What can an arm holster worn smart phone do for activity recognition?

Proceedings - International Symposium on Wearable Computers, ISWC

Volumn , Issue , 2011, Pages 79-82

  Muehlbauer, Michael ^a   Bahle, Gernot ^a   Lukowicz, Paul ^a  

^a UNIVERSITY OF PASSAU   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY RECOGNITION; ARM MOTIONS; CONTINUOUS DATA; DATA SETS; PHONE PLATFORMS; PHYSICAL EXERCISE; SMART PHONES; UPPER ARM; USER-INDEPENDENT RECOGNITION;

MOBILE DEVICES; WEARABLE COMPUTERS;

TELEPHONE SETS;

EID: 80052013045     PISSN: 15504816     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISWC.2011.23     Document Type: Conference Paper

References (13)

1. E. Berlin, J. Liu, K. van Laerhoven, and B. Schiele. Coming to grips with the objects we grasp: detecting interactions with efficient wrist-worn sensors. In Proc. of the 4th int. conf. on tangible, embedded, and embodied interaction, TEI '10, pages 57-64, NY, USA, 2010. ACM.
2. K.-h. Chang, M. Chen, and J. Canny. Tracking free-weight exercises. In J. Krumm, G. Abowd, A. Seneviratne, and T. Strang, editors, UbiComp 2007: Ubiquitous Computing, volume 4717 of Lecture Notes in Computer Science, pages 19-37. Springer Berlin / Heidelberg, 2007.
3. R. K. Ganti, S. Srinivasan, and A. Gacic. Multisensor fusion in smartphones for lifestyle monitoring. In BSN '10: Proc. of the 2010 Int. Conf. on Body Sensor Networks, pages 36-43, Washington, DC, USA, 2010. IEEE.
4. T. Huynh, U. Blanke, and B. Schiele. Scalable recognition of daily activities with wearable sensors. In J. Hightower, B. Schiele, and T. Strang, editors, Location- and Context-Awareness, volume 4718 of Lecture Notes in CS, pages 50-67. Springer Berlin / Heidelberg, 2007.
5. H. Junker, O. Amft, P. Lukowicz, and G. Tröster. Gesture spotting with body-worn inertial sensors to detect user act. Pattern Recogn., 41:2010-2024, 2008.
6. N. Kern, B. Schiele, and A. Schmidt. Multi-sensor activity context detection for wearable computing. In Ambient Intelligence, volume 2875 of Lecture Notes in CS, pages 220-232. Springer Berlin / Heidelberg, 2003. (Pubitemid 137631976)
7. A. Khan, Y.-K. Lee, S. Lee, and T.-S. Kim. Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis. In Future Inf. Tech. (FutureTech), 2010 5th Int. Conf. on, pages 1-6, May 2010.
8. K. Kunze and P. Lukowicz. Dealing with sensor displacement in motion-based onbody activity recognition systems. In Proc. of the 10th int. conf. on Ubiquitous computing, UbiComp '08, pages 20-29, NY, USA, 2008. ACM.
9. J. R. Kwapisz, G. M. Weiss, and S. A. Moore. Activity recognition using cell phone accelerometers. Human Factors, page 1018, 2010.
10. J. Lester, T. Choudhury, and G. Borriello. A practical approach to recognizing physical activities. In Proc. of Pervasive, pages 1-16, 2006. (Pubitemid 44044748)
11. C. Mattmann, O. Amft, H. Harms, G. Troster, and F. Clemens. Recognizing upper body postures using textile strain sensors. In Wearable Computers, 2007 11th IEEE Int. Symp. on, pages 29-36, Oct. 2007. (Pubitemid 351424262)
12. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher. Activity recognition and monitoring using multiple sensors on different body positions. In BSN '06: Pro. of the Int. Workshop on Wearable and Implantable Body Sensor Networks, pages 113-116, Washington, DC, USA, 2006. IEEE. (Pubitemid 44711293)
13. T. S. Saponas, J. Lester, J. Froehlich, J. Fogarty, and J. Landay. ilearn on the iphone: Real-time human activity classification on commodity mobile phones. Cse technical report, University of Washington, 2008.