Skip to main content

Safety Services in Smart Environments Using Depth Cameras

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10217))

Abstract

Falls of elderly persons are the most common cause of serious injuries in this age group. It is important to detect the fall in a timely manner. If medical help can’t be provided immediately a deterioration of the patient’s state may occur. In order to tackle this challenge, we want to propose two combined safety services that can utilize the same sensor to prevent and detect falls. The Dangerous Object Adviser detects small obstacles located on the floor and warns the user about the stumbling hazard when the user walks in their direction. The Fall Detection Service detects a fall and informs caregivers. This enables the caregivers to provide medical care in time. Both services are implemented by using the Microsoft Kinect, with the obstacles extracted from the depth image and the usage of skeleton tracking gives to provide the necessary information on the user position and pose.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Su, B.Y., Ho, K.C., Rantz, M.J., Skubic, M.: Doppler radar fall activity detection using the wavelet transform. IEEE Trans. Biomed. Eng. 62(3), 865–875 (2015)

    Article  Google Scholar 

  2. Prediger, M., Braun, A., Marinc, A., Kuijper, A.: Robot-supported pointing interaction for intelligent environments. In: Streitz, N., Markopoulos, P. (eds.) DAPI 2014. LNCS, vol. 8530, pp. 172–183. Springer, Cham (2014). doi:10.1007/978-3-319-07788-8_17

    Chapter  Google Scholar 

  3. Kepski, M., Kwolek, B.: Human fall detection using kinect sensor. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) CORES 2013. AISC, vol. 226, pp. 743–752. Springer, Heidelberg (2013). doi:10.1007/978-3-319-00969-8_73

    Chapter  Google Scholar 

  4. Hanke, S., Sandner, E., Stainer-Hochgatterer, A., Tsiourti, C., Braun, A.: The technical specification and architecture of a virtual support partner. In: AmI (Workshops/Posters) (2015)

    Google Scholar 

  5. Chen, J., Kwong, K., Chang, D., Luk, J., Bajcsy, R.: Wearable sensors for reliable fall detection. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 3551–3554, January 2005

    Google Scholar 

  6. Kawatsu, C., Li, J., Chung, C.J.: Development of a fall detection system with microsoft kinect. In: Kim, J.-H., Matson, E.T., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications 2012. AISC, vol. 208, pp. 623–630. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37374-9_59

    Chapter  Google Scholar 

  7. Alwan, M., Rajendran, P.J., Kell, S., Mack, D., Dalal, S., Wolfe, M., Felder, R.: A smart and passive floor-vibration based fall detector for elderly. In: 2006 2nd International Conference on Information Communication Technologies, vol. 1, pp. 1003–1007 (2006)

    Google Scholar 

  8. Braun, A., Heggen, H., Wichert, R.: CapFloor – a flexible capacitive indoor localization system. In: Chessa, S., Knauth, S. (eds.) EvAAL 2011. CCIS, vol. 309, pp. 26–35. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33533-4_3

    Chapter  Google Scholar 

  9. Braun, A., Wichert, R., Kuijper, A., Fellner, D.W.: Capacitive proximity sensing in smart environments. J. Ambient Intell. Smart Environ. 7(4), 483–510 (2015)

    Article  Google Scholar 

  10. Kirchbuchner, F., Grosse-Puppendahl, T., Hastall, M.R., Distler, M., Kuijper, A.: Ambient intelligence from senior citizens’ perspectives: understanding privacy concerns, technology acceptance, and expectations. In: Ruyter, B., Kameas, A., Chatzimisios, P., Mavrommati, I. (eds.) AmI 2015. LNCS, vol. 9425, pp. 48–59. Springer, Cham (2015). doi:10.1007/978-3-319-26005-1_4

    Chapter  Google Scholar 

  11. Rougier, C., Meunier, J.: Demo: fall detection using 3D head trajectory extracted from a single camera video sequence. J. Telemedicine Telecare 11(4), 37–42 (2005)

    Google Scholar 

  12. Gasparrini, S., Cippitelli, E., Spinsante, S., Gambi, E.: A depth-based fall detection system using a kinect\(\textregistered \) sensor. Sensors 14(2), 2756 (2014)

    Article  Google Scholar 

  13. Hamm, J., Money, A.G., Atwal, A., Paraskevopoulos, I.: Fall prevention intervention technologies: a conceptual framework and survey of the state of the art. J. Biomed. Inform. 59, 319–345 (2016)

    Article  Google Scholar 

  14. Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Auton. Robot. 4(4), 333–349 (1997)

    Article  Google Scholar 

  15. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments. Int. J. Rob. Res. 31(5), 647–663 (2012)

    Article  Google Scholar 

  16. Microsoft Corporation: Kinect for windows SDK - kinect fusion, May 2016

    Google Scholar 

  17. Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)

    Article  Google Scholar 

  18. Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)

    Article  Google Scholar 

  19. Fryar, C.D., Qiuping, G., Ogden, C.L.: Anthropometric reference data for children and adults: United States, 2007–2010. Vital and Health Stat. Ser. 11, Data Natl. Health Surv. 252, 1–48 (2012)

    Google Scholar 

  20. Microsoft Corporation: Kinect for windows SDK v1.8 - skeletal tracking, May 2016

    Google Scholar 

  21. Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9–13 May 2011

    Google Scholar 

  22. Microsoft Corporation: Kinect for windows SDK - kinect API overview, May 2016

    Google Scholar 

  23. Microsoft Corporation: Kinect for windows SDK - features, May 2016

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andreas Braun .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Mettel, M.R., Alekseew, M., Stocklöw, C., Braun, A. (2017). Safety Services in Smart Environments Using Depth Cameras. In: Braun, A., Wichert, R., Maña, A. (eds) Ambient Intelligence. AmI 2017. Lecture Notes in Computer Science(), vol 10217. Springer, Cham. https://doi.org/10.1007/978-3-319-56997-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56997-0_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56996-3

  • Online ISBN: 978-3-319-56997-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics