Using unlabeled data in a sparse-coding framework for human activity recognition

Pervasive and Mobile Computing

Volumn 15, Issue , 2014, Pages 242-262

  Bhattacharya, Sourav ^a   Nurmi, Petteri ^a   Hammerla, Nils ^b   Plötz, Thomas ^b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

Activity recognition; Machine learning; Sparse coding; Unsupervised learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION; SUPERVISED LEARNING; UBIQUITOUS COMPUTING; UNSUPERVISED LEARNING; VECTOR SPACES;

ACTIVITIES OF DAILY LIVING; ACTIVITY RECOGNITION; FEATURE REPRESENTATION; GENERALIZATION CAPABILITY; HUMAN ACTIVITY RECOGNITION; SPARSE CODING; STATE-OF-THE-ART APPROACH; SUPERVISED LEARNING APPROACHES;

CODES (SYMBOLS);

EID: 84912150169     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2014.05.006     Document Type: Article

References (62)

1. L. Atallah, G.-Z. Yang, The use of pervasive sensing for behaviour profiling - a survey, Pervasive and Ubiquitous Computing 5 (5) (2009) 447-464.
2. N.D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, A.T. Campbell, A survey of mobile phone sensing, IEEE Commun. Mag. 48 (9) (2010) 140-150.
3. L. Bao, S.S. Intille, Activity recognition from user-annotated acceleration data, in: A. Ferscha, F. Mattern (Eds.), Proc. Int. Conf. Pervasive Comp. (Pervasive), 2004.
4. B. Logan, J. Healey, M. Philipose, E.M. Tapia, S. Intille, A long-term evaluation of sensing modalities for activity recognition, in: Proc. ACM Conf. Ubiquitous Comp. (UbiComp), 2007.
5. C. Pham, P. Olivier, Slice&dice: Recognizing food preparation activities using embedded accelerometers, in: Proc. Int. Conf. Ambient Intell. (AmI), 2009.
6. J. Hoey, T. Plötz, D. Jackson, A. Monk, C. Pham, P. Olivier, Rapid specification and automated generation of prompting systems to assist people with dementia, Pervasive Ubiquitous Comput. 7 (3) (2011) 299-318. http://dx.doi.org/10.1016/j.pmcj.2010.11.007.
7. S. Consolvo, D.W. McDonald, T. Toscos, M.Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, I. Smith, J.A. Landay, Activity sensing in the wild: a field trial of ubifit garden, in: Proc. ACM SIGCHI Conf. on Human Factors in Comp. Systems (CHI), 2008.
8. M. Rabbi, S. Ali, T. Choudhury, E. Berke, Passive and in-situ assessment of mental and physical well-being using mobile sensors, in: Proc. ACM Conf. Ubiquitous Comp. (UbiComp), 2011.
9. Thomas Plötz, Nils Y. Hammerla, Agata Rozga, Andrea Reavis, Nathan Call, Gregory D. Abowd, Automatic assessment of problem behavior in individuals with developmental disabilities, in: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp '12, Pittsburgh, Pennsylvania, ACM, New York, NY, USA, ISBN: 978-1-4503-1224-0, 2012, pp. 391-400.
10. J. Lester, T. Choudhury, G. Borriello, A practical approach to recognizing physical activities, in: Proc. Int. Conf. Pervasive Comp. (Pervasive), 2006.
11. J. Pärkkä, M. Ermes, P. Korpipää, J. Mäntyjärvi, J. Peltola, I. Korhonen, Activity classification using realistic data from wearable sensors, Biomedicine 10 (1) (2006) 119-128.
12. C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
13. D. Figo, P. Diniz, D. Ferreira, J. Cardoso, Preprocessing techniques for context recognition from accelerometer data, Pervasive Mobile Comput. 14 (7) (2010) 645-662.
14. T. Huynh, B. Schiele, Analyzing features for activity recognition, in: Proc. Joint Conf. on Smart objects and Ambient Intell. (sOc-EUSAI), 2005.
15. V. Könönen, J. Mäntyjärvi, H. Similä, J. Pärkkä, M. Ermes, Automatic feature selection for context recognition in mobile devices, Pervasive Ubiquitous Comput. 6 (2) (2010) 181-197.
16. T. Huynh, M. Fritz, B. Schiele, Discovery of activity patterns using topic models, in: Proc. ACM Conf. Ubiquitous Comp. (UbiComp), 2008, pp. 10-19. http://doi.acm.org/10.1145/1409635.1409638.
17. M. Stikic, D. Larlus, S. Ebert, B. Schiele, Weakly supervised recognition of daily life activities with wearable sensors, IEEE Trans. Pattern Anal. Mach. Intell. 33 (12) (2011) 2521-2537.
