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Personal and Ubiquitous Computing

, Volume 17, Issue 6, pp 1063–1072 | Cite as

Introducing the use of depth data for fall detection

  • Rainer Planinc
  • Martin Kampel
Original Article

Abstract

Current emergency systems for elderly contain at least one sensor (button or accelerometer), which has to be worn or pressed in case of emergency. If elderly fall and loose their consciousness, they are not able to press the button anymore. Therefore, autonomous systems to detect falls without wearing any devices are needed. This paper presents three different non-invasive technologies: the use of audio, 2D sensors (cameras) and introduces a new technology for fall detection: the Kinect as 3D depth sensor. Our fall detection algorithms using the Kinect are evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living. The evaluation results are compared with State-of-the-Art approaches using 2D sensors or microphones.

Keywords

Fall detection Depth sensor Kinect Autonomous system 

References

  1. 1.
    Aghajan H, Wu C, Kleihorst R (2008) Distributed vision networks for human pose analysis. In: Signal processing techniques for knowledge extraction and information fusion, pp 181–200Google Scholar
  2. 2.
    Aldrich F (2003) Smart homes: past, present and future. In: Harper R (eds) Inside the smart home, Springer, London, pp 17–39CrossRefGoogle Scholar
  3. 3.
    Anderson D, Keller J, Skubic M, Chen X, He Z (2006) Recognizing falls from silhouettes. In: 28th Annual international conference of the IEEE engineering in medicine and biology society (EMBS ’06), New York, pp 6388–6391Google Scholar
  4. 4.
    Anderson D, Luke R, Keller J, Skubic M, Rantz M, Aud M (2009) Linguistic summarization of video for fall detection using voxel person and fuzzy logic. Comput Vis Image Underst 113:80–89CrossRefGoogle Scholar
  5. 5.
    Belbachir AN, Lunden T, Hanák P, Markus F, Böttcher M, Mannersola T (2010) Biologically-inspired stereo vision for elderly safety at home. e & i Elektrotechnik Informationstechnik 127(7):216–222CrossRefGoogle Scholar
  6. 6.
    Cai Y (2010) Mobile intelligence. J Univers Comput Sci 16(12):1650–1665Google Scholar
  7. 7.
    Diraco G, Leone A, Siciliano P (2010) An active vision system for fall detection and posture recognition in elderly healthcare. In: Design, automation test in Europe conference exhibition (DATE), Dresden, pp 1536–1541Google Scholar
  8. 8.
    Doukas C, Maglogiannis I (2008) Advanced patient or elder fall detection based on movement and sound data. In: Second international conference on pervasive computing technologies for healthcare, pp 103–107. IEEE, TampereGoogle Scholar
  9. 9.
    Doulamis AD, Doulamis ND, Kollias SD (2000) A fuzzy video content representation for video summarization and content-based retrieval. Signal Process 80(6):1049–1067zbMATHCrossRefGoogle Scholar
  10. 10.
    Erik Stone MS (2011) Evaluation of an inexpensive depth camera for in-home gait assessment. J Ambient Intell Smart Environ 3(4):349–361Google Scholar
  11. 11.
    Fisk MJ (2001) The implication of smart home technologies. In: Inclusive housing in an aging society: innovative approaches. The Policy Press, Bristol, pp 101–124Google Scholar
  12. 12.
    Gales M, Young S (2008) The application of hidden Markov models in speech recognition. Found Trends Signal Process 1(3):195–304CrossRefGoogle Scholar
  13. 13.
    García-Vázquez J, Rodríguez M, Andrade A, Bravo J (2011) Supporting the strategies to improve elders medication compliance by providing ambient aids. Pers Ubiquit Comput 15(4):389–397CrossRefGoogle Scholar
  14. 14.
    Grabner H, Leistner C, Bischof H (2008) Time dependent on-line boosting for robust background modeling. In: Proceedings of the international conference on computer vision, imaging and computer graphics theory and applications, pp 612–618Google Scholar
  15. 15.
    Jansen B, Temmermans F, Deklerck R (2007) 3D human pose recognition for home monitoring of elderly. In: Conference of the IEEE on engineering in medicine and biology society, Lyon, pp 4049–4051Google Scholar
  16. 16.
    Jara A, Zamora M, Skarmeta A (2011) An internet of things-based personal device for diabetes therapy management in ambient assisted living (AAL). Pers Ubiquit Comput 15(4):431–440CrossRefGoogle Scholar
  17. 17.
    Kampel M, Wildenauer H, Blauensteiner P, Hanbury A (2007) Improved motion segmentation based on shadow detection. Electronic Lett Comput Vis Image Anal 6(3)Google Scholar
  18. 18.
    Labayrade R, Aubert D, Tarel JP (2002) Real time obstacle detection in stereovision on non flat road geometry through “v-disparity” representation. In: IEEE Intell Vehicle Symp 2:646–651Google Scholar
  19. 19.
    Laptev I, Caputo B, Schüldt C, Lindeberg T (2007) Local velocity-adapted motion events for spatio-temporal recognition. Comput Vis Image Underst 108(3):207–229CrossRefGoogle Scholar
  20. 20.
