Fusing on-body sensing with local and temporal cues for daily activity recognition

BODYNETS 2014 - 9th International Conference on Body Area Networks

Volumn , Issue , 2014, Pages 83-89

  Zhu, Zack ^a   Blanke, Ulf ^a   Calatroni, Alberto ^a   Brdiczka, Oliver ^b   Tröster, Gerhard ^a  

^a ETH ZURICH   (Switzerland)

^b PALO ALTO RESEARCH CENTER   (United States)

Author keywords

Activity routine recognition; Crowd sensing platform; Wearable sensors; Web repository exploitation

Indexed keywords

MOTION SENSORS; PATTERN RECOGNITION;

AMERICAN TIME USE SURVEYS; ANDROID SMARTPHONE; COMMERCIAL DEVICES; CONTROLLED ENVIRONMENT; PERSONALIZED MODEL; RECOGNITION ACCURACY; SENSING PLATFORMS; WEB REPOSITORIES;

WEARABLE SENSORS;

EID: 84936013796     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.4108/icst.bodynets.2014.257014     Document Type: Conference Paper

References (21)

1. N. Aharony, W. Pan, C. Ip, I. Khayal, and A. Pentland. Social fmri: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing, 7(6):643-659, 2011.
2. M. Berchtold, M. Budde, D. Gordon, H. R. Schmidtke, and M. Beigl. Actiserv: Activity recognition service for mobile phones. In Wearable Computers (ISWC), 2010 International Symposium on, pages 1-8. IEEE, 2010.
3. U. Blanke, T. Franke, G. Tröster, and P. Lukowicz. Capturing crowd dynamics at large scale events using participatory gps-localization. In The 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2014.
4. U. Blanke and B. Schiele. Daily routine recognition through activity spotting. In International Symposium on Location and Context Awareness (LoCA), May 2009.
5. U. Blanke, B. Schiele, M. Kreil, P. Lukowicz, B. Sick, and T. Gruber. All for one or one for all?-combining heterogeneous features for activity spotting. In 7th IEEE PerCom Workshop on Context Modeling and Reasoning (CoMoRea), Mannheim, Germany, 2010.
6. M. Borazio and K. Van Laerhoven. Improving activity recognition without sensor data: a comparison study of time use surveys. In Proceedings of the 4th Augmented Human International Conference, 2013.
7. M. Borazio and K. Van Laerhoven. Using time use with mobile sensor data: a road to practical mobile activity recognition? In Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, page 20. ACM, 2013.
8. L. Breiman. Random forests. Mach. Learn., 45(1):5-32, Oct. 2001.
9. F. Ichikawa, J. Chipchase, and R. Grignani. Where's the phone? A study of Mobile Phone Location in Public Spaces. In 2nd International Conference on Mobile Technology, Applications and Systems, pages 1-8, 2005.
10. N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell. A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9):140-150, 2010.
11. L. Liao, D. Fox, and H. Kautz. Location-based activity recognition. In Neural Information Processing Systems (NIPS), 2005.
12. L. Liao, D. J. Patterson, D. Fox, and H. Kautz. Learning and inferring transportation routines. Artificial Intelligence, 171(5):311-331, 2007.
13. E. Miluzzo, N. D. Lane, S. B. Eisenman, and A. T. Campbell. Cenceme: Injecting sensing presence into social networking applications. In Proceedings of the 2Nd European Conference on Smart Sensing and Context, EuroSSC'07, pages 1-28, Berlin, Heidelberg, 2007. Springer-Verlag.
14. K. Partridge and P. Golle. On using existing time-use study data for ubiquitous computing applications. In Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp '08, pages 144-153, New York, NY, USA, 2008. ACM.
15. S. N. Patel, J. A. Kientz, G. R. Hayes, S. Bhat, and G. D. Abowd. Farther than you may think: An empirical investigation of the proximity of users to their mobile phones. In UbiComp 2006: Ubiquitous Computing, pages 123-140. Springer, 2006.
16. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
17. T. Phan, S. Kalasapur, and A. Kunjithapatham. Sensor fusion of physical and social data using web socialsense on smartphone mobile browsers. In Proceedings of the 11th Annual IEEE Consumer Communications and Networking Conferece, 2014.
18. K. J. Shelley. Developing the american time use survey activity classification system. Monthly Lab. Rev., 128:3, 2005.
19. W. Sherchan, P. P. Jayaraman, S. Krishnaswamy, A. Zaslavsky, S. Loke, and A. Sinha. Using on-the-move mining for mobile crowdsensing. In Mobile Data Management (MDM), 2012 IEEE 13th International Conference on, pages 115-124. IEEE, 2012.
20. K. Van Laerhoven, D. Kilian, and B. Schiele. Using rhythm awareness in long-term activity recognition. In Wearable Computers, 2008. ISWC 2008. 12th IEEE International Symposium on, pages 63-66. IEEE, 2008.
21. A. Zinnen, U. Blanke, and B. Schiele. An analysis of sensor-oriented vs. model-based activity recognition. In Proceedings of the 13th IEEE International Symposium on Wearable Computers (ISWC), 2009.