18. R. Raina, A. Battle, H. Lee, B. Packer, A.Y. Ng, Self-taught learning: transfer learning from unlabeled data, in: Proc. Int. Conf. on Machine Learning (ICML), 2007.
19. R. Grosse, R. Raina, H. Kwong, A.Y. Ng, Shift-invariance sparse coding for audio classification, in: Proc. Int. Conf. Uncertainty Art. Intell. (UAI), 2007.
20. D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. Förster, G. Tröster, P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha, J. Doppler, C. Holzmann, M. Kurz, G. Holl, R. Chavarriaga, H. Sagha, H. Bayati, M. Creatura, J. del R. Millán, Collecting complex activity datasets in highly rich networked sensor environments, in: Networked Sensing Systems (INSS), 2010 Seventh International Conference on, 2010, pp. 233-240. http://dx.doi.org/10.1109/INSS.2010.5573462.
21. O. Amft, Self-taught learning for activity spotting in on-body motion sensor data, in: Proc. Int. Symp. Wearable Comp. (ISWC), 2011.
22. O. Chapelle, B. Schölkopf, A. Zien (Eds.), Semi-Supervised Learning, MIT Press, 2010.
23. D. Guan, W.Y. Lee, Y.-K. Lee, A. Gavrilov, S. Lee, Activity recognition based on semi-supervised learning, in: Proc. IEEE Int. Conf. on Embedded and Real-Time Comp. Systems and Applications (RTCSA), 2007.
24. M. Stikic, K.B. Schiele, Exploring semi-supervised and active learning for activity recognition, in: Proc. Int. Symp. Wearable Comp. (ISWC), 2008.
25. K. Nigam, A.K. McCallum, S. Thrun, T. Mitchell, Text classification from labeled and unlabeled documents using EM, Mach. Learn. 39 (2-3) (2000) 103-134. Special issue on information retrieval.
26. T. Stiefmeier, D. Roggen, G. Tröster, G. Ogris, P. Lukowicz, Wearable activity tracking in car manufacturing, IEEE Pervasive Comput. 7 (2) (2008) 42-50.
27. H. Alemar, T.L.M. van Kasteren, C. Ersoy, Using active learning to allow activity recognition on a large scale, in: Proc. Int. Joint Conf. Ambient Intell. (AmI), Springer, 2011.
28. R. Caruana, Multitask learning, Mach. Learn. 28 (1) (1997) 41-75.
29. D.H. Hu, V.W. Zheng, Q. Yang, Cross-domain activity recognition via transfer learning, Pervasive Ubiquitous Comput. 7 (3) (2011) 344-358.
30. T.L.M. van Kasteren, G. Englebienne, B.J.A. Kröse, Transferring knowledge of activity recognition across sensor networks, in: Proc. Int. Conf. Pervasive Comp. (Pervasive), 2010.
31. Nicholas D. Lane, Ye Xu, Hong Lu, Shaohan Hu, Tanzeem Choudhury, Andrew T. Campbell, Feng Zhao, Enabling large-scale human activity inference on smartphones using community similarity networks (CSN), in: Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp '11, Beijing, China, ACM, New York, NY, USA, ISBN: 978-1-4503-0630-0, 2011, pp. 355-364.
32. R.A. Amar, D.R. Dooly, S.A. Goldman, Q. Zhang, Multiple-instance learning of real-valued data, in: Proc. Int. Conf. on Machine Learning (ICML), 2001.
33. M. Stikic, B. Schiele, Activity recognition from sparsely labeled data using multi-instance learning, in: Proc. Int. Symp. on Location and Context- Awareness (LoCA), 2009.
34. A. Coates, H. Lee, A.Y. Ng, An analysis of single-layer networks in unsupervised feature learning, in: Proc. Int. Conf. Art. Intell. and Statistics (AISTAT), 2011.
35. G.E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18 (7) (2006) 1527-1554.
36. J. Mantyjärvi, J. Himberg, T. Seppänen, Recognizing human motion with multiple acceleration sensors, in: 2001 IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, 2001, pp. 747-752.
37. T. Plötz, N.Y. Hammerla, P. Olivier, Feature learning for activity recognition in ubiquitous computing, in: Proc. Int. Joint Conf. Art. Intell. (IJCAI), 2011.
38. N. Hammerla, R. Kirkham, P. Andras, T. Plötz, On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution, in: Proc. Int. Symp. Wearable Computing (ISWC), 2013.
39. D. Minnen, T. Starner, I. Essa, C. Isbell, Discovering characteristic actions from on-body sensor data, in: Proc. Int. Symp. Wearable Comp. (ISWC), 2006.
40. J. Frank, S. Mannor, D. Precup, Activity and gait recognition with time-delay embeddings, in: Proc. AAAI Conf. Art. Intell. (AAAI), 2010.
