Detecting falls with 3D range camera in ambient assisted living applications: A preliminary study

Medical Engineering and Physics

Volumn 33, Issue 6, 2011, Pages 770-781

  Leone, Alessandro ^a   Diraco, Giovanni ^a   Siciliano, Pietro ^a  

^a CNR   (Italy)

Author keywords

3D range camera; Ambient assisted living; Fall detection

Indexed keywords

3D RANGE CAMERAS; ACTIVE SENSOR; ACTIVITIES OF DAILY LIVING; ALGORITHMIC FRAMEWORK; AMBIENT ASSISTED LIVING; AUTOMATIC DETECTION; CRITICAL EVENTS; DATA SETS; DEPTH IMAGE; ECONOMIC COSTS; EMERGENCY RESPONSE; FALL ACCIDENTS; FALL DETECTION; HOME ENVIRONMENT; ILLUMINATION CONDITIONS; IMAGING METHOD; INFORMATION AND COMMUNICATION TECHNOLOGIES; METRIC INFORMATION; NIGHT VISION; NON-TECHNICAL USERS; OLDER PEOPLE; PASSIVE VISION; QUALITY OF LIFE; RANGE CAMERAS; SEGMENTATION AND TRACKING; SELF-CALIBRATION ALGORITHMS; SENSITIVITY AND SPECIFICITY; TIME OF FLIGHT; VISION SYSTEMS; VISION TECHNOLOGY;

ALGORITHMS; IMAGE SEGMENTATION; INFORMATION TECHNOLOGY; THREE DIMENSIONAL;

CAMERAS;

ARTICLE; ASSISTED LIVING FACILITY; AUTOMATION; BODY MOVEMENT; COMPUTER SYSTEM; CONTROLLED STUDY; ECONOMIC ASPECT; ELDERLY CARE; EMERGENCY TREATMENT; FALLING; ILLUMINATION; IMAGE DISPLAY; INDEPENDENCE; MEDICAL INSTRUMENTATION; MOTION ANALYSIS SYSTEM; NIGHT VISION; PATIENT SAFETY; PRIORITY JOURNAL; RELIABILITY; SENSITIVITY AND SPECIFICITY; SOCIAL ASPECT; TELEVISION CAMERA; THREE DIMENSIONAL IMAGING;

ACCIDENTAL FALLS; ACCIDENTS, HOME; ACTIVITIES OF DAILY LIVING; ADULT; AGED; FEASIBILITY STUDIES; HOME CARE SERVICES; HUMANS; IMAGING, THREE-DIMENSIONAL; MIDDLE AGED; MONITORING, AMBULATORY; PHOTOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79959516241     PISSN: 13504533     EISSN: 18734030     Source Type: Journal    
DOI: 10.1016/j.medengphy.2011.02.001     Document Type: Article

References (25)

1. Noury N., Rumeau P., Bourke A.K., ÓLaighin G., Lundy J.E. A proposal for the classification and evaluation of fall detectors. IRBM 2008, 29(6):340-349.
2. Chung M.C., McKee K.J., Austin C., Barkby H., Brown H., Cash S., et al. Posttraumatic stress disorder in older people after a fall. Int J Geriatr Psychiatry 2009, 24(9):955-964.
3. Sadigh S., Reimers A., Andersson R., Laflamme L. Falls and fall-related injuries among the elderly: a survey of residential-care facilities in a Swedish municipality. J Community Health 2004, 29:129-140.
4. Shumway-Cook A., Ciol M.A., Hoffman J., Dudgeon B.J., Yorkston K., Chan L. Falls in the Medicare population: incidence, associated factors, and impact on health care. Phys Ther 2009, 89(4):324-332.
5. Lord S.R., Sherrington C., Menz H.B. Falls in older people: risk factors and strategies for prevention 2007, Cambridge University Press.
6. Elliott S., Painter J., Hudson S. Living alone and fall risk factors in community-dwelling middle age and older adults. J Community Health 2009, 34:301-310.
7. Noury N., Fleury A., Rumeau P., Bourke A.K., Laighin G.O., Rialle V., et al. Fall detection-principles and methods. Proceedings of the 29th IEEE EMBS 2007, 1663-1666.
8. Yu X. Approaches and principles of fall detection for elderly and patient. Proceedings of the 10th IEEE HealthCom 2008, 42-47.
9. Shaou-Gang M., Fu-Chiau S., Chia-Yuan H A smart vision-based human fall detection system for telehealth applications. Proceedings of the 3rd Telehealth Conference 2007, 7-12.
10. Jansen B., Deklerck R. Context aware inactivity recognition for visual fall detection. Proceedings of IEEE Pervasive Health Conference 2006, 1-4.
11. Cucchiara R., Prati A., Vezzani R. A multi-camera vision system for fall detection and alarm generation. Expert Syst J 2007, 24(5):334-345.
12. Hussmann S., Hagebeuker B., Ringbeck T. A performance review of 3D TOF vision systems in comparison to stereo vision systems 2008, I-Tech Education and Publishing, [Book " Stereo Vision" (7)], p. 103-20.
13. Hartley R.I., Zisserman A. Multiple view geometry in computer vision 2004, Cambridge University Press. 2nd ed.
14. http://www.mesa-imaging.ch/.
15. Guomundsson S.A., Aanaes H., Larsen R. Environmental effects on measurement uncertainties of time-of-flight cameras. Proceedings of IEEE ISSCS, vol. 1 2007, 1-4.
16. Fischler M.A., Bolles R.C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 1981, 24(6):381-395.
17. Lee D.S. Effective Gaussian mixture learning for video background subtraction. IEEE PAMI 2005, 27(5):827-832.
18. Isard M., Blake A. Condensation-conditional density propagation for visual tracking. Int J Comput Vision 1998, 29(1):5-28.
19. http://www.xsens.com/.
20. Parkkari J., Kannus P., Palvanen M., Natri A., Vainio J., Aho H., et al. Majority of hip fractures occur as a result of a fall and impact on the greater trochanter of the femur: a prospective controlled hip fracture study with 206 consecutive patients. Calcif Tissue Int 1999, 65:183-187.
21. http://sourceforge.net/projects/opencvlibrary.
22. Stauffer C., Grimson W. Adaptive background mixture models for real-time tracking. Proceedings of IEEE Computer Vision and Pattern Recognition Conference 1999, 246-252.
23. Kalman R.E. A new approach to linear filtering and prediction problems. Trans ASME-J Basic Eng 1960, 82(Series D):35-45.
24. Netcarity UE Project, http://www.netcarity.org/.
25. Bourke A.K., Lyons G.M. A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med Eng Phys 2008, 30(1):84-90.