Robust Automated Human Activity Recognition and Its Application to Sleep Research

IEEE International Conference on Data Mining Workshops, ICDMW

Volumn 0, Issue , 2016, Pages 495-502

  Sathyanarayana, Aarti ^a   Ofli, Ferda ^a   Fernandez Luque, Luis ^a   Srivastava, Jaideep ^a   Elmagarmid, Ahmed ^a   Arora, Teresa ^a   Taheri, Shahrad ^a  

^a QATAR COMPUTING RESEARCH INSTITUTE   (Qatar)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; CONTINUOUS TIME SYSTEMS; DATA MINING; HEALTH; PATTERN RECOGNITION; TIME SERIES; WEARABLE TECHNOLOGY;

APPLICATION AREA; HUMAN ACTIVITY RECOGNITION; HUMAN BEHAVIOURS; ITS APPLICATIONS; MARKET PENETRATION; PHYSICAL ACTIVITY; STATE-OF-THE-ART PROCEDURES; WEARABLE DEVICES;

SLEEP RESEARCH;

EID: 85015227318     PISSN: 23759232     EISSN: 23759259     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2016.0077     Document Type: Conference Paper

References (58)

1. H. Wang, D. Oneata, J. Verbeek, and C. Schmid, "A robust and efficient video representation for action recognition, " International Journal of Computer Vision, pp. 1-20, 2015.
2. B. Fernando, E. Gavves, J. Oramas, A. Ghodrati, and T. Tuytelaars, "Rank pooling for action recognition, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-1, 2016.
3. I. Atmosukarto, N. Ahuja, and B. Ghanem, "Action recognition using discriminative structured trajectory groups, " in 2015 IEEE Winter Conference on Applications of Computer Vision, Jan 2015, pp. 899-906.
4. S. Ma, L. Sigal, and S. Sclaroff, "Space-Time tree ensemble for action recognition, " in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
5. Z. Zhou, F. Shi, and W. Wu, "Learning spatial and temporal extents of human actions for action detection, " IEEE Transactions on Multimedia, vol. 17, no. 4, pp. 512-525, April 2015.
6. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, "Learning spatiotemporal features with 3d convolutional networks, " in 2015 IEEE International Conference on Computer Vision (ICCV), Dec 2015, pp. 4489-4497.
7. V. Kantorov and I. Laptev, "Efficient feature extraction, encoding and classification for action recognition, " in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
8. X. Peng, C. Zou, Y. Qiao, and Q. Peng, Action Recognition with Stacked Fisher Vectors. Cham: Springer International Publishing, 2014, pp. 581-595.
9. H. Kuehne, J. Gall, and T. Serre, "An end-To-end generative framework for video segmentation and recognition, " in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), March 2016, pp. 1-8.
10. J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, "Long-Term recurrent convolutional networks for visual recognition and description, " in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
11. L. Wang, Y. Qiao, and X. Tang, "Action recognition with trajectorypooled deep-convolutional descriptors, " in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 4305-4314.
12. L. Sun, K. Jia, D. Y. Yeung, and B. E. Shi, "Human action recognition using factorized spatio-Temporal convolutional networks, " in 2015 IEEE International Conference on Computer Vision (ICCV), Dec 2015, pp. 4597-4605.
13. S.-R. Ke, H. L. U. Thuc, Y.-J. Lee, J.-N. Hwang, J.-H. Yoo, and K.-H. Choi, "A review on video-based human activity recognition, " Computers, vol. 2, no. 2, pp. 88-131, 2013.
14. P. V. K. Borges, N. Conci, and A. Cavallaro, "Video-based human behavior understanding: A survey, " IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 11, pp. 1993-2008, Nov 2013.
15. N. Ikizler, R. G. Cinbis, S. Pehlivan, and P. Duygulu, "Recognizing actions from still images, " in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, Dec 2008, pp. 1-4.
16. C. Thurau and V. Hlavac, "Pose primitive based human action recognition in videos or still images, " in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, June 2008, pp. 1-8.
17. F. S. Khan, R. Muhammad Anwer, J. van de Weijer, A. D. Bagdanov, A. M. Lopez, and M. Felsberg, "Coloring action recognition in still images, " International Journal of Computer Vision, vol. 105, no. 3, pp. 205-221, 2013.
