Approaches and principles of fall detection for elderly and patient

2008 10th IEEE Intl. Conf. on e-Health Networking, Applications and Service, HEALTHCOM 2008

Volumn , Issue , 2008, Pages 42-47

  Yu, Xinguo ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Algorithm; Approach; Camera; Device; Elderly; Fall detection; Healthcare; Patient; Principle; Sensor; System

Indexed keywords

SENSORS;

ALGORITHM; APPROACH; CAMERA; CLASSIFICATION TREES; DEVICE; E-HEALTH; ELDERLY; FALL DETECTION; HEALTHCARE; HEALTHCARE INDUSTRIES; PATIENT; PRINCIPLE; SENSOR; SYSTEM;

HEALTH;

EID: 52949149759     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HEALTH.2008.4600107     Document Type: Conference Paper

References (80)

1. O. Almeida, M. Zhang, and L. C. Liu. Dynamic fall detection and pace measurement in walking sticks, manuscript, 2007.
2. nd Info. & Com. Tech.), vol. 1, pp.1003-1007, 24-28 April 2006.
3. th Int'l Conf. of the IEEE Eng. in Medicine and Biology Society), pp. 6388-6391, Aug. 2006.
4. A K Bourke, and O. M. Lyons. A threshold-based detection algorithm using a bi-axial gyroscope sensor, Med. Eng. Phys., 2006.
5. A. K. Bourke, V. J O'Brien J and GM Lyons. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm, Gait Posture, 2006.
6. G Brown. An accelerometer based fall detector: Development, experimentation, and analysis, Internal Report, University of California in Berkeley, 2005.
7. th Signal Processing and Communications Applications, pp.1- 4, April 17-19, 2006.
8. K. Cameron, K. Hughes, and K. Doughty. Reducing fall incidence in community elders by telecare using predictive systems, 19th EMBC, vol.3, pp.1036-1039, 30 Oct-2 Nov 1997.
9. th EMBC, pp. 3551-3554, 2005.
10. R. Cucchiara, A. Prati, and R. Vezzani. A multi-camera vision system for fall detection and alarm generation, Expert Systems Journal, vol. 24(5), pp. 334-345, 2007.
11. M. A. Clifford, R. L. Borras, and L. Gomez. System and method for human body fall detection, US Patent 7248172, 07/24/2007.
12. Y. Depeursinge. Device for monitoring the activity of a person and/or detecting a fall, US patent 6,201,476 B1, 13 March 2001.
13. A. Diaz, M. Prado, L. M. Roa, J. Reina-Tosina, and G. Sanchez. Preliminary evaluation of a full-time falling monitor for the elderly, 26th EMBC, San Francisco, CA, USA. Sept. 1-5, 2004.
14. K. Doughty, R. Lewis and A. McIntosh. The design of a practical and reliable fall detector for community and institutional telecare, J. Telemed Telecare, 6 Sup 1 pp: S150-4, 2000.
15. C. Doukas, I. Maglogiannis, P.Tragas, D. Liapis, and G. Yovanof. Patient fell detection using support vector machines, Int'l Federation for Information Processing, vol 247/2007, pp. 147-156.
16. V. F. S. Fook, P. V. Thang, T. M. Htwe, Q. Qiang, A. A. P. Wai. Automated recognition of complex agitation behavior of dementia patients using video camera, 9th Int'l Conf. on e-Health Networking, Application and Services, pp.68-73, 19-22 June 2007.
17. E. E. Freeman, B. Munoz, G. Rubin, and S. K. West. Visual field loss increases the risk of fells in older adults: the Salisbury eye evaluation, IOVS 48: 4445-4450, 2007.
18. M. J. Gibson, et al. The prevention of falls in later life. A report of the Kellogg Int'l Work Group on the Prevention of Falls by the Elderly, Danish Medical Bulletin, 34 Supplement 4:1-24, 1987.
