Human activity recognition: A review

Proceedings - 4th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2014

Volumn , Issue , 2014, Pages 389-393

  Ann, Ong Chin ^a   Theng, Lau Bee ^a  

^a SWINBURNE UNIVERSITY OF TECHNOLOGY   (Malaysia)

Author keywords

depth sensor; Human activity recognition; Kinect; RGB camera; sensing technology; wearable devices

Indexed keywords

CAMERAS; COMPUTER VISION; MEDICAL COMPUTING; PATTERN RECOGNITION; WEARABLE SENSORS; WEARABLE TECHNOLOGY;

DEPTH SENSORS; HUMAN ACTIVITY RECOGNITION; KINECT; RGB CAMERAS; SENSING TECHNOLOGY; WEARABLE DEVICES;

HUMAN COMPUTER INTERACTION;

EID: 84946686970     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCSCE.2014.7072750     Document Type: Conference Paper

References (50)

1. A. Iosifidis, A. Tefas, and I. Pitas, "Multi-view action rec ognition based on action volumes, fuzzy distances and cluster discriminant analysis," Signal Processing, vol. 93, no. 6, pp. 1445-1457, Jun. 2013.
2. D. Weinland,R. Ronfard,E. Boyer A surve y of vision-based methods for action representation, segmentation and recognition," Comput. Vis. Image Underst. 115 2 224-241, Feb. 2011.
3. S. Ali and M. Shah, "Human action recognitio n in videos using kinematic features and multiple instance learning.," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 2, pp. 288-303, Feb. 2010.
4. A. Oikonomopoulos and M. Pantic, "Human A ctivity Recognition Using Hierarchically-Mined Feature Constellations," pp. 150-159, 2013.
5. M. Javan Roshtkhari and M. D. Levine, "Human activity recognition in videos using a single example, " Image Vis. Comput., vol. 31, no. 11, pp. 864-876, Nov. 2013.
6. J. Yang, J. Lee, and J. Choi, "Activity Recognition Based on RFID Object Usage for Smart Mobile Devices, " J. Comput. Sci. Techn ol., vol. 26, no. 2, pp. 239-246, Mar. 2011.
7. L. Chen, H. Wei, and J. Ferryman, "A survey of human motion analysis using depth imagery, " Pattern Recognit. Lett., vol. 34, no. 15, pp. 1995-2006, Nov. 2013.
8. W. Ong, L. Palafox, and T. Koseki, "Investigation of Feature Extraction for Unsupervised Learning in Human Activity Detection, " Bull. Networking, Comput. Syst. Softw., vol. 2, no. 1, pp. 30-35, 2013.
9. O. D. Lara and M. a. Labrador, " A Survey on Human Activity Recognition using Wearable Sensors," IEEE Commun. Surv. Tutorials, vol. 15, no. 3, pp. 1192-1209, Jan. 2013.
10. A. A. Chaaraoui, J. R. Padilla-López, P. Climent-Pérez, and F. Flórez-Revuelta, " Evolutionary joint selection to improve human action recognition with RGB-D devices," Expert Syst. Appl., vol. 41, no. 3, pp. 786-794, Feb. 2014.
11. N. Noorit and N. Suvonvorn, "Human Activity Recognition from Basic Actions Using Finite State Machine, " in Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), 2014, vol. 285, pp. 379-386.
12. M. S. Ryoo, " Human activity prediction: Early recognition of ongoing activities from streaming videos," in 2011 International Conference on Computer Vision, 2011, no. Iccv, pp. 1036-1043.
13. J. Preis, M. Kessel, M. Werner, and C. Linnhoff-Popien, "Gait Recognition with Kinect, " in Workshop on Kinect in Pervasive Computing at Pervasive 2012, 2012.
14. V. Tamas, "Human Behavior Recognition In Video Sequences, " Technical University of CLUJ-NAPOCA, 2013.
15. W. S. Lasecki, Y. C. Song, H. Kautz, and J. P. Bigham, " Real-time crowd labeling for deployable activity recognition," in Proceedings of the 2013 conference on Computer supported cooperative work-CSCW '13, 2013, p. 1203.
