b-COELM: A fast, lightweight and accurate activity recognition model for mini-wearable devices

Pervasive and Mobile Computing

Volumn 15, Issue , 2014, Pages 200-214

  Hu, Lisha ^a,b,c   Chen, Yiqiang ^a,b   Wang, Shuangquan ^a,b   Chen, Zhenyu ^a,b,c  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Activity recognition; Extreme learning machine; Proximal support vector machine; Wearable device

Indexed keywords

COMPUTATIONAL COMPLEXITY; KNOWLEDGE ACQUISITION; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES;

ACTIVITIES OF DAILY LIVING; ACTIVITY RECOGNITION; EXTREME LEARNING MACHINE; GENERALIZATION ABILITY; LOW COMPUTATIONAL COMPLEXITY; MULTICLASS CLASSIFICATION PROBLEMS; PROXIMAL SUPPORT VECTOR MACHINES; WEARABLE DEVICES;

WEARABLE TECHNOLOGY;

EID: 84912150112     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2014.06.002     Document Type: Article

References (72)

1. N.D. Lane, et al., Enabling large-scale human activity inference on smartphones using community similarity networks (CSN), in: Proceedings of the ACM Conference on Ubiquitous Computing, 2011, pp. 355-364.
2. Y.Q. Chen, et al., Surrounding context and episode awareness using dynamic bluetooth data, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2012, pp. 629-630.
3. K. Ouchi, M. Doi, Indoor-outdoor activity recognition by a smartphone, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2012, pp. 600-601.
4. H. Lee, et al., Mobile posture monitoring system to prevent physical health risk of smartphone users, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2012, pp. 592-593.
5. J. Beltrń-Márquez, Activity recognition using a spectral entropy signature, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2012, pp. 576-579.
6. K. Yatani, K.N. Truong, Bodyscope: a wearable acoustic sensor for activity recognition, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2012, pp. 341-350.
7. J. Park, et al., Online pose classification and walking speed estimation using handheld devices, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2012, pp. 113-122.
8. Z.Y. Chen, et al., Inferring social contextual behavior from bluetooth traces, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2013, pp. 267-270.
9. G. Spina, et al., COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2013, pp. 597-606.
10. H. Hung, et al., Classifying social actions with a single accelerometer, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2013, pp.207-210.
11. H. Gjoreski, et al., Ensembles of multiple sensors for human energy expenditure estimation, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2013, pp.359-362.
12. Z.Y. Chen, et al., Contextsense: unobtrusive discovery of incremental social context using dynamic bluetooth data, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2014.
13. R. Wang, et al., Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2014.
14. V.W. Zheng, Qiang Yang, User-dependent aspect model for collaborative activity recognition, in: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, 2011, pp. 2085-2090.
15. Z. Zhao, et al., Cross-people mobile-phone based activity recognition, in: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, 2011, pp. 2545-2550.
16. X.Y. Gao, et al., SOML: sparse online metric learning with application to image retrieval, in: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.
17. Perera, J.F. Allen, Learning names for RFID-tagged objects in activity videos, in: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012, pp. 2449-2450.
18. S. Abdullah, et al., Towards population scale activity recognition: a framework for handling data diversity, in: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012, 851-857.
19. Z.Y. Chen, Mining individual behavior pattern based on significant locations and spatial trajectories, in: Proceedings of the International Conference on Pervasive Computing and Communications, 2012, pp. 540-541.
20. S. Kozina, et al., Efficient activity recognition and fall detection using accelerometers, in: Evaluating AAL Systems Through Competitive Benchmarking, Communications in Computer and Information Science, Vol. 386, 2013, pp. 13-23.
