A survey on human activity recognition using wearable sensors

IEEE Communications Surveys and Tutorials

Volumn 15, Issue 3, 2013, Pages 1192-1209

  Lara, Óscar D ^a   Labrador, Miguel A ^a  

^a University of South Florida   (United States)

Author keywords

context awareness; Human centric sensing; machine learning; mobile applications

Indexed keywords

CONTEXT- AWARENESS; GENERAL ARCHITECTURES; HUMAN ACTIVITY RECOGNITION; HUMAN-CENTRIC SENSING; ISSUES AND CHALLENGES; LEARNING APPROACH; MOBILE APPLICATIONS; RECOGNITION PERFORMANCE;

ENERGY UTILIZATION; LEARNING SYSTEMS; MOBILE DEVICES; SENSORS; SURVEYS; UBIQUITOUS COMPUTING;

PATTERN RECOGNITION;

EID: 84881311778     PISSN: None     EISSN: 1553877X     Source Type: Journal    
DOI: 10.1109/SURV.2012.110112.00192     Document Type: Article

References (95)

1. A. J. Perez, M. A. Labrador, and S. J. Barbeau, "G-sense: A scalable architecture for global sensing and monitoring," IEEE Network, vol. 24, no. 4, pp. 57-64, 2010.
2. Y. Jia, "Diatetic and exercise therapy against diabetes mellitus," in Second International Conference on Intelligent Networks and Intelligent Systems, pp. 693-696, 2009.
3. J. Yin, Q. Yang, and J. Pan, "Sensor-based abnormal human-activity detection," IEEE Trans. Knowl. Data Eng., vol. 20, no. 8, pp. 1082-1090, 2008.
4. O. X. Schlmilch, B. Witzschel, M. Cantor, E. Kahl, R. Mehmke, and C. Runge, "Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring," Computers in Human Behavior, vol. 15, no. 5, pp. 571-583, 1999.
5. F. Foerster, M. Smeja, and J. Fahrenberg, "Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring," Computers in Human Behavior, vol. 15, no. 5, pp. 571-583, 1999.
6. E. Kim, S. Helal, and D. Cook, "Human activity recognition and pattern discovery," IEEE Pervasive Computing, vol. 9, no. 1, pp. 48-53, 2010.
7. T. van Kasteren, G. Englebienne, and B. Krse, "An activity monitoring system for elderly care using generative and discriminative models," J. Personal and Ubiquitous Computing, 2010.
8. A. Tolstikov, X. Hong, J. Biswas, C. Nugent, L. Chen, and G. Parente, "Comparison of fusion methods based on dst and dbn in human activity recognition," J. Control Theory and Applications, vol. 9, pp. 18-27, 2011.
9. J. Yang, J. Lee, and J. Choi, "Activity recognition based on rfid object usage for smart mobile devices," J. Computer Science and Technology, vol. 26, pp. 239-246, 2011.
10. J. Sarkar, L. Vinh, Y.-K. Lee, and S. Lee, "Gpars: a general-purpose activity recognition system," Applied Intelligence, pp. 1-18, 2010.
11. J. Hong and T. Ohtsuki, "A state classification method based on spacetime signal processing using svm for wireless monitoring systems," in 2011 IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), pp. 2229-2233, sept. 2011.
12. P. Turaga, R. Chellappa, V. Subrahmanian, and O. Udrea, "Machine recognition of human activities: A survey," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 11, pp. 1473-1488, 2008.
13. J. Candamo, M. Shreve, D. Goldgof, D. Sapper, and R. Kasturi, "Understanding transit scenes: A survey on human behavior-recognition algorithms," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 1, pp. 206-224, 2010.
14. C. N. Joseph, S. Kokulakumaran, K. Srijeyanthan, A. Thusyanthan, C. Gunasekara, and C. Gamage, "A framework for whole-body gesture recognition from video feeds," in International Conference on Industrial and Information Systems (ICIIS), pp. 430-435, 2010.
15. M. Ahad, J. Tan, H. Kim, and S. Ishikawa, "Human activity recognition: Various paradigms," in International Conference on Control, Automation and Systems, pp. 1896-1901, 2008.
16. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, Alex, Kipman, and A. Blake, "Real-time human pose recognition in parts from single depth images," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2011.
17. J. Iglesias, J. Cano, A. M. Bernardos, and J. R. Casar, "A ubiquitous activity-monitor to prevent sedentariness," in Proc. IEEE Conference on Pervasive Computing and Communications, 2011.
18. D. Choujaa and N. Dulay, "Tracme: Temporal activity recognition using mobile phone data," in IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, vol. 1, pp. 119-126, 2008.
19. J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, and I. Korhonen, "Activity classification using realistic data from wearable sensors," IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 1, pp. 119-128, 2006. (Pubitemid 43092325)
