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Non-intrusive Bedside Event Recognition Using Infrared Array and Ultrasonic Sensor

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Book cover Ubiquitous Computing and Ambient Intelligence (UCAmI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10069))

Abstract

Falls in hospitals, in residential care facilities and in home of elderly commonly occur near the bed. Recognizing bedside events may give caretakers the opportunity to intervene, thereby preventing a fall from happening. Most approaches today either use cameras which invade privacy, or sensor devices attached to bed. In this paper an experimental approach for recognizing bedside events using a ceiling mounted 60 × 80 longwave infrared array combined with an ultrasonic sensor device is presented. This novel approach makes it possible to monitor activity while preserving privacy in a non-intrusive manner.

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References

  1. Public Health Agency of Canada: Report on Seniors’ fall in Canada (2005). http://publications.gc.ca/collections/Collection/HP25-1-2005E.pdf. Accessed 9 Mar 2016

  2. Public Health Agency of Canada: Senior’s fall in Canada – Second Report (2014). http://www.phac-aspc.gc.ca/seniors-aines/publications/public/injury-blessure/seniors_falls-chutes_aines/assets/pdf/seniors_falls-chutes_aines-eng.pdf. Accessed 9 Mar 2016

  3. Inouye, S.K., Brown, C.J., Tinetti, M.E.: Medicare nonpayment, hospital falls, and unintended consequences. New Eng. J. Med. 360, 2390–2393 (2009)

    Article  Google Scholar 

  4. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)

    Google Scholar 

  5. flir.com: FLIR Lepton Camera Breakout 1.4. http://www.flir.com/uploadedFiles/CVS_Americas/Cores_and_Components_NEW/Resources/flir-lepton-breakout-product-brief.pdf. Accessed 30 May 2016

  6. Shojania K.G., Duncan B.W., McDonald K.M., Wachter R.M., Markowitz A.J.: Making Health Care Safer: A critical analysis of patient safety practices. Evidence Report/Technology Assessment, Number 43 (2001). http://archive.ahrq.gov/clinic/ptsafety/pdf/ptsafety.pdf. Accessed 30 May 2016

  7. Capezuti, E., Maislin, G., Strumpf, N., Evans, L.: Siderail use and bed-related fall outcomes among nursing home residents. J. Am. Geriatr. Soc. 50, 90–96 (2002)

    Article  Google Scholar 

  8. Madokoro, H., Shimoi, N., Sato, K.: Bed-leaving detection using piezoelectric unrestrained sensors and its measurement system regarding QOL. Nurs. Health 1(2), 36–45 (2013)

    Google Scholar 

  9. Capezuti, E., Brush, B.L., Lane, S., Rabinowitz, H.U., Secic, M.: Bed-exit alarm effectiveness. Arch. Gerontol. Geriat. 49, 27–31 (2009)

    Article  Google Scholar 

  10. Ranasinghe, D.C., Shinmoto Torres, R.L., Hill, K., Visvanathan, R.: Low cost and batteryless sensor-enabled radio frequency identification tag based approaches to identify patient bed entry and exit posture transitions. Gait Posture 39, 118–123 (2014)

    Article  Google Scholar 

  11. Rapp, K., Becker, C., Cameronv, K., Büchele G.: Epidemiology of falls in residential aged care: analysis of more than 70,000 falls from residents of Bavarian nursing homes. J. Am. Med. Dir. Assoc. 13, 187.e1–187.e6 (2012)

    Google Scholar 

  12. Hanger, H.C., Ball, M.C., Wood, L.A.: An analysis of falls in the hospital: can we do without bedrails? J. Am. Geriatr. 47, 529–531 (1999)

    Article  Google Scholar 

  13. Shorr, R.I., Chandler, A.M., Mion, L.C., Waters, T.M., Liu, M., Daniels, M.J., Kessler, L.A., Miller, S.T.: Effects of an intervention to increase bed alarm use to prevent falls in hospitalized patients: a cluster randomized trial. Ann. Intern. Med. 157, 692–699 (2012)

    Article  Google Scholar 

  14. Tideiksaar, R., Feiner, C.F., Maby, J.: Falls prevention: the efficacy of a bed alarm system in an acute-care setting. Mount Sinai J. Med. New York 60(6), 522–527 (1993)