    Leikas J, Salo J, Poramo R (1998) Security alarm system supports independent living of demented persons. Gerontechnology: a sustainable investment in the future. Technol Inf 48:402–405Google Scholar
  21. 21.
    Lindemann U, Hock A, Stuber M, Keck W, Becker C (2005) Evaluation of a fall detector based on accelerometers: a pilot study. Med Biol Eng Comput 43:548–551CrossRefGoogle Scholar
  22. 22.
    Litvak D, Zigel Y, Gannot I (2008) Fall detection of elderly through floor vibrations and sound. In: 30th Annual international conference of the IEEE engineering in medicine and biology society, (EMBS ’08), vol 2008, pp 4632–4635Google Scholar
  23. 23.
    Lubinski R (1991) Dementia and communication. B.C. Decker, Inc., HamiltonGoogle Scholar
  24. 24.
    Mihailidis A, Carmichael B, Boger J (2002) The use of computer vision in an intelligent environment to support aging-in-place, safety, and independence in the home. IEEE Trans Inf Technol Biomed 8(3):238–247CrossRefGoogle Scholar
  25. 25.
    Noury N, Rumeau P, Bourke A, OLaighin G, Lundy J (2008) A proposal for the classification and evaluation of fall detectors. Biomed Eng Res (IRBM) 29(6):340–349Google Scholar
  26. 26.
    Oggier T, Lehmann M, Kaufmann R, Schweizer M, Richter M, Metzler P, Lang G, Lustenberger F, Blanc N (2004) An all-solid-state optical range camera for 3D real-time imaging with sub-centimeter depth resolution (SwissRanger). In: Proceedings of SPIE, vol 5249, SPIE, pp 534–545Google Scholar
  27. 27.
    Popescu M, Li Y, Skubic M, Rantz M (2008) An acoustic fall detector system that uses sound height information to reduce the false alarm rate. In: 30th Annual international conference of the IEEE engineering in medicine and biology society (EMBS ’08), pp 4628–4631Google Scholar
  28. 28.
    Popescu M, Mahnot A (2009) Acoustic fall detection using one-class classifiers. In: Symposium of the association for the advancement of artificial intelligence, AAAI 2009, pp 3505–3508Google Scholar
  29. 29.
    Rougier C, Auvinet E, Rousseau J, Mignotte M, Meunier J (2011) Fall detection from depth map video sequences. In: Abdulrazak B, Giroux S, Bouchard B, Pigot H, Mokhtari M (eds) Toward useful services for elderly and people with disabilities, Lecture Notes in Computer Science, vol 6719. Springer, Berlin / Heidelberg, Montreal, pp 121–128Google Scholar
  30. 30.
    Rougier C, Meunier J, St-Arnaud A, Rousseau J (2006) Monocular 3d head tracking to detect falls of elderly people. In: 28th Annual international conference of the IEEE on engineering in medicine and biology society (EMBS ’06), New York, pp 6384 –6387Google Scholar
  31. 31.
    Rougier C, Meunier J, St-Arnaud A, Rousseau J (2007) Fall detection from human shape and motion history using video surveillance. In: 21st International conference on advanced information networking and applications workshops (AINAW ’07), vol 2, Niagara Falls, pp 875–880Google Scholar
  32. 32.
    Saunders J (1996) Real time discrimination of broadcast speech/music. In: Proceedings of the international conference on acoustics, speech, signal processing (ICASSP), pp 993–996Google Scholar
  33. 33.
    Scheirer EMS (1997) Construction and evaluation of a robust multifeature speech/music discriminator. In: Proceedings of the international conference on acoustics, speech, signal processing (ICASSP), pp 1331–1334Google Scholar
  34. 34.
    Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A (2011) Real-time human pose recognition in parts from single depth images. In: IEEE conference on computer vision and pattern recognition (CVPR), 2011, pp 1297–1304Google Scholar
  35. 35.
    Van Rijsbergen CJ (1979) Information retrieval. Butterworths, LondonGoogle Scholar
  36. 36.
    Willems J, Debard G, Bonroy B, Vanrumste BTG (2009) How to detect human fall in video? An overview. In: Positioning and context-aware international conference (POCA)Google Scholar
  37. 37.
    Zambanini S, Machajdik J, Kampel M (2010) Early versus late fusion in a multiple camera network for fall detection. In: 34th Annual workshop of the Austrian Association f. Pattern recognition (ÖAGM 2010), vol 819862, Zwettl, Austria, pp 15–22Google Scholar
  38. 38.
    Zhang T, Kuo CC (1999) Hierarchical classification of audio data for archiving and retrieving. In: Proceedings of the international conference on acoustics, speech, signal processing (ICASSP), vol 6, pp 3001–3004Google Scholar
  39. 39.
    Zhao J, Katupitiya J, Ward J (2007) Global correlation based ground plane estimation using V-disparity image. In: IEEE international conference on robotics and automation, pp 529–534Google Scholar
  40. 40.
    Zweng A, Zambanini S, Kampel M (2010) Introducing a statistical behavior model into camera-based fall detection. In: Proceedings of the 6th international symposium on visual computing (ISCV), vol 6453, Las Vegas, Nevada, pp 163–172Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Computer Vision Lab, Vienna University of TechnologyViennaAustria

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