20. L. C. Jatoba, U. Grossmann, C. Kunze, J. Ottenbacher, and W. Stork, "Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity," in 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5250-5253, 2008.
21. T. Brezmes, J.-L. Gorricho, and J. Cotrina, "Activity recognition from accelerometer data on a mobile phone," in Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, vol. 5518, pp. 796-799, Springer Berlin/Heidelberg, 2009.
22. K. Oh, H.-S. Park, and S.-B. Cho, "A mobile context sharing system using activity and emotion recognition with bayesian networks," in International Conference on Ubiquitous Intelligence Computing, pp. 244-249, 2010.
23. E. M. Tapia, S. S. Intille, W. Haskell, K. Larson, J. Wright, A. King, and R. Friedman, "Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart monitor," in Proc. International Symposium on Wearable Computers, 2007.
24. T.-P. Kao, C.-W. Lin, and J.-S. Wang, "Development of a portable activity detector for daily activity recognition," in IEEE International Symposium on Industrial Electronics, pp. 115-120, 2009.
25. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher, "Activity recognition and monitoring using multiple sensors on different body positions," in Proc. International Workshop on Wearable and Implantable Body Sensor Networks, (Washington, DC, USA), IEEE Computer Society, 2006.
26. O. D. Lara, A. J. Perez, M. A. Labrador, and J. D. Posada, "Centinela: A human activity recognition system based on acceleration and vital sign data," Journal on Pervasive and Mobile Computing, 2011.
27. L. Bao and S. S. Intille, "Activity recognition from user-annotated acceleration data," in Pervasive, pp. 1-17, 2004.
28. Y. Hanai, J. Nishimura, and T. Kuroda, "Haar-like filtering for human activity recognition using 3d accelerometer," in IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 675-678, 2009.
29. Z.-Y. He and L.-W. Jin, "Activity recognition from acceleration data using ar model representation and svm," in International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2245-2250, 2008.
30. M. Berchtold, M. Budde, H. Schmidtke, and M. Beigl, "An extensible modular recognition concept that makes activity recognition practical," in Advances in Artificial Intelligence, Lecture Notes in Computer Science, pp. 400-409, Springer Berlin/Heidelberg, 2010.
31. D. Riboni and C. Bettini, "Cosar: hybrid reasoning for context-aware activity recognition," Personal and Ubiquitous Computing, vol. 15, pp. 271-289, 2011.
32. T. Brezmes, J.-L. Gorricho, and J. Cotrina, "Activity recognition from accelerometer data on a mobile phone," in Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, vol. 5518 of Lecture Notes in Computer Science, pp. 796-799, Springer Berlin/Heidelberg, 2009.
33. M. Berchtold, M. Budde, D. Gordon, H. Schmidtke, and M. Beigl, "Actiserv: Activity recognition service for mobile phones," in International Symposium on Wearable Computers, pp. 1-8, 2010.
34. Z. He, Z. Liu, L. Jin, L.-X. Zhen, and J.-C. Huang, "Weightlessness feature; a novel feature for single tri-axial accelerometer based activity recognition," in 19th International Conference on Pattern Recognition, pp. 1-4, 2008.
35. Z. He and L. Jin, "Activity recognition from acceleration data based on discrete consine transform and svm," in IEEE International Conference on Systems, Man and Cybernetics, pp. 5041-5044, 2009.
36. Y.-P. Chen, J.-Y. Yang, S.-N. Liou, Gwo Yun=Lee, and J.-S. Wang, "Online classifier construction algorithm for human activity detection using a tri-axial accelerometer," Applied Mathematics and Computation, vol. 205, no. 2, pp. 849-860, 2008.
37. S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava, "Using mobile phones to determine transportation modes," ACM Trans. Sensor Networks, vol. 6, no. 2, pp. 1-27, 2010.
38. A. Khan, Y. Lee, and S. Lee, "Accelerometer's position free human activity recognition using a hierarchical recognition model," in IEEE International Conference on e-Health Networking Applications and Services (Healthcom), pp. 296-301, 2010.
39. A. Khan, Y.-K. Lee, S. Lee, and T.-S. Kim, "A triaxial accelerometerbased physical-activity recognition via augmented-signal features and a hierarchical recognizer," IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 5, pp. 1166-1172, 2010.
40. K. Frank, M. Rockl, M. Nadales, P. Robertson, and T. Pfeifer, "Comparison of exact static and dynamic bayesian context inference methods for activity recognition," in IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 189-195, 2010.
