Video based technology for ambient assisted living: A review of the literature

Journal of Ambient Intelligence and Smart Environments

Volumn 3, Issue 3, 2011, Pages 253-269

  Cardinaux, Fabien ^a   Bhowmik, Deepayan ^b   Abhayaratne, Charith ^b   Hawley, Mark S ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Assisted living; computer vision; video monitoring

Indexed keywords

AMBIENT ASSISTED LIVING; ASSISTED LIVING; COMPUTER VISION SYSTEM; LITERATURE REVIEWS; OLDER PEOPLE; QUALITY OF LIFE; TECHNICAL SOLUTIONS; VIDEO MONITORING;

COMPUTER VISION;

VIDEO CAMERAS;

EID: 79960814907     PISSN: 18761364     EISSN: None     Source Type: Journal    
DOI: 10.3233/AIS-2011-0110     Document Type: Review

References (72)

1. AALIANCE, Ambient assisted living roadmap, 2010, http://www.aaliance.eu/ public/documents/aaliance-roadmap.
2. J.K. Aggarwal and S. Park, Human motion: Modeling and recognition of actions and interactions, in: Proc. 3D Data Processing, Visualization and Transmission 3DPVT, 2004, pp. 640-647. (Pubitemid 40456354)
3. M. Alwan, P.J. Rajendran, S. Kell, D. Mack, S. Dalal, M. Wolfe, and R. Felder, A smart and passive floor-vibration based fall detector for elderly, in: Proc. Information and Communication Technologies (ICTTA), Vol. 1, 2006, pp. 1003-1007.
4. E. Broadbent, R. Stafford, and B. MacDonald, Acceptance of healthcare robots for the older population: Review and future directions, International Journal of Social Robotics, 1 (2009), 319-330.
5. J. Broekens, M. Heerink, and H. Rosendal, Assistive social robots in elderly care: A review, Gerontechnology, 8 (2009), 94-103.
6. S. Brownsell, S. Blackburn, and M.S. Hawley, An evaluation of second and third generation telecare services in older people's housing, Journal of Telemedicine and Telecare, 1 (2008), 8-12.
7. G. Cambul, M.R. Sahin, andM.E. Karshgil, Automatic fall detection in real time video based applications, in: Proc. IEEE Signal Processing and Communications Applications, 2006, pp. 1-4.
8. D. Chen, A.J. Bharucha, and H.D. Wactlar, Intelligent video monitoring to improve safety of older persons, in: Proc. IEEE International Conference on Engineering in Medicine and Biology Society (EMBS), 2007, pp. 3814-3817.
9. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, Detecting moving objects, ghosts, and shadows in video streams, IEEE Transaction on Pattern Analysis and Machine Intelligence, 25(10) (Oct. 2003), 1337-1342.
10. D.F. DeMenthon and L.S. Davis, Model-based object pose in 25 lines of code, International Journal of Computer Vision, 15 (1995), 123-141.
11. G. Demiris, S. Speedie, and S. Finkelstein, A questionnaire for the assessment of patients' impressions of the risks and benefits of home telecare, Journal of Telemedicine and Telecare, 6 (2000), 278-284.
12. F. Dufaux and T. Ebrahimi, Scrambling for video surveillance with privacy, in: Proc. 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2006, p. 160.
13. J.P. Egan, Signal detection theory and ROC analysis, Series in Cognition and Perception, 1975.
14. A.M. Elgammal, D. Harwood, and L.S. Davis, Non-parametric model for background subtraction, in: Proc. 6th European Conference on Computer Vision-Part II (ECCV), 2000, pp. 751-767.
15. H.-L. Eng, J. Wang, A.H. Kam, and W.-Y. Yau, A bayesian framework for robust human detection and occlusion handling human shape model, in: Proc. of the 17th International Conference on Pattern Recognition (ICPR 2004), Vol. 2, 2004, pp. 257-260, doi: 10.1109/ICPR.2004.1334150.
16. J. Fei and I. Pavlidis, Thermistor at a distance: Unobtrusive measurement of breathing, IEEE Transactions on Biomedical Engineering, 57(4) (Apr. 2010), 988-998.
17. S. Fleck and W. Strasser, Smart camera based monitoring system and its application to assisted living, Proc. of the IEEE, 96(10) (Oct. 2008), 1698-1714.
18. H. Foroughi, A. Naseri, A. Saberi, and H.S. Yazdi, An eigenspace-based approach for human fall detection using integrated time motion image and neural network, in: Proc. International Conference on Signal Processing (ICSP), 2008, pp. 1499-1503.
19. J.A. Freer, B.J. Beggs, H.L. Fernandez-Canque, F. Chevrier, and A. Goryashko, Automatic intruder detection incorporating intelligent scene monitoring with video surveillance, in: Proc. European Conference on Security and Detection (ECOS), 1997, pp. 109-113.
20. M. Fujita, AIBO: Toward the era of digital creatures, International Journal on Robotic Research, 20(10) (2001), 781-794. (Pubitemid 33741144)
21. M. Garbey, S. Nanfei, A. Merla, and I. Pavlidis, Contact-free measurement of cardiac pulse based on the analysis of thermal imagery, IEEE Transactions on Biomedical Engineering, 54(8) (Aug. 2007), 1418-1426. (Pubitemid 47076250)
22. R. Gross, L. Sweeney, F.D.T. Frade, and S. Baker, Model-based face de-identification, in: Proc. 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2006, pp. 161-168.
23. S. Hongeng, R. Nevatia, and F. Bremond, Video-based event recognition: Activity representation and probabilistic recognition methods, Computer Vision and Image Understanding, 96(2) (2004), 129-162.
24. M. Isard and A. Blake, CONDENSATION-conditional density propagation for visual tracking, International Journal of Computer Vision, 29 (1998), 5-28. (Pubitemid 128523364)
25. Y.A. Ivanov and A.F. Bobick, Recognition of visual activities and interactions by stochastic parsing, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8) (Aug. 2000), 852-872.
26. B. Jansen and R. Deklerck, Context aware inactivity recognition for visual fall detection, in: Proc. Pervasive Health Conference and Workshops, 2006, pp. 1-4.
27. K. Kim, T.H. Chalidabhongse, D. Harwood, and L. Davis, Real-time foreground-background segmentation using codebook model, Real-Time Imaging, 11(3) (2005), 172-185, Special issue on video object processing. (Pubitemid 40929458)
28. S. Kim, T. Schap, M. Bosch, R. Maciejewski, E. Delp, D. Ebert, and C. Boushey, Development of a mobile user interface for image-based dietary assessment, in: Proc. 9th International Conference on Mobile and Ubiquitous Multimedia, 2010.
29. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3) (Mar. 1998), 226-239. (Pubitemid 128741312)
30. T. Ko, A survey on behavior analysis in video surveillance for homeland security applications, in: Proc. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2008, pp. 1-8.
31. B. Laxton, L. Jongwoo, and D. Kriegman, Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video, in: Proc. IEEE Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1-8.
32. T. Lee and A. Mihailidis, An intelligent emergency response system: Preliminary development and testing of automated fall detection, Journal of Telemedicine and Telecare, 11(4) (2005), 194-198. (Pubitemid 40848126)
33. C.W. Lin and Z.H. Ling, Automatic fall incident detection in compressed video for intelligent homecare, in: Proc. International Conference on Computer Communications and Networks (ICCCN), 2007, pp. 1172-1177.
34. D. Litvak, I. Gannot, and Y.Y. Zigel, Detection of falls at home using floor vibrations and sound, in: Proc. IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI), 2008, pp. 514-518.
35. S.T. Londei, J. Rousseau, F. Ducharme, A. St-Arnaud, J. Meunier, J. Saint-Arnaud, and F. Giroux, An intelligent videomonitoring system for fall detection at home: Perceptions of elderly people, Journal of Telemed Telecare, 15(8) (2009), 383-390.
36. F. Marquis-Faulkes, S.J. McKenna, A.F. Newell, and P. Gregor, Gathering the requirements for a fall monitor using drama and video with older people, Journal Technology and Disability, 17(4) (2005), 227-236. (Pubitemid 43091476)
37. M. Mataric, J. Eriksson, D. Feil-Seifer, and C. Winstein, Socially assistive robotics for post-stroke rehabilitation, Journal of NeuroEngineering and Rehabilitation, 4(1) (2007), pp. 5.
38. A. Mihailidis, T. Tam, M. McLean, and T. Lee, An intelligent health monitoring and emergency response system, in: Proc. International Conference on Smart Homes and Health Telematics (ICOST), 2005, pp. 272-281.
39. T.B. Moeslund, A. Hilton, and V. Krüger, A survey of advances in vision-based human motion capture and analysis, Computer Vision and Image Understanding, 104 (Nov. 2006), 90-126. (Pubitemid 44573873)
40. H. Nait-charif and S.J. McKenna, Tracking poorly modelled motion using particle filters with iterated likelihood weighting, in: Proc. Asian conference on computer vision (ACCV), 2004, pp. 156-161.
41. H. Nait-Charif and S.J. McKenna, Activity summarisation and fall detection in a supportive home environment, in: Proc. IEEE International Conference on Pattern Recognition (ICPR), Vol. 4, 2004, pp. 323-326.
42. N. Nasr, S. Torsi, S.Mawson, P. Wright, and G. Mountain, Self management of stroke supported by assistive technology, in: Proc. Virtual Rehabilitation International Conference, 2009, p. 193.
43. A.H. Nasution and S. Emmanuel, Intelligent video surveillance for monitoring elderly in home environments, in: Proc. IEEE Workshop on Multimedia Signal Processing (MMSP), 2007, pp. 203-206.
44. J.W.P. Ng, B.P.L. Lo, O.Wells,M. Sloman, N. Peters, A. Darzi, C. Toumazou, and G. Yang, Ubiquitous monitoring environment for wearable and implantable sensors (ubimon), in: Proc. 6th International Conference on Ubiquitous Computing (UBICOMP '04), 2004.
45. The Royal Academy of Engineering, Dilemmas of Privacy and Surveillance: Challenges of Technological Change, March 2007, http://www.raeng.org.uk/ societygov/policy/current-issues/privacy-surveillance/pdf/dilemmas-of-privacy- and-surveillance-report.pdf.
46. Department of Health Publication, Efficiencies in Telecare, Feb. 2010, http://www.csed.dh.gov.uk/-library/Resources/CSED/CSEDProduct/ Telecare-North-Yorks-Case-Study-webversionvs-1.pdf.
47. E.J. Pauwels, A.A. Salah, and R. Tavenard, Sensor networks for ambient intelligence, in: Proc. IEEE Workshop on Multimedia Signal Processing (MMSP), 2007, pp. 13-16.
48. M.Z. Poh, D.J. McDuff, and R.W. Picard, Non-contact, automated cardiac pulse measurements using video imaging and blind source separation, Opt. Express, 18(10) (May 2010), 10762-10774.
49. GER'HOME Project, http://gerhome.cstb.fr/.
50. HERMES Project, http://www.fp7-hermes.eu/.
51. C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, Monocular 3d head tracking to detect falls of elderly people, in: Proc. IEEE International Conference on Engineering in Medicine and Biology Society (EMBS), 2006, pp. 6384-6387. (Pubitemid 46521382)
52. C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, Fall detection from human shape and motion history using video surveillance, in: Proc. International Conference on Advanced Information Networking and Applications Workshops (AINAW), Vol. 2, 2007, pp. 875-880. (Pubitemid 47565316)
53. Link Care Services, http://www.linkcareservices.com/lcs/LCS\- Presentation\-Guide.pdf.
54. C. Stauffer and W.E.L. Grimson, Learning patterns of activity using real-time tracking, IEEE Transaction on Pattern Analysis and Machine Intelligence, 22 (Aug. 2000), 747-757, ISSN 0162-8828.