19. M. Gostynski. Prevalence, circumstances and consequences of falls in institutionalized elderly: a pilot study, Soz Praventivmed, Vol.36(6):341-5, 1991.
20. R. J. Gurley, N Lum, M Sande, B Lo, and MH Katz. Persons found in their homes helpless or dead, New Eng J Med.; 334(26): 1710-6, 1996.
21. T. R Hansen, J. M Eklund, J. Sprinkle, and R Bajcsy Using smart sensors and a camera phone to detect and verify the fall of elderly persons European Medicine, Biology and Eng. Conf., vol. 3, 2005.
22. Y. T. Hsu, J. W. Hsieh, H. F. Kao, and H. Y. M. Liao. Human behavior analysis using deformable triangulations, IEEE 7th Workshop on Multimedia Signal Processing, Oct. 2005, pp. 1-4.
23. th EMBC, San Francisco, CA, USA, Sept. 1-5, 2004
24. B. Jansen and R. Deklerck. Context aware inactivity recognition for visual fell detection, Pervasive Health Conference and Workshops, pp.1-4, Nov. 29-Dec. 1, 2006.
25. A. H. Khandoker, D. Lai, R.K. Begg, and M.Palaniswaml. A wavelet-based approach for screening falls risk in the elderly using support vector machines, ICISIP 2006 (4th Int'l Conf. Intelligent Sensing & Inf. Processing), pp:184-189, Oct. 15 2006-Dec. 18 2006.
26. J. Krauss, and J. Zhao. Activity sensor and fall detection, manuscript, 2008.
27. th EMBC, pp. 2315-2318, 22-26 Aug. 2007.
28. T. Lee, and A. Mihailidis, An intelligent emergency response system.: preliminary development and testing of automated fall detection. J. of Telemedicine and Telecare, 11, 194-198, 2005.
29. L. J. Lin, F. T. Chiou and H. H. Cohen. Slip and fell accident prevention: A review of research, practice, and regulations, Journal of Safety Research, vol.26(4), pp.203-212.
30. C. S. Lin, H. C. Hsu, Y. L. Lay, C. C. Chiu and C. S. Chao, Wearable device for real-time monitoring of human falls, Measurement, vol. 40( 9-10), pp. 831-840, Nov.-Dec. 2007.
31. C. S. Lin, H. C. Hsu, C. C. Chiu, S. L. Lin, and C. S. Chao, A PDA based wearable system for real-time monitoring of human falls, IETE Journal of Research, Vol 52 (6), 403-416, 2006.
32. C. W. Lin, and Z. H. Ling, Automatic fall incident detection in compressed video for intelligent homecare, ICCCN 2007 ( Proc. of 16th Int'l Conf. on Com. Comm. & Networks), pp.1172-1177, Aug. 2007.
33. C. W. Lin, Z H. Ling, Y. C. Chang, and. C. J. Kuo, Compressed-domain fall incident detection for intelligent home surveillance, ISCAS (IEEE Int'l Symp. Circ. A Sys.), vol. 4, pp.3781-84, 23-26 May 2005.
34. U. Lindemann. Evaluation of a fall detector based on accelerometersa pilot study, Med Biol Eng Comput, 43(5):548-51, sep 2005.
35. T. E. Lockhart. Fall-sensing systems, hip protector systems, and protective systems, US Patent, US2006/0049950 A1, 2006.
36. C. J. Lord, and D. P. Colvin, Falls in the elderly: Detection and assessment, 13rd EMBC, 1991.
37. S. R. Lord, C. Sherrington, H. B. Menz, and J. C. T. Close. Falls in older people: risk factors and strategies for prevention, Cambridge University Press, 2nd edition, March 19, 2007.
38. S. Luo, and Q. Hu. A dynamic motion pattern analysis approach to fall detection, ISCAS 2004, Vol. 1, pp:5-8, 1-3 Dec. 2004.
39. M. J. Mathie, J. Basilakis, and B. G. Celler. A system for monitoring posture and physical activity using accelerometers, 23rd EMBC, Istanbul, 25-28 Oct. 2001.