41. "Google Places API," http://code.google.com/apis/maps/ documentation/places/.
42. K. L. Huang, S. S. Kanhere, and W. Hu, "Preserving privacy in participatory sensing systems," Computer Communications, vol. 33, no. 11, pp. 1266-1280, 2010.
43. D. Christin, A. Reinhardt, S. S. Kanhere, and M. Hollick, "A survey on privacy in mobile participatory sensing applications," J. Systems and Software, vol. 84, no. 11, pp. 1928-1946, 2011.
44. I. J. Vergara-Laurens and M. A. Labrador, "Preserving privacy while reducing power consumption and information loss in lbs and participatory sensing applications," in Proc. IEEE GLOBECOM, 2011.
45. M. Ermes, J. Parkka, and L. Cluitmans, "Advancing from offline to online activity recognition with wearable sensors," in Engineering in Medicine and Biology Society. 30th Annual International Conference of the IEEE, pp. 4451-4454, 2008.
46. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher, "Activity recognition and monitoring using multiple sensors on different body positions," in Proc. International Workshop on Wearable and Implantable Body Sensor Networks, pp. 113-116, 2006. (Pubitemid 44711293)
47. B. Longstaff, S. Reddy, and D. Estrin, "Improving activity classification for health applications on mobile devices using active and semi-supervised learning," in Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 1-7, 2010.
48. O. D. Lara and M. A. Labrador, "A mobile platform for real time human activity recognition," in Proc. IEEE Conference on Consumer Communications and Networks, 2012.
49. I. H. Witten and E. Frank, Data Mining, Practical Machine Learning Tools and Techniques. Elsevier, 2 ed., 2005.
50. R. T. Olszewski, C. Faloutsos, and D. B. Dot, Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. 2001.
51. K. Altun and B. Barshan, "Human activity recognition using inertial/magnetic sensor units," in Human Behavior Understanding, Lecture Notes in Computer Science, pp. 38-51, Springer Berlin/Heidelberg, 2010.
52. T. Choudhury, S. Consolvo, B. Harrison, J. Hightower, A. LaMarca, L. LeGrand, A. Rahimi, A. Rea, G. Bordello, B. Hemingway, P. Klasnja, K. Koscher, J. Landay, J. Lester, D. Wyatt, and D. Haehnel, "The mobile sensing platform: An embedded activity recognition system," IEEE Pervasive Computing, vol. 7, no. 2, pp. 32-41, 2008. (Pubitemid 351575120)
53. J. Penttil, J. Peltola, and T. Seppnen, "A speech/music discriminator based audio browser with a degree of certanity measure," in Proc. International Workshop Information Retrieval, p. 125-131, 2001.
54. G. Dahlquist and Ake Bjorck, Numerical methods in scientific computing. vol 1. SIAM, 2008.
55. J. Cheng, O. Amft, and P. Lukowicz, "Active capacitive sensing: Exploring a new wearable sensing modality for activity recognition," in Pervasive Computing, vol. 6030 of Lecture Notes in Computer Science, pp. 319-336, Springer Berlin/Heidelberg, 2010.
56. D. Minnen, T. Westeyn, D. Ashbrook, P. Presti, and T. Starner, "Recognizing soldier activities in the field," in 4th International Workshop on Wearable and Implantable Body Sensor Networks, vol. 13, pp. 236-241, Springer Berlin Heidelberg, 2007.
57. D. McGlynn and M. G. Madden, "An ensemble dynamic time warping classifier with application to activity recognition," in Research and Development in Intelligent Systems XXVII, pp. 339-352, Springer London, 2011.
58. N. Pham and T. Abdelzaher, "Robust dynamic human activity recognition based on relative energy allocation," in Distributed Computing in Sensor Systems, vol. 5067 of Lecture Notes in Computer Science, pp. 525-530, Springer Berlin/Heidelberg, 2008.
59. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, 2005. (Pubitemid 41245053)
60. M. A. Hall, Correlation-based Feature Selection for Machine Learning. 1999.
61. "The Waikato Environment for Knowledge Analysis," http://www.cs. waikato.ac.nz/ml/weka/.
62. R. Cilla, M. Patricio, A. Berlanga, and J. Molina, "Creating human activity recognition systems using pareto-based multiobjective optimization," in Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009.
63. M. Stikic, D. Larlus, and B. Schiele, "Multi-graph based semisupervised learning for activity recognition," in International Symposium on Wearable Computers, pp. 85-92, 2009.
64. M. Stikic, D. Larlus, S. Ebert, and B. Schiele, "Weakly supervised recognition of daily life activities with wearable sensors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2521-2537, 2011.
65. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. eleventh annual conference on Computational learning theory, pp. 92-100, ACM, 1998.