41. P.O. Hoyer, Non-negative sparse coding, in: Proc. IEEE Workshop on Neural Networks for Signal Processing, 2002.
42. H. Lee, A. Battle, R. Raina, A.Y. Ng, Efficient sparse coding algorithms, in: Proc. Int. Conf. Neural Information Proc. Systems (NIPS), 2007.
43. Bruno A. Olshausen, David J. Field, Sparse coding with an overcomplete basis set: a strategy employed by V1, Vis. Res. 37 (23) (1997) 3311-3325. http://dx.doi.org/10.1016/S0042-6989(97)00169-7.
44. P. Berkhin, Survey of clustering data mining techniques, in: J. Kogan, C. Nicholas, M. Teboulle (Eds.), Grouping Multidimensional Data, Springer, 2006, pp. 25-71. http://dx.doi.org/10.1007/3-540-28349-8.
45. David Lazer, Alex S. Pentland, Lada Adamic, Sinan Aral, Albert L. Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Yony Jebara, Gary King, Michael Macy, Deb Roy, Marshall V. Alstyne, Life in the network: the coming age of computational social science, Science 323 (5915) (2009) 721-723. http://dx.doi.org/10.1126/science.1167742.
46. L. Liao, D.J. Patterson, D. Fox, H. Kautz, Learning and inferring transportation routines, Artificial Intelligence 171 (5:6) (2007).
47. Y. Zheng, Y. Liu, J. Yuan, X. Xie, Urban computing with taxicabs, in: Proc. ACM Conf. Ubiquitous Comp. (UbiComp), 2011.
48. D. Soper, Is human mobility tracking a good idea? Commun. ACM 55 (4) (2012) 35-37.
49. T. Brezmes, J.-L. Gorricho, J. Cotrina, Activity recognition from accelerometer data on a mobile phone, in: Workshop Proc. of 10th Int. Work-Conference on Artificial Neural Networks (IWANN), 2009.
50. Sasank Reddy, Min Mun, Jeff Burke, Deborah Estrin, Mark Hansen, Mani Srivastava, Using mobile phones to determine transportation modes, ACM Trans. on Sensor Networks 6 (2) (2010) 13:1-13:27. http://dx.doi.org/10.1145/1689239.1689243.
51. S. Wang, C. Chen, J. Ma, Accelerometer based transportation mode recognition on mobile phones, in: Proc. Asia-Pacific Conf. on Wearable Computing Systems.
52. S. Hemminki, P. Nurmi, S. Tarkoma, Accelerometer-based transportation mode detection on smartphones, in: Embedded Networked Sensor Systems (SenSys), 2013.
53. Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem Choudhury, Andrew T. Campbell, The Jigsaw continuous sensing engine for mobile phone applications, in: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys'10, ACM, New York, NY, USA, ISBN: 978-1-4503-0344-6, 2010, pp. 71-84.
54. T. Plötz, P. Moynihan, C. Pham, P. Olivier, Activity recognition and healthier food preparation, in: Activity Recognition in Pervasive Intelligent Environments, Vol. 4, Atlantis Press, 2011, pp. 313-329.
55. I. Joliffe, Principal Component Analysis, Springer, 1986.
56. H. Sagha, S. Digumarti, J. del R. Millan, R. Chavarriaga, A. Calatroni, D. Roggen, G. Tröster, Benchmarking classification techniques using the opportunity human activity dataset, in: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2011.
57. Q. McNemar, Note on the sampling error of the difference between correlated proportions or percentages, Psychometrika 12 (1947) 153-157. URL http://dx.doi.org/10.1007/BF02295996.
58. P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley Longman Publishing Co., Inc., 2005.
59. M.B. Kjærgaard, S. Bhattacharya, H. Blunck, P. Nurmi, Energy-efficient trajectory tracking for mobile devices, in: The 9th International Conference on Mobile Systems, Applications and Services, 2011, pp. 307-320.
60. S. Bhattacharya, H. Blunck, M. Kjærgaard, P. Nurmi, Robust and energy-efficient trajectory tracking for mobile devices, IEEE Trans. Mobile Comput. PP (99) (2014) 1-1. http://dx.doi.org/10.1109/TMC.2014.2318712.
61. P. Lukowicz, G. Pirkl, D. Bannach, F. Wagner, A. Calatroni, K. Förster, T. Holleczek, M. Rossi, D. Roggen, G. Tröster, J. Doppler, C. Holzmann, A. Riener, A. Ferscha, R. Chavarriaga, Recording a complex, multi modal activity data set for context recognition, in: ARCS Workshops, 2010, pp. 161-166.
62. A. Bulling, U. Blanke, B. Schiele, A tutorial on human activity recognition using body-worn inertial sensors, ACM Comput. Surv. 46 (3) (2014) 33:1-33:33.