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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Center for Disease Control and Prevention (CDC): Falls among older adults: An overview. www.cdc.gov/homeandrecreationalsafety/Falls/adultfalls.html. Accessed 13 Dec 2013
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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
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)
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)
Mastorakis, G., Makris, D.: Fall detection system using Kinect’s infrared sensor. J. Real-Time Image Process. 9(4), 635–646 (2014)
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)
Planinc, R., Kampel, M.: Introducing the use of depth data for fall detection. Pers. Ubiquit. Comput. 17(6), 1063–1072 (2012)
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)
Bagalà, F., et al.: Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7(5), e37062 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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)