66. C. Randell and H. Muller, "Context awareness by analysing accelerometer data," in The Fourth International Symposium on Wearable Computers, pp. 175-176, 2000. (Pubitemid 32072879)
67. M. Berchtold, M. Budde, D. Gordon, H. Schmidtke, and M. Beigl, "Actiserv: Activity recognition service for mobile phones," pp. 1-8, 2010.
68. C. Zhu and W. Sheng, "Human daily activity recognition in robotassisted living using multi-sensor fusion," in IEEE International Conference on Robotics and Automation, pp. 2154-2159, 2009.
69. L. Vinh, S. Lee, H. Le, H. Ngo, H. Kim, M. Han, and Y.-K. Lee, "Semimarkov conditional random fields for accelerometer-based activity recognition," Applied Intelligence, vol. 35, pp. 226-241, 2011.
70. J. Quinlan, C4.5: programs for machine learning. Morgan Kaufmann series in machine learning, Morgan Kaufmann Publishers, 1993.
71. P. Antal, "Construction of a classifier with prior domain knowledge formalised as bayesian network," in Proc. 24th Annual Conference of the IEEE Industrial Electronics Society, vol. 4, pp. 2527-2531, 1998.
72. H. Zhang, "The Optimality of Naive Bayes.," in FLAIRS Conference, AAAI Press, 2004.
73. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
74. S. Gallant, "Perceptron-based learning algorithms," IEEE Trans. Neural Netw., vol. 1, no. 2, pp. 179-191, 1990. (Pubitemid 20713330)
75. A. Ali, R. King, and G.-Z. Yang, "Semi-supervised segmentation for activity recognition with multiple eigenspaces," in International Summer School and Symposium on Medical Devices and Biosensors, pp. 314-317, 2008.
76. D. Guan, W. Yuan, Y.-K. Lee, A. Gavrilov, and S. Lee, "Activity recognition based on semi-supervised learning," in 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 469-475, 2007.
77. T. Huynh and B. Schiele, "Towards less supervision in activity recognition from wearable sensors," in 10th IEEE International Symposium on Wearable Computers, pp. 3-10, 2006.
78. "The Java Data Mining Platform," http://www.jdmp.org/.
79. J. Nishimura and T. Kuroda, "Multiaxial haar-like feature and compact cascaded classifier for versatile recognition," IEEE Sensors J., vol. 10, pp. 1786-1795, nov. 2010.
80. "Zephyr Bioharness BT Website," http://www.zephyr-technology. com/bioharness-bt.html.
81. "Google's Android becomes the world's leading smart phone platform," http://www.canalys.com/newsroom/google%E2%80%99s-android- becomes-world%E2%80% 99s-leading-smart-phone-platform.
82. D. Arvidsson, F. Slinde, and L. Hulth́en, "Free-living energy expenditure in children using multi-sensor activity monitors," Clinical nutrition (Edinburgh, Scotland), vol. 28:3, s. 305, 2009.
83. A. Teller, "A platform for wearable physiological computing," Interacting with Computers, pp. 917-937, 2004.
84. "Google Scholar," http://scholar.google.com/.
85. "Sprint Java ADP," http://developer.sprint.com/site/global/ home/p home.jsp.
86. A. Khan, Y. Lee, and S. Lee, "Accelerometer's position free human activity recognition using a hierarchical recognition model," in 12th IEEE International Conference on e-Health Networking Applications and Services (Healthcom), pp. 296-301, 2010.
87. "Datasets for Human Activity Recognition from Tim Van Kasteren's website," https://sites.google.com/site/tim0306/datasets.
88. "Datasets for Human Activity Recognition from MIT Media Lab," http://architecture.mit.edu/house n/data/PlaceLab/PlaceLab.htm.
89. "Datasets for Human Activity Recognition from Tanzeem Choudhury's website," http://www.cs.dartmouth.edu/?tanzeem/teaching/CS188-Fall08/ dataset.html.
90. "Datasets for Human Activity Recognition from ETH's Wearable Computing Lab," http://www.wearable.ethz.ch/resources/Dataset.
91. "2011 Activity Recognition Challenge," www.oportunity-project. eu/challenge.
92. U. Blanke and B. Schiele, "Remember and transfer what you have learned-recognizing composite activities based on activity spotting," in International Symposium on Wearable Computers, pp. 1-8, 2010.
93. R. Helaoui, M. Niepert, and H. Stuckenschmidt, "Recognizing interleaved and concurrent activities: A statistical-relational approach," in IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1-9, 2011.
94. C. Elkan, "The foundations of cost-sensitive learning," in Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pp. 973-978, 2001.
95. N. Lane, Y. Xu, H. Lu, A. Campbell, T. Choudhury, and S. Eisenman, "Exploiting social networks for large-scale human behavior modeling," IEEE Pervasive Computing, vol. 10, no. 4, pp. 45-53, 2011.