18. G. Sharma, F. Jurie, and C. Schmid, "Expanded parts model for semantic description of humans in still images, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-1, 2016.
19. "A survey on still image based human action recognition, " Pattern Recognition, vol. 47, no. 10, pp. 3343-3361, 2014.
20. L. Xia, C. C. Chen, and J. K. Aggarwal, "View invariant human action recognition using histograms of 3d joints, " in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 2012, pp. 20-27.
21. X. Yang and Y. L. Tian, "Eigenjoints-based action recognition using naive-bayes-nearest-neighbor, " in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 2012, pp. 14-19.
22. F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, "Sequence of the most informative joints (smij): A new representation for human skeletal action recognition, " Journal of Visual Communication and Image Representation, vol. 25, no. 1, pp. 24-38, 2014, visual Understanding and Applications with RGB-D Cameras. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1047320313000680
23. R. Chaudhry, F. Ofli, G. Kurillo, R. Bajcsy, and R. Vidal, "Bioinspired dynamic 3d discriminative skeletal features for human action recognition, " in 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 2013, pp. 471-478.
24. J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Mining actionlet ensemble for action recognition with depth cameras, " in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, June 2012, pp. 1290-1297.
25. R. Vemulapalli, F. Arrate, and R. Chellappa, "Human action recognition by representing 3d skeletons as points in a lie group, " in 2014 IEEE Conference on Computer Vision and Pattern Recognition, June 2014, pp. 588-595.
26. M. E. Hussein, M. Torki, M. A. Gowayyed, and M. El-Saban, "Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations, " IJCAI, vol. 13, pp. 2466-2472, 2013.
27. F. Lv and R. Nevatia, Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 359-372.
28. Y. Sheikh, M. Sheikh, and M. Shah, "Exploring the space of a human action, " in Tenth IEEE International Conference on Computer Vision (ICCV05) Volume 1, vol. 1, Oct 2005, pp. 144-149 Vol. 1.
29. J. Wang, Z. Liu, J. Chorowski, Z. Chen, and Y. Wu, Robust 3D Action Recognition with Random Occupancy Patterns. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 872-885.
30. A. W. Vieira, E. R. Nascimento, G. L. Oliveira, Z. Liu, and M. F. M. Campos, STOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 252-259.
31. "Human activity recognition from 3d data: A review, " Pattern Recognition Letters, vol. 48, pp. 70-80, 2014, celebrating the life and work of Maria Petrou.
32. L. Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Z. Yu, "Sensorbased activity recognition, " IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 790-808, Nov 2012.
33. A. Bulling, U. Blanke, and B. Schiele, "A tutorial on human activity recognition using body-worn inertial sensors, " ACM Comput. Surv., vol. 46, no. 3, pp. 33:1-33:33, Jan. 2014.
34. A. Calzada, J. Liu, C. D. Nugent, H. Wang, and L. Martinez, "Sensorbased activity recognition using extended belief rule-based inference methodology, " in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 2694-2697.
35. "Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification, " Human Movement Science, vol. 40, pp. 59-76, 2015.
36. A. M. Tripathi, D. Baruah, and R. D. Baruah, "Acoustic sensor based activity recognition using ensemble of one-class classifiers, " in Evolving and Adaptive Intelligent Systems (EAIS), 2015 IEEE International Conference on, Dec 2015, pp. 1-7.
37. "On the use of ensemble of classifiers for accelerometer-based activity recognition, " Applied Soft Computing, vol. 37, pp. 1018-1022, 2015.
38. R. Akhavian and A. Behzadan, "Wearable sensor-based activity recognition for data-driven simulation of construction workers activities, " in 2015 Winter Simulation Conference (WSC), Dec 2015, pp. 3333-3344.