21. F. Palumbo, et al., Multisensor data fusion for activity recognition based on reservoir computing, in: Evaluating AAL Systems Through Competitive Benchmarking, Communications in Computer and Information Science, Vol. 386, 2013, pp. 24-35.
22. M. Ángel Álvarez de la Concepción, et al., Activity recognition system using non-intrusive devices through a complementary technique based on discrete methods, in: Evaluating AAL Systems Through Competitive Benchmarking, Communications in Computer and Information Science, Vol. 386, 2013, pp. 36-47.
23. J. Antonio Álvarez-García, Evaluating human activity recognition systems for AAL environments, in: Evaluating AAL Systems Through Competitive Benchmarking, Communications in Computer and Information Science, Vol. 386, 2013, pp. 131-136.
24. L.M. Soria Morillo, et al., Activity recognition system using AMEVA method, in: Evaluating AAL Systems Through Competitive Benchmarking, Communications in Computer and Information Science, Vol. 386, 2013, pp. 137-147.
25. J.H. Hong, et al., An activity recognition system for ambient assisted living environments, in: Evaluating AAL Systems Through Competitive Benchmarking, Communications in Computer and Information Science, Vol. 386, 2013, pp. 148-158.
26. N. Li, et al., Visual experience for recognising human activities, in: Evaluating AAL Systems Through Competitive Benchmarking, Communications in Computer and Information Science, Vol. 386, 2013, pp. 173-185.
27. P. Barsocchi, et al., RSSI localisation with sensors placed on the user, in: International Conference on Indoor Positioning and Indoor Navigation, 2010, pp.1-6.
28. R. Guraliuc, et al., Limb movements classification using wearable wireless transceivers, IEEE Trans. Inform. Technol. Biomed. 15 (3) (2011) 474-480.
29. L. Bao, S.S. Intille, Activity Recognition from User-Annotated Acceleration Data, in: Lecture Notes in Computer Science, vol. 3001, 2004, pp. 1-17.
30. E. Berlin, K.V. Laerhoven, Detecting leisure activities with dense motif discovery, in: Proceedings of the ACM Conference on Ubiquitous Computing, 2012, pp. 250-259.
31. Amft, et al., Detection of eating and drinking arm gestures using inertial body-worn sensors, in: Proceedings of the IEEE International Symposium on Wearable Computers, 2005, pp.160-163.
32. K.V. Laerhoven, et al., Enabling efficient time series analysis for wearable activity data, in: Proceedings of the International Conference on Machine Learning and Applications, 2009, pp.392-397.
33. D. Minnen, et al., Discovering characteristic actions from on-body sensor data, in: Proceedings of IEEE International Symposium on Wearable Computing, 2006, pp. 11-18.
34. J.R. Kwapisz, et al., Activity recognition using cell phone accelerometers, ACM SIGKDD Explor. Newslett. 12 (2) (2010) 74-82.
35. G. Bieber, et al., Activity Recognition for Everyday Life on Mobile Phones, in: Lecture Notes in Computer Science, vol. 5615, 2009, pp. 289-296.
36. T. Brezmes, et al., Activity Recognition from Aaccelerometer Data on a Mobile Phone, in: Lecture Notes in Computer Science, vol. 5518, 2009, pp. 796-799.
37. S. Consolvo, et al., Activity sensing in the wild: a field trial of UbiFit garden, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008, pp. 1797-1806.
38. Z.Y. Chen, et al., Unobtrusive sleep monitoring using smartphones, in: Proceedings of the 7th International ICST Conference on Pervasive Computing Technologies for Healthcare, 2013, pp. 145-152.
39. Y. Xu, et al., Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns, in: Proceedings of the 17th ACM International Symposium on Wearable Computers, 2013, pp. 69-76.
40. Z.Y. Chen, et al., Online sequential ELM based transfer learning for transportation mode recognition, in: Proceedings of the 6th IEEE International Conference on Cybernetics and Intelligent Systems, 2013, pp. 78-83.
41. T. Choudhury, et al., The mobile sensing platform: an embedded activity recognition system, IEEE Trans. Pervasive Comput. 7 (2) (2008) 1268-1536.
42. U. Maurer, et al., Location and Activity Recognition Using eWatch: A Wearable Sensor Platform, in: Lecture Notes in Computer Science, vol. 3864, 2006, pp. 86-102.