16. O. Banos, M. Damas, H. Pomares, A. Prieto, and I. Rojas, " Daily living activity recognition based on statistical feature quality group selection," Expert Syst. Appl., vol. 39, no. 9, pp. 8013-8021, Jul. 2012.
17. L. Chen, C. D. Nugent, and H. Wang, "A Knowledge-Driven Approach to Activity Recognition in Smart Homes, " IEEE Trans. Kn owl. Data Eng., vol. 24, no. 6, pp. 961-974, Jun. 2012.
18. A. Jalal, Z. Uddin, J. T. Kim, and T. Kim, "Recognition of Human Home Activities via Depth Silhouettes and a Transformation for Smart Homes," no. February, pp. 467-475, 2011.
19. Y.-J. Chang, S.-F. Chen, and J.-D. Huang, " A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities.," Res. Dev. Disabil., vol. 32, no. 6, pp. 2566-2570, 2011.
20. A. Hayes, P. Dukes, and L. F. Hodges, "A Virtual Environment for Post-Stroke Motor Rehabilitation, " South Carolina, 2011.
21. D. González-Ortega, F. J. Díaz-Pernas, M. Martínez-Zarzuela, and M. Antón-Rodríguez, " A Kinect-based system for cognitive rehabilitation exercises monitoring., " Comput. Methods Programs Biomed., vol. 113, no. 2, pp. 620-31, Feb. 2014.
22. N. Alshurafa, W. Xu, J. Liu, M.-C. Huang, B. Mortazavi, C. Roberts, and M. Sarrafzadeh, " Designing a Robust Activity Recognition Framework for Health and Exergaming using Wearable Sensors., " IEEE J. Biomed. Heal. informatics, no. c, pp. 1-11, Oct. 2013.
23. E. S. Sazonov, G. Fulk, J. Hill, Y. Schutz, and R. Browning, " Monitoring of posture allocations and activities by a shoebased wearable sensor.," IEEE Trans. Biomed. Eng., vol. 58, no. 4, pp. 983-90, Apr. 2011.
24. A. M. Khan, "Human Activity Recognition Using A Single Tri-axial Accelerometer, " Kyung Hee University, Seoul, Korea, 2011.
25. G. Paragliola and A. Coronato, "Intelligent Monitoring of Stereotyped Motion Disorders in Case of Children with Autism, " 2013 9th Int. Conf. Intell. Environ., pp. 258-261, Jul. 2013.
26. E. Kantoch and P. Augustyniak, "Human activity surveillance based on wearable body sensor network, " in Computing in Cardiology (CinC), 2012, pp. 325-328.
27. E. Stone and M. Skubic, "Passive, In-Home Gait Measurement Using an Inexpensive Depth Camera: Initial Results, " in Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare, 2012, pp. 183-186.
28. A. T. Nghiem, E. Auvinet, and J. Meunier, "Head detection using Kinect camera and its application to fall detection, " in 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012, pp. 164-169.
29. Q. V. Vo, G. Lee, and D. Choi, "Fall Detection Based on Movement and Smart Phone Technology, " in 2012 IEEE RIVF International Conferen ce on Computing & Communication Technologies, Research, Innovation, and Vision for the Future, 2012, pp. 1-4.
30. S. Wang, S. Zabir, and B. Leibe, "Lying Pose Recognition for Elderly Fall Detection, " in Robotics:Science and Systems VII, no. 1, H. Durrant-Whyte, N. Roy, and P. Abbeel, Eds. MIT Press, 2012, pp. 345-353.
31. J. Han, L. Shao, D. Xu, and J. Shotton, "Enhanced computer vision w ith Microsoft Kinect sensor: a review.," IEEE Trans. Cybern., vol. 43, no. 5, pp. 1318-34, Oct. 2013.
32. J. Smisek, M. Jancosek, and T. Pajdla, "3D with Kinect, " in 2011 IEEE I nternational Conference on Computer Vision Workshops (ICCV Workshops), 2011, pp. 1154-1160.
33. Z. Zhang, "Microsoft Kinect Sensor and Its Effect, " IEEE Multimed., vol. 19, no. 2, pp. 4-10 , Feb. 2012.
34. D. T. G. Huynh, "Human Activity Recognition with Wearable Sensors, " Technische Universitä t Darmstadt, 2008.