39. "Sensor-based human activity recognition system with a multilayered model using time series shapelets, " Knowledge-Based Systems, vol. 90, pp. 138-152, 2015.
40. "Dynamic sensor data segmentation for real-Time knowledge-driven activity recognition, " Pervasive and Mobile Computing, vol. 10, Part B, pp. 155-172, 2014.
41. S. C. Mukhopadhyay, "Wearable sensors for human activity monitoring: A review, " IEEE Sensors Journal, vol. 15, no. 3, pp. 1321-1330, March 2015.
42. L. M. Zhou, C. Gurrin, and Z. Qiu, "Zhiwo: Activity tagging and recognition system for personal lifelogs, " in Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, ser. ICMR 13, 2013, pp. 321-322.
43. C. Meurisch, B. Schmidt, M. Scholz, I. Schweizer, and M. Mühlhäuser, "Labels: Quantified self app for human activity sensing, " in Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, ser. UbiComp/ISWC15 Adjunct, 2015, pp. 1413-1422.
44. J. Hamm, B. Stone, M. Belkin, and S. Dennis, Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 328-342.
45. C. Dobbins and R. Rawassizadeh, "Clustering of physical activities for quantified self and mhealth applications, " in Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on, Oct 2015, pp. 1423-1428.
46. M. Uddin, A. Salem, I. Nam, and T. Nadeem, "Wearable sensing framework for human activity monitoring, " in Proceedings of the 2015 Workshop on Wearable Systems and Applications, ser. WearSys 15, 2015, pp. 21-26.
47. O. Banos, J. Bang, T. Hur, M. H. Siddiqi, H. T. Thien, L. B. Vui, W. A. Khan, T. Ali, C. Villalonga, and S. Lee, "Mining human behavior for health promotion, " in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug 2015, pp. 5062-5065.
48. P. S. Freedson, E. Melanson, and J. Sirard, "Calibration of the computer science and applications inc. accelerometer, " Medicine and Science in Sports and Exercise, vol. 30, no. 5, pp. 777-781, 1998.
49. T. W. Strine and D. P. Chapman, "Associations of frequent sleep insufficiency with health-related quality of life and health behaviors, " Sleep medicine, vol. 6, no. 1, pp. 23-27, 2005.
50. T. Arora, E. Broglia, D. Pushpakumar, T. Lodhi, and S. Taheri, "An investigation into the strength of the association and agreement levels between subjective and objective sleep duration in adolescents, " PloS one, vol. 8, no. 8, p. e72406, 2013.
51. C. Iber, S. Ancoli-Israel, A. Chesson, and S. Quan, The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, 2007.
52. A. Sadeh, A. Raviv, and R. Gruber, "Sleep patterns and sleep disruptions in school-Age children, " Developmental psychology, vol. 36, no. 3, p. 291, 2000.
53. J. T. Cacioppo, L. C. Hawkley, G. G. Berntson, J. M. Ernst, A. C. Gibbs, R. Stickgold, and J. A. Hobson, "Do lonely days invade the nights? potential social modulation of sleep efficiency, " vol. 13, no. 4, pp. 384-387, 2002. [Online]. Available: http://pss.sagepub.com/content/13/4/384.abstract
54. "Normative values of polysomnographic parameters in childhood and adolescence: Quantitative sleep parameters, " Sleep Medicine, vol. 12, no. 6, pp. 542-549, 2011.
55. K. P. Gabriel, J. J. McClain, K. K. Schmid, K. L. Storti, R. R. High, D. A. Underwood, L. H. Kuller, and A. M. Kriska, "Issues in accelerometer methodology: The role of epoch length on estimates of physical activity and relationships with health outcomes in overweight, post-menopausal women, " International Journal of Behavioral Nutrition and Physical Activity, vol. 7, no. 1, pp. 1-10, 2010.
56. R. P. Troiano, D. Berrigan, K. W. Dodd, L. C. Masse, T. Tilert, M. McDowell et al., "Physical activity in the united states measured by accelerometer, " Medicine and science in sports and exercise, vol. 40, no. 1, pp. 181-188, 2008.
57. N. James and D. Matteson, "ecp: An r package for nonparametric multiple change point analysis of multivariate data, " Journal of Statistical Software, vol. 62, no. 1, pp. 1-25, 2015.
58. R. Williams, I. Karacan, and C. Hursch, Electroencephalography (Eeg) of Human Sleep: Clinical Applications, ser. A Wiley biomedical-health publication.