    Google Scholar 

  15. Bruyneel, M., Libert, W., Ninane, V.: Detection of bed-exit events using a new wireless bed monitoring assistance. Int. J. Med. Inform 80, 127–132 (2011)

    Article  Google Scholar 

  16. Dykes, P.C., Carroll, D.L., Hurley, A., Lipsitz, S., Benoit, A., Chang, F., Meltzerm, S., Tsurikova, R., Zuyov, L., Middleton, B.: Fall Prevention in Acute Care Hospitals: a randomized trial. JAMA, J. Am. Med. Assoc. 304, 1912–1918 (2010)

    Article  Google Scholar 

  17. Lee, D.-C.A., Pritchard, E., McDermott, F., Haines, T.P.: Falls prevention education for older adults during and after hospitalization: a systematic review and meta-analysis. Health Educ. J. 73, 530–544 (2014)

    Article  Google Scholar 

  18. Ryu, Y.M., Roche, J.P., Brunton, M.: Patient and family education for fall prevention: involving patients and families in a fall prevention program on a neuroscience unit. J. Nurs. Care Qual. 24, 243–249 (2009)

    Article  Google Scholar 

  19. Fonda, D., Cook, J., Sandler, V., Bailey, M.: Sustained reduction in serious fall-related injuries in older people in hospital. Med. J. Aust. 184, 379–382 (2006)

    Google Scholar 

  20. Shee, A.M., Phillips, B., Hill, K., Dodd, K.: Feasibility, acceptability, and effectiveness of an electronic sensor bed/chair alarm in reducing falls in patients with cognitive impairment in a subacute ward. J. Nurs. Care Qual. 29, 253–262 (2014)

    Article  Google Scholar 

  21. maxbotix.com, “I2CXL-MaxSonar® - EZTM Series Datasheet”. http://www.maxbotix.com/documents/I2CXL-MaxSonar-EZ_Datasheet.pdf. Accessed 30 May 2016

  22. Ni, B., Nguyen, C.D., Moulin, P.: RGBD-camera based get-up event detection for hospital fall prevention. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1405–1408. IEEE Press, New York (2012)

    Google Scholar 

  23. Rantz, M.J., Banerjee, T.S., Cattoor, E., Scott, S.D., Skubic, M., Popescu, M.: Automated fall detection with quality improvement “Rewind” to reduce falls in hospital rooms. J. Gerontol. Nurs. 40, 13–17 (2014)

    Article  Google Scholar 

  24. Sixsmith, A., Johnson, N.: A smart sensor to detect the falls of the elderly. IEEE Pervas. Comput. 3, 42–47 (2004)

    Article  Google Scholar 

  25. beagleboard.org: “BeagleBone Black” (2016). https://beagleboard.org/black. Accessed 30 May 2016

  26. Mashiyama S., Hong J., Ohtsuki T.: A fall detection system using low resolution infrared array sensor. In: IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), pp. 2109–2113. IEEE Press, New York (2014)

    Google Scholar 

  27. Li, Y., Berkowitz, L., Noskin, G., Mehrotra, S.: Detection of patient’s bed statuses in 3D using a Microsoft Kinect. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5900–5903. IEEE Press, New York (2014)

    Google Scholar 

  28. flir.com: “FLIR LEPTON® - Longwave Infrared (LWIR) Camera Module. http://www.mds-flir.com/datasheet/FLIR-Lepton-datasheet.pdf. Accessed 30 May 2016

  29. Danielsen, A., Olofsen, H., Bremdal, B.A.: Increasing fall risk awareness using wearables: A fall risk awareness protocol. J. Biomed. Inform. 63, 184–194 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. UiT – The Arctic University of Norway, Narvik, Norway

    Asbjørn Danielsen

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  1. Asbjørn Danielsen
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Correspondence to Asbjørn Danielsen .

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Carmelo R. García

  2. Departamento de Estadistica, Universidad La Laguna, La Laguna, Spain

    Pino Caballero-Gil

  3. Florida State University, Tallahassee, Florida, USA

    Mike Burmester

  4. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Alexis Quesada-Arencibia

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Danielsen, A. (2016). Non-intrusive Bedside Event Recognition Using Infrared Array and Ultrasonic Sensor. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science(), vol 10069. Springer, Cham. https://doi.org/10.1007/978-3-319-48746-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-48746-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48745-8

  • Online ISBN: 978-3-319-48746-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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