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Feasibility of Non-contact Smart Sensor-Based Falls Detection in a Residential Aged Care Environment

  • Ann BordaEmail author
  • Cathy Said
  • Cecily Gilbert
  • Frank Smolenaers
  • Michael McGrath
  • Kathleen Gray
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 170)

Abstract

Few studies of sensor-based falls detection devices have monitored older people in long-term care settings. The present investigation has addressed this gap by trialing the feasibility and acceptability of a non-contact smart sensor system (NCSSS) to monitor behaviour and detect falls in an Australian residential aged care facility (RAC). Methods This investigation was undertaken using a mixed methods approach, comprising three phases:
  1. (1)

    Pilot study design and implementation at a RAC, using a purposive sampling approach;

     
  2. (2)

    Study evaluation and post-pilot interviews; and

     
  3. (3)

    Analysis and review of results.

     
Results Data was collected for four RAC participants over four weeks of the NCSSS pilot study. Numerous feasibility challenges were encountered, for example, in the installation configuration, placement of sensors for optimal detection, network and connectivity issues, and maintenance requirements. Conclusion The area of smart sensor technologies in falls monitoring and detection remains a relatively emergent field of investigation, and presently there are few real-life studies of NCSSS in an Australian RAC setting reported in the literature. This study confirmed that NCSSS technology may have a role in falls and behaviour monitoring of elderly residents in RAC and home environments. However, feasibility factors may affect implementation and adherence.

Keywords

Falls detection Falls monitoring Smart sensors Ambient assistive technology Residential aged care Patient safety 

Notes

Acknowledgements

Study participants and their carers; Staff at the residential care facility; Professor Fernando Martin-Sanchez, Universidad de A Coruna; Advisory Board members Dr Frances Batchelor (National Ageing Research Institute) Melbourne, Prof George Demiris (Department of Biobehavioral Nursing and Health Systems, University of Washington, Seattle), Prof. Dr. Michael Marschollek (Hannover Medical School and University of Braunschweig—Institute of Technology); Dr Karen Courtney (School of Health Information Science, University of Victoria, Victoria, Canada) for permission to adapt her interview guide, and Raoul Ney, Semantrix Pty Ltd.

This study was funded in 2015 by the Networked Society Institute (formerly, Institute for a Broadband-enabled Society), The University of Melbourne, and completed in 2017.

References

  1. 1.
    Allan, L.M., Ballard, C.G., Rowan, E.N., Kenny, R.A.: Incidence and prediction of falls in dementia: a prospective study in older people. PLoS ONE 4, e5521 (2009)CrossRefGoogle Scholar
  2. 2.
    Aloulou, H., Mokhtari, M., Tiberghien, T., Biswas, J., Phua, C., Lin, J.H.K., Yap, P.: Deployment of assistive living technology in a nursing home environment: methods and lessons learned. BMC Med. Inform. Decis. Mak. 13(42), 17 p (2013). https://doi.org/10.1186/1472-6947-13-42CrossRefGoogle Scholar
  3. 3.
    Australian Institute of Health and Welfare: Admitted Patient Care 2015–16. Australian Hospital Statistics. Health Services Series No. 75. AIHW, Canberra (2017)Google Scholar
  4. 4.
    Boulton, E., Hawley-Hague, H., Vereijken, B., Clifford, A., Guldemond, N., Pfeiffer, K., Hall, A., Chesani, F., Mellone, S., Bourke, A., Todd, C.: Developing the FARSEEING Taxonomy of Technologies: Classification and Description of Technology Use (including ICT) in Falls Prevention Studies. J. Biomed. Inform. (2016).  https://doi.org/10.1016/j.jbi.2016.03.017CrossRefGoogle Scholar
  5. 5.
    Bradley, C.: Trends in Hospitalisations Due to Falls by Older People, Australia 1999-00 to 2010-11. Injury Research and Statistics No. 84. Cat. No. INJCAT 160. AIHW, Canberra (2013)Google Scholar
  6. 6.
    Brender, J., Talmon