Accelerometer-based transportation mode detection on smartphones

SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

Volumn , Issue , 2013, Pages

  Hemminki, Samuli ^a   Nurmi, Petteri ^a   Tarkoma, Sasu ^a  

^a UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Activity Recognition; Mobile Sensing; Transportation Mode Detection

Indexed keywords

ALGORITHMS; EMBEDDED SYSTEMS; SMARTPHONES;

ACCELEROMETER MEASUREMENT; ACTIVITY RECOGNITION; HIERARCHICAL DECOMPOSITIONS; KEY CHARACTERISTICS; MOBILE SENSING; MOVEMENT PATTERN; PRIMARY CONTRIBUTION; TRANSPORTATION MODE;

ACCELEROMETERS;

EID: 84905707140     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2517351.2517367     Document Type: Conference Paper

References (38)

1. 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.
2. 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.
3. A. Bolbol, T. Cheng, I. Tsapakis, and J. Haworth. Inferring hybrid transportation modes from sparse gps data using a moving window svm classiffication. Computers, Environment and Urban Systems, 31;6:526-537, 2012.
4. 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.
5. 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 ubiffit 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.
6. 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.
7. 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.
8. Y. Freund and R. E. Schapire. A short introduction to boosting. Journal of Japanese Society for Artifficial Intelligence, 14(5):771-780, 1999.
9. J. Froehlich, T. Dillahunt, P. Klasnja, J. Manko, 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.
10. M. C. González, C. A. Hidalgo, and A.-L. Barabási. Understanding individual human mobility patterns. Nature, 453:779-782, 2008.
11. 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.
12. 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.
13. A. Jylha, 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. T. M. Mitchell. Mining our reality. Science, 326(1644):1644-1645, 2009.
22. D. Mizell. Using gravity to estimate accelerometer orientation. In Proc. Seventh IEEE International Symposium on Wearable Computers, pages 252-253, 18-21 Oct. 2005.
23. M. Mun, D. Estrin, J. Burke, and M. Hansen. Parsimonious mobility classiffication using gsm and wifi traces. In Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (SenSys), pages 1-5, 2008.
24. 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.
25. 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.
26. 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.
27. 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.
28. C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 19(5968):1018-1021, 2010.
29. D. Soper. Is human mobility tracking a good idea? Communications of the ACM, 55; 4:35-37, 2012.
30. 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.
31. 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.
32. 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.
33. 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.
34. 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.
35. 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.
36. 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.
37. 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.
38. 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.