Skip to main content

Testing Real-Time In-Home Fall Alerts with Embedded Depth Video Hyperlink

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8456))

Abstract

A method for sending real-time fall alerts containing an embedded hyperlink to a depth video clip of the suspected fall was evaluated in senior housing. A previously reported fall detection method using the Microsoft Kinect was used to detect naturally occurring falls in the main living area of each apartment. In this paper, evaluation results are included for 12 apartments over a 101 day period in which 34 naturally occurring falls were detected. Based on computed fall confidences, real-time alerts were sent via email to facility staff. The alerts contained an embedded hyperlink to a short depth video clip of the suspected fall. Recipients were able to click on the hyperlink to view the clip on any device supporting play back of MPEG-4 video, such as smart phones, to immediately determine if the alert was for an actual fall or a false alarm. Benefits and limitations of the technology are discussed.

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. Center for Disease Control and Prevention (CDC): Falls among older adults: An overview. www.cdc.gov/homeandrecreationalsafety/Falls/adultfalls.html. Accessed 13 Dec 2013

  2. Stevens, J.A., Corso, P.S., Finkelstein, E.A., Miller, T.R.: The costs of fatal and non-fatal falls among older adults. Inj. Prev. 12(5), 290–295 (2006)

    Article  Google Scholar 

  3. Tinetti, M.E., Liu, W.L., Claus, E.B.: Predictors and prognosis of inability to get up after falls among elderly persons. JAMA, J. Am. Med. Assoc. 269(1), 65–70 (1993)

    Article  Google Scholar 

  4. Noury, N., et al.: Fall detection-principles and methods. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1663–1666 (2007)

    Google Scholar 

  5. Bourke, A.K., O’brien, J.V., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2), 194–199 (2007)

    Article  Google Scholar 

  6. Demiris, G., et al.: Older adults’ attitudes towards and perceptions of smart home technologies: A pilot study. Inf. Health Soc. Care 29(2), 87–94 (2004)

    Article  Google Scholar 

  7. Sixsmith, A., Johnson, N., Whatmore, R.: Pyrolitic IR sensor arrays for fall detection in the older population. J. Phys. IV France 128, 153–160 (2005)

    Article  Google Scholar 

  8. Li, Y., Zeng, Z.L., Popescu, M., Ho, K.C.: Acoustic fall detection using a circular microphone array. In: 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2242–2245 (2010)

    Google Scholar 

  9. Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans. Circ. Syst. Video Technol. 21, 611–622 (2011)

    Article  Google Scholar 

  10. Miaou, S.G., Sung, P.H., Huang, C.Y.: A customized human fall detection system using omni-camera images and personal information. In: Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, pp. 39–42 (2006)

    Google Scholar 

  11. Lee, T., Mihailidis, A.: An intelligent emergency response system: preliminary development and testing of automated fall detection. J. Telemed. Telecare 11(4), 194–198 (2005)

    Article  Google Scholar 

  12. Anderson, D., Luke, R.H., Keller, J., Skubic, M., Rantz, M., Aud, M.: Linguistic summarization of activities from video for fall detection using voxel person and fuzzy logic. Comput. Vis. Image Underst. 113(1), 80–89 (2009)

    Article  Google Scholar 

  13. Auvinet, E., et al.: Fall detection with multiple cameras: An occlusion-resistant method based on 3-d silhouette vertical distribution. IEEE Trans. Info. Tech. Biomed. 15(2), 290–300 (2011)

    Article  Google Scholar 

  14. Demiris, G., Parker, O.D., Giger, J., Skubic, M., Rantz, M.: Older adults’ privacy considerations for vision based recognition methods of eldercare applications. Technol. Health Care 17, 41–48 (2009)

    Google Scholar 

  15. Stone, E., Skubic, M.: Fall detection in homes of older adults using the microsoft kinect. IEEE J. Biomed. Health Inf. (2014). doi:10.1109/JBHI.2014.2312180

  16. Kepski, M., Kwolek, B., Austvoll, I.: Fuzzy inference-based reliable fall detection using kinect and accelerometer. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 266–273. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Marzahl, C., Penndorf, P., Bruder, I., Staemmler, M.: Unobtrusive fall detection using 3D images of a gaming console: concept and first results. In: Wichert, R., Eberhardt, B. (eds.) Ambient Assisted Living. ATSC, vol. 2, pp. 135–146. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  18. Mastorakis, G., Makris, D.: Fall detection system using Kinect’s infrared sensor. J. Real-Time Image Process. 9(4), 635–646 (2014)

    Article  Google Scholar 

  19. Rougier, C., Anvient, E., Rousseau, J., Mignotte, M., Meunier, J.: Fall detection from depth map video sequences. In: International Conference on Smart Homes and Health Telematics, pp. 121–128 (2011)

    Google Scholar 

  20. Planinc, R., Kampel, M.: Introducing the use of depth data for fall detection. Pers. Ubiquit. Comput. 17(6), 1063–1072 (2012)

    Article  Google Scholar 

  21. Bourke, A.K., Pepijn, W.J., Chaya, A.E., Olaighin, G.M., Nelson, J.: Testing of a long-term fall detection system incorporated into a custom vest for the elderly. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2844–2847 (2008)

    Google Scholar 

  22. Bagalà, F., et al.: Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7(5), e37062 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Erik E. Stone .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Stone, E.E., Skubic, M. (2015). Testing Real-Time In-Home Fall Alerts with Embedded Depth Video Hyperlink. In: Bodine, C., Helal, S., Gu, T., Mokhtari, M. (eds) Smart Homes and Health Telematics. ICOST 2014. Lecture Notes in Computer Science(), vol 8456. Springer, Cham. https://doi.org/10.1007/978-3-319-14424-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14424-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14423-8

  • Online ISBN: 978-3-319-14424-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics