Self-taught learning for activity spotting in on-body motion sensor data

Proceedings - International Symposium on Wearable Computers, ISWC

Volumn , Issue , 2011, Pages 83-86

  Amft, Oliver ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CONTEXT RECOGNITION; HUMAN LEARNING; LARGE AMOUNTS OF DATA; LEARNING APPROACH; MOTION SENSORS; ON-BODY; PERFORMANCE COMPARISON; RELEVANT PATTERNS; UNLABELED DATA;

WEARABLE COMPUTERS;

EID: 80052006855     PISSN: 15504816     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISWC.2011.37     Document Type: Conference Paper

References (14)

1. O. Amft. Adaptive activity spotting based on event rates. In SUTC 2010: Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pages 169-176. IEEE, 2010.
2. O. Amft, D. Bannach, G. Pirkl, M. Kreil, and P. Lukowicz. Towards wearable sensing based assessment of fluid intake. In PerHealth 2010: Proceedings of the First IEEE PerCom Workshop on Pervasive Healthcare, pages 298-303. IEEE, 2010.
3. O. Amft and G. Tröster. Recognition of dietary activity events using on-body sensors. Artif Intell Med, 42(2):121-136, 2008.
4. D. Bannach, O. Amft, K. S. Kunze, E. A. Heinz, G. Tröster, and P. Lukowicz. Waving real hand gestures recorded by wearable motion sensors to a virtual car and driver in a mixed-reality parking game. In CIG 2007: Proceedings of the 2nd IEEE Symposium on Computational Intelligence and Games, pages 32-39. IEEE Press, 2007. (Pubitemid 47431281)
5. U. Blanke and B. Schiele. Daily routine recognition through activity spotting. In LoCA 2009: Proceedings of the 4th International Symposium on Location and Context Awareness, volume 5561 of Lecture Notes in Computer Science, pages 192-206. Springer, 2009.
6. N. V. Chawla and G. Karakoulas. Learning from labeled and unlabeled data: an empirical study across techniques and domains. J Artif Int Res, 23(1):331-366, 2005. (Pubitemid 41525872)
7. R. A. Finana, A. T. Sapeluk, and R. I. Damper. Impostor cohort selection for score normalisation in speaker verification. Pattern Recogn Lett, 18(9):881-888, 1997. (Pubitemid 127411231)
8. D. Guan, Y.-K. Lee, and S. Lee. Activity recognition with the aid of unlabeled samples. In ICUIMC 2009: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, pages 670-674. ACM, 2009.
9. S. Kallio, J. Kela, P. Korpipää, and J. Mäntyjärvi. User independent gesture interaction for small handheld devices. International Journal of Pattern Recognition and Artificial Intelligence, 20(4):505-524, 2006. (Pubitemid 43999894)
10. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In NIPS 2006: Advances in Neural Information Processing Systems, volume 19. MIT Press, 2007.
11. H.-K. Lee and J. H. Kim. An HMM-based threshold model approach for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10):961-973, 1999.
12. K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In CIKM 2000: Proceedings of the ninth international conference on Information and knowledge management, pages 86-93. ACM, 2000.
13. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning: transfer learning from unlabeled data. In ICML 2007: Proceedings of the 24th international conference on Machine learning, volume 227, pages 759-766. ACM, 2007.
14. H.-D. Yang, S. Sclaroff, and S.-W. Lee. Sign language spotting with a threshold model based on conditional random fields. IEEE T Pattern Anal, 31(7):1264-1277, 2009.