEE, 2005.Google Scholar
- T. Iso and K. Yamazaki. Gait analyzer based on a cell phone with a single three-axis accelerometer. Proceedings of the 8th conference on Human-computer interaction with mobile devices and services, pages 141--144, 2006. Google ScholarDigital Library
- A. Jylhä, P. Nurmi, M. Siren, S. Hemminki, and G. Jacucci. Matkahupi: a persuasive mobile application for sustainable mobility. In ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2013. Google ScholarDigital Library
- D. H. Kim, Y. Kim, D. Estrin, and M. B. Srivastava. Sensloc: sensing everyday places and paths using less energy. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys), pages 43--56. ACM, 2010. Google ScholarDigital Library
- M. B. Kjærgaard, S. Bhattacharya, H. Blunck, and P. Nurmi. Energy-efficient trajectory tracking for mobile devices. In Proceedings of the 9th International Conference on Mobile Systems, Applications and Services (MobiSys), 2011. Google ScholarDigital Library
- J. Krumm and E. Horvitz. LOCADIO: Inferring motion and location from Wi-Fi signal strengths. In Proceedings of the 1st International Conference on Mobile and Ubiquitous Systems (Mobiquitous), pages 4--14. IEEE, 2004.Google ScholarCross Ref
- N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell. A survey of mobile phone sensing. IEEE Communications Magazine, 48(9):140--150, 2010. Google ScholarDigital Library
- D. Lazer, A. P. L. Adamic, S. Aral, A.-L. Barabási, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. R. 2, and M. V. Alstyne. Computational social science. Science, 323(5915):721--723, 2009.Google ScholarCross Ref
- H. Lu, J. Yang, Z. Liu, N. D. Lane, C. T., and C. A. The jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pages 71--84, 2010. Google ScholarDigital Library
- E. Miluzzo, N. D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S. B. Eisenman, X. Zheng, and A. T. Campbell. Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In Proceedings of the 6th ACM conference on Embedded network sensor systems (SenSys), pages 337--350, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- T. M. Mitchell. Mining our reality. Science, 326(1644):1644--1645, 2009.Google ScholarCross Ref
- D. Mizell. Using gravity to estimate accelerometer orientation. In Proc. Seventh IEEE International Symposium on Wearable Computers, pages 252--253, 18--21 Oct. 2005. Google ScholarDigital Library
- M. Mun, D. Estrin, J. Burke, and M. Hansen. Parsimonious mobility classification using gsm and wifi traces. In Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (SenSys), pages 1--5, 2008.Google Scholar
- K. Muthukrishnan, M. Lijding, N. Meratnia, and P. Havinga. Sensing Motion Using Spectral and Spatial Analysis of WLAN RSSI. In Proceedingds of the 2nd European Conference on Smart Sensing and Context (EuroSSC), pages 62--76,. Springer, 2007. Google ScholarDigital Library
- P. Nurmi, S. Bhattacharya, and J. Kukkonen. A grid-based algorithm for on-device GSM positioning. In Proceedings of the 12th International Conference on Ubiquitous Computing (UbiComp), pages 227--236, 2010. Google ScholarDigital Library
- S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava. Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks, 6(2):13:1--13:27, 2010. Google ScholarDigital Library
- T. Sohn, A. Varshavsky, A. LaMarca, M. Y. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. G. Griswold, and E. de Lara. Mobility detection using everyday GSM traces. In Proceedings of the 8th International Conference on Ubiquitous Computing (Ubicomp), pages 212--224, 2006. Google ScholarDigital Library
- C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 19(5968):1018--1021, 2010.Google ScholarCross Ref
- D. Soper. Is human mobility tracking a good idea? Communications of the ACM, 55; 4:35--37, 2012. Google ScholarDigital Library
- L. Stenneth, O. Wolfson, P. S. Yu, and B. Xu. Transportation mode detection using mobile phones and gis information. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 54--63, 2011. Google ScholarDigital Library
- S. Wang, C. Chen, and J. Ma. Accelerometer based transportation mode recognition on mobile phones. In Asia-Pacific Conference on Wearable Computing Systems, pages 44--46, 2010. Google ScholarDigital Library
- J. Ward, P. Lukowicz, and G. Tröster. Evaluating performance in continuous context recognition using event-driven error characterization. In Proceedings of the 2nd International Workshop on Location- and Context-Awareness (LoCA), pages 239--255. Springer, 2006. Google ScholarDigital Library
- J. A. Ward, P. Lukowicz, and H. W. Gellersen. Performance metrics for activity recognition. ACM Transactions on Intelligent Systems and Technology (TIST), 2(1):6:1--6:23, 2011. Google ScholarDigital Library
- Z. Yan, V. Subbaraju, D. Chakrabarti, A. Misra, and A. K. Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. International Symposium on Wearable Computers (ISWC), 2012. Google ScholarDigital Library
- J. Yang. Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics (IMCE), pages 1--9, 2009. Google ScholarDigital Library
- Y. Zheng, Y. Chen, Q. Li, and W.-Y. Xie, X. Ma. Understanding transportation modes based on gps data for web applications. ACM Transactions on the Web, 4,1, 2010. Google ScholarDigital Library
- Y. Zheng, Q. Li, Y. Chen, X. Xie, and W.-Y. Ma. Understanding mobility based on gps data. In Proceedings of the 10th international conference on Ubiquitous computing, pages 312--321, 2008. Google ScholarDigital Library
- Y. Zheng, Y. Liu, J. Yuan, and X. Xie. Urban computing with taxicabs. In Proceedings of the 13th International Conference on Ubiquitous Computing (Ubicomp), pages 89--98. ACM, 2011. Google ScholarDigital Library
Index Terms
- Accelerometer-based transportation mode detection on smartphones
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