40. S. G. Miaou, F. C. Shih, and C.Y. Huang. A smart vision-based human fall detection system for telehealth applications, 3rd IASTED int'l Conf. on Telehealth, pp.564, May 30-June 1, 2007, Montreal, Quebec, Canada.
41. S. G. Miaou, P. H. Sung, and C. Y. Huang. A customized human fall detection system using omni-camera images and personal information, D2H2 2006 (1st Transdisciplinary Conf. on Distributed Diagnosis and Home Healthcare, pp.3942, 2006.
42. A. Mihailidis. An intelligent emergency response system: preliminary development and testing of automated fell detection, J. Telemed. Telecare, Vol 11(4):194-8, 2005.
43. A. Mihailidis, B. Carmichael, and J. Boger. The use of computer vision in an intelligent environment to support aging-in-place, safety, and independence in the home, IEEE Trans. on Information Technology in Biomedicine, vol. 8(3), pp.238-247, Sept. 2004.
44. H. Nart-Charif and S. J. McKenna. Activity summarisation and fall detection in a supportive home environment, ICPR 2004.
45. N. Noury, P. Barralon, G. Virone, P Boissy, M Hamel, and P Rumeau. A smart sensor based on rules and its evaluation in daily routines, EMBC 2003, Cancun-Mexico, pp3286-89, sept 2003.
46. th EMBC, pp.1663-1666, 22-26 Aug. 2007.
47. N. Noury, T. Herve, V. Rialle, G. Virone, E. Mercier, G. Morey, A. Moro, and T. Porcheron. Monitoring behavior in home using a smart fall sensor and positionsensors, 1st Annual Int'l IEEE-EMBS Special Topic Conf. on Microtech. in Med. & Biology, pp.607-610, Oct. 12-14, 2000.
48. N. Noury, A. Tarmizi, D. Savall, P.Boissy, P.Barralon, G. Virone, and P.Rumeau. A smart sensor for fall detection in daily routine, SICICA2003, Aveiro-Portugal, 9-11 Jul 2003.
49. M. N. Nyan, Francis E. H. Tay, M. Manimaran and K. H. W. Seah. Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer, J. Phys.: Conf. Ser. 34 (2006) 1059-1067.
50. G. Pérolle, P. Fraisse, M. Mavros, and I. Etxeberria. Automatic fall detection and activity monitoring for elderly, manuscript, 2007.
51. T. J. Petelenz, and S. C. Peterson. Elderly fall monitoring method and device, US Patent: 6433690 issued on Aug 13, 2002.
52. M. Prado, J. Reina-Tosina, and L. Roa. Distributed intelligent architecture for falling detection and physical activity analysis in the elderly, 2nd Joint EMBS/BME, Houston, TX, USA. Oct. 23-26, 2002.
53. C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau. Fall detection from, human shape and motion history using video surveillance, 21st Int'l Conf. on Advanced Information Networking and Applications Workshops, Vol 2, pp. 875-880, 2007.
54. C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau. Monocular 3D head tracking to detect falls of elderly people, 28th EMBC, pp.6384-6387, Aug. 2006.
55. C. Rougier and J. Meunier. Demo: fall detection using 3D head trajectory, 1st Int'l Workshop on Video Processing for Security (V4PS), Québec, June 2006.
56. O. P. Ryynanen , S.L. Kivela, R Honkanen, and P Laippala. Falls and lying helpless in the elderly, Z Gerontol; 25(4):278-82, 1992.
57. S. Sadigh, A Reimers, R Andersson, and L Laflamme. Falls and fall related injuries among the elderly: a survey of residential-care facilities in a Swedish municipality. J. Community Health, vol.29(2), pp.129-40, Apr 2004.