55. H. Sun, V. De Florio, N. Gui, and C. Blondia, Promises and challenges of ambient assisted living systems, in: Proc. International Conference on Information Technology: New Generations (ITNG), 2009, pp. 1201-1207.
56. C. Takano and Y. Ohta, Heart rate measurement based on a time-lapse image, Medical Engineering and Physics, 29(8) (2007), 853-857. (Pubitemid 46898901)
57. J. Tao, T. Mukherjee, M.F. Wong, M. Wang, and Y.P. Tan, Fall incidents detection for intelligent video surveillance, in: Proc. International Conference on Information, Communications and Signal Processing, 2005, pp. 1590-1594. (Pubitemid 46562301)
58. J. Tao, M. Turjo, and Y.P. Tan, Quickest change detection for health-care video surveillance, in: Proc. IEEE International Symposium on Circuits and Systems (ISCAS), 2006, p. 4.
59. E.M. Tapia, S.S. Intille, and K. Larson, Activity recognition in the home using simple and ubiquitous sensors, Pervasive Computing, 3001 (2004), 158-175.
60. N. Thome and S. Miguet, A HHMM-based approach for robust fall detection, in: Proc. International Conference on Control, Automation, Robotics and Vision (ICARCV), 2006, pp. 1-8.
61. N. Thome, S. Miguet, and S. Ambellouis, A real-time, multiview fall detection system: A LHMM-based approach, IEEE Transactions on Circuits and Systems for Video Technology, 18(11) (Nov. 2008), 1522-1532, ISSN 1051-8215.
62. B.U. Töreyin, Y. Dedeoglu, and A.E. Çetin, HMM based falling person detection using both audio and video, Computer Vision in Human-Computer Interaction 3766 (2005), 211-220.
63. B.U. Töreyin, E. Birey Soyer, Ibrahim Onaran, and A.E. Çetin, Falling person detection using multi-sensor signal processing, EURASIP Journal on Advances in Signal Processing, 2008.
64. P. Turaga, R. Chellappa, V.S. Subrahmanian, and O. Udrea, Machine recognition of human activities: A survey, IEEE Transactions on Circuits and Systems for Video Technology, 18(11) (Nov. 2008), 1473-1488.
65. L. Wang and D. Suter, Learning and matching of dynamic shape manifolds for human action recognition, IEEE Transactions on Image Processing, 16(6) (June 2007), 1646-1661. (Pubitemid 46796060)
66. M.L. Wang, C.C. Huang, and H.Y. Lin, An intelligent surveillance system based on an omnidirectional vision sensor, in: Proc. IEEE Cybernetics and Intelligent Systems, 2006, pp. 1-6.
67. J.A. Ward, P. Lukowicz, and H.W. Gellersen, Performance metrics for activity recognition, ACM Trans. Intell. Syst. Technol., 2 (Jan. 2011), 6:1-6:23.
68. A. Warren, R. Bayley, C. Bennett, A.J. Charlesworth, R.A. Clarke, and C. Oppenheim, Privacy impact assessments: The UK experience, in: Proc. 31st International Conference of Data Protection and Privacy Commissioners, SSRN, 2009.
69. T. Winkler and B. Rinner, TrustCAM: Security and privacyprotection for an embedded smart camera based on trusted computing, in: Proc. IEEE Conference on Advanced Video and Signal Based Surveillance, 2010, pp. 593-600.
70. Z.Y. Wu, Y.C. Lee, F. Lai, H.C. Lee, and Y. Chung, A secure authentication scheme for telecare medicine information systems, Journal of Medical Systems (2010), 1-7.
71. G.Z. Yang, B. Lo, L. Wang, M. Rans, S. Thiemjarus, J. Ng, P. Garner, S. Brown, B. Majeed, and I. Neid, From sensor networks to behaviour profiling: A homecare perspective of intelligent building, in: Proc. IEE Seminar for Intelligent Buildings, 2004.
72. Y. Zigel, D. Litvak, and I. Gannot, A method for automatic fall detection of elderly people using floor vibrations and sound-proof of concept on human mimicking doll falls, IEEE Transactions on Biomedical Engineering, 56(12) (Dec. 2009), 2858-2867.