43. Z. Zeng, Q. Ji, Knowledge Based Activity Recognition with Dynamic Bayesian Network, in: Lecture Notes in Computer Science, vol. 6316, 2010, pp. 532-546.
44. MS. Kanchan Gaikwad, HMM classifier for human activity recognition, Internat. J. Comput. Sci. Eng. 2 (4) (2012) 27-36.
45. N. Oliver, E. Horvitz, A Comparison of HMMs and Dynamic Bayesian Networks for Recognizing Office Activities, in: Lecture Notes in Computer Science, vol. 3538, 2005, pp. 199-209.
46. Madabhushi, J.K. Aggarwal, A bayesian approach to human activity recognitio, in: Second IEEE Workshop on Visual Surveillance, 1999.
47. Y.Q. Song, et al., Collaborative boosting for activity classification in microblogs, in: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013, pp. 482-490.
48. Reiss, et al., A competitive approach for human activity recognition on smartphones, in: Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2013.
49. J. Pärkkä, et al., Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree, IEEE Trans. Inform. Technol. Biomed. 14 (5) (2010) 1211-1215.
50. J. Yang, Toward physical activity diary: motion recognition using simple acceleration features with mobile phones, in: Proceedings of the International Workshop on Interactive Multimedia for Consumer Electronics, 2009, pp. 1-10.
51. H.Y. Zhao, Z.J. Liu, Human action recognition based on non-linear SVM decision tree, J. Comput. Inform. Syst. 7 (7) (2011) 2461-2468.
52. D. Anguita, et al., Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine, in: Lecture Notes in Computer Science, vol. 7657, 2012, pp. 216-223.
53. H.M. Qian, et al., Recognition of human activities using SVM multi-class classifier, Pattern Recognit. Lett. 31 (2010) 100-111.
54. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
55. C. Cortes, V.N. Vapnik, Support vector networks, Mach. Learn. 20 (3) (1995) 273-297.
56. G. Fung, O.L. Mangassarian, Proximal support vector machine classifiers, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 2001, pp. 77-86.
57. J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural Process. Lett. 9 (3) (1999) 293-300.
58. J. Weston, C. Watkinis, Multi-class support vector machines, in: Proceedings of International Conference on European Symposium on Artificial Neural Networks, 1999.
59. J.C. Plat, et al., Large margin DAG's for multiclass classification, Adv. Neural Inf. Process. Syst. (2000) 547-553.
60. E.J. Bredensteiner, K.P. Bennett, Multicategory classification by support vector machines, Comput. Optim. Appl. 12 (1-3) (1999) 53-79.
61. Y. Guermeur, Combining discriminant models with new multiclass SVMs, Pattern Anal. Appl. 5 (2) (2002) 168-179.
62. C.W. Hsu, C.J. Lin, A comparison of methods for multiclass support vector machines, IEEE Trans. Neural Netw. 13 (2) (2002) 415-425.
63. Y.Z. Xing, et al., Multiclass least squares wavelet support vector machines, in: Proceedings of IEEE International Conference on Networking, Sensing and Control, April 6-8, 2008, pp. 498-502.
64. T.V. Gestel, et al., Multiclass LS- SVMs: moderated outputs and coding-decoding schemes, Neural Process. Lett. 15 (1) (2002) 45-58.
65. M.R. Guarracino, A. Irpino, R. Verde, Multiclass generalized eigenvalue proximal support vector machines, in: Proceedings of International Conference on Complex, Intelligent and Software Intensive Systems, February 15-18, 2010, pp. 25-32.
66. G.M. Fung, O.L Mangasarian, Multicategory proximal support vector machine classifiers, Mach. Learn. 59 (1-2) (2005) 77-97.
67. G.B. Huang, et al., Extreme learning machine: theory and applications, Neurocomputing 7 (1-3) (2006) 489-501.
68. G.B. Huang, et al., Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of IEEE International Joint Conference on Neural Networks, July 25-29, 2004, pp. 985-990.
69. G.B. Huang, et al., Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Netw. 17 (4) (2006) 879-892.
70. G.B. Huang, et al., Extreme learning machine for regression and multiclass classification, IEEE Trans. Syst. Man Cybern. 42 (2) (2012) 513-529.
71. J.W. Han, et al., Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher, 2000.
72. M. Hall, et al., The WEKA data mining software: an update, ACM SIGKDD Explor. 11 (1) (2009) 10-18.