58. A. Salvà, I. Bolíbar, G. Pera, and C. Arias. Incidence and consequences of falls among elderly people living in the community, Med Clin (Barc). vol. 122(5), pp.172-176, Feb 14, 2004.
59. T. E. Scott, Bed exit detection apparatus, US Patent 6067019 issued on May 23, 2000.
60. A. Sixsmith and N. Johnson. A smart sensor to detect the falls of the elderly, IEEE Pervasive Computing, pp42-47, No 2, 2004.
61. A. Sixsmith, N. Johnson, and R. Whatmore. Pyroelectric IR sensor arrays for fall detection in the older population, J. Phys. IV France 128 pp.153-160, 2005.
62. J. A. Stevens, P. S. Corso, E A. Finkelstein, and T. R. Miller. The costs of fatal and non-fetal falls among older adults. Inj. Prev. 12: 290-295, 2006.
63. S. Straus. A 16-week tai chi programme prevented falls in healthy older adults. Evid. Based Med. 13: 54-54, 2008.
64. nd EMBS, Vol. 4, pp: 2608-2610, Chicago, IL, USA, 2000.
65. R. Teasell, M. McRae, N. Foley, and A. Bhardwaj. The incidence and consequences of falls in stroke patients during inpatient rehabilitation: factors associated with high risk, Archives of Physical Medicine and Rehabilitation, vol 83(3), pp.329-333, Mar 2002.
66. M. E. Tinetti, and C. S. Williams. Falls, injuries due to fells, and the risk of admission to a nursing home, New Eng J. of Medicine, vol.337, pp. 1279-1284, Oct. 30, 1997.
67. N. Thome, and S. Miguet. A HHMM-Based approach for robust fall detection, ICARCV '06 (9th Int'l Conf. on Control, Automation, Robotics and Vision), pp.1-8, Dec. 5-8, 2006.
68. B. U. Töreyin, Y. Dedeoglu, and A. E. Çetin. HMM based felling person detection using both audio and video, IEEE 14th Signal Processing and Communications Applications, 17-19 April 2006.
69. B. J. Vellas, S. J. Wayne, P. J. Garry and R. N. Baumgartner. A two-year longitudinal study of falls in 482 community-dwelling elderly adults, J Gerontol A Biol Sci Med Sci. Vol. 53(4), pp.264-274, Jul 1998.
70. N. C. Voermans, A. H. Snijders, Y. Schoon, and B. R. Bloem. Why old people fall (and how to stop them). PN 7:158-171, 2007.
71. G. Williams, K. Doughty, K. Cameron, and D. A. Bradley. A smart fall and activity monitor for telecare applications, 20th EMBC, vol.3, pp.1151-1154, 29 Oct-1 Nov 1998.
72. A. Williams, D. Ganesan, and A. Hanso. Aging in place: fall detection and localization in a distributed smart camera network, ACM MM, pp 892-901, 2007.
73. G. Wu. Distinguishing fall activities from normal activities by velocity characteristics, J. of Biomechanics, vol. 33(110, pp.1497-1500, 2000.
74. T. Zhang, J. Wang, L. Xu, and P. Liu. Using wearable sensor and NMF algorithm, to realize ambulatory fall detection , LNCS, Vol. 4222, pp.488-491, 2006.
75. T. Zhang, et al. Fall detection by embedding an accelerometer in cellphone and using KFD algorithm, Int'l Journal of Computer Science and Network Security. 6(10) pp: 277-84, 2006.
76. http://www.monitoringcare.com/medical-alert/fall.-detection-sensor.asp
77. http://www.argussecuri.tytech.com/pr_iv_appIi.catlons_slip.Fall.html [78] http://www.chhsys.com/asp_chhsys07/detectors.html
78. http://www.tactex.com/products_bed.php
79. http://www.tecsol.com.au/
80. htrp://www.ageproofyourbrain.com/silver-industry-conference-amp- exhibition-sicex-2008-singapor