35. E. Lawrence, C. Sax, K. F. Navarro, and M. Qiao, " Interactive Games to Improve Quality of Life for the Elderly: Towards Integration into a WSN Monitoring System," in 2010 Second International Conference on eHealth, Telemedicine, and Social Medicine, 2010, pp. 106-112.
36. K. Gerling, I. Livingston, L. Nacke, and R. Mandryk, " Fullbody motion-based game interaction for older adults," in Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems-CHI '12, 2012, p. 1873.
37. B. Lange, C.-Y. Chang, E. Suma, B. Newman, A. S. Rizzo, and M. Bolas, " Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor.," Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2011, pp. 1831-4, Jan. 2011.
38. K. Yoshimitsu, Y. Muragaki, T. Maruyama, M. Yamato, and H. Iseki, " Development and Initial Clinical Testing of 'OPECT': An Innovative Device for Fully Intangible Control of the Intraoperative Image-Displaying Monitor by the Surgeon, " Neurosurgery, vol. 10, 2014.
39. L. Xia, C. Chen, and J. K. Aggarwal, "View inva riant human action recognition using histograms of 3D joints," in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012, pp. 20-27.
40. S. M. Amiri, M. T. Pou razad, P. Nasiopoulos, and V. C. M. Leung, " Human action recognition using meta learning for RGB and depth information," 2014 Int. Conf. Comput. Netw. Commun., pp. 363-367, Feb. 2014.
41. E. Frontoni, A. Mancini, and P. Zingaretti, "RGBD Sensors for Human Activity Detection in AAL Environments, " in Ambient Assisted Living, S. Longhi, P. Siciliano, M. Germani, and A. Monteriu, Eds. Cham: Springer International Publishing, 2014, pp. 127-135.
42. X. Yang and Y. Tian, "Effective 3D action recognition using EigenJoints, " J. Vis. Commun. Image Represent., vol. 25, no. 1, pp. 2-11, Ja n. 2014.
43. Y. E. Ustev, O. Durmaz Incel, and C. Ersoy, " User, device and orientation independent human activity recognition on mobile phones," in Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication-UbiComp '13 Adjunct, 2013, pp. 1427-1436.
44. M. Zhang and A. A. Sawchuk, "Motion primitive-based human activity recognition using a bag-of-features approach, " in Proceed ings of the 2nd ACM SIGHIT symposium on International health informatics-IHI '12, 2012, no. 1, p. 631.
45. A. Reiss,G. Hendeby,D. Stricker A Competitive Approach for Human Activity Recognition on Smartphones," in 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2013, no. Esann 455-460.
46. M. Kreil, B. Sick, and P. Lukowicz, " Dealing with human variability in motion based, wearable activity recognition," in 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), 2014, pp. 36-40.
47. Z. He and X. Bai, "A wearable wireless body area network for human activity recognition, " in 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN), 2014, pp. 115-119.
48. B. M irmahboub, S. Samavi, N. Karimi, and S. Shirani, " Automatic Monocular System for Human Fall Detection based on Variations in Silhouette Area.," IEEE Trans. Biomed. Eng., no. c, pp. 1-10, Nov. 2012.
49. K. Khoshelham, "Accuracy Analysis of Kinect Depth Data, " in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011, vol. XXXVIII, no. August, pp. 133-138.
50. T. Mashita, K. Shimatani, M. Iwata, H. Miyamoto, D. Komaki, T. Hara, K. Kiyokawa, H. Takemura, and S. Nishio, " Human activity recognition for a content search system considering situations of smartphone users, " 2012 IEEE Virtual Real., pp. 1-2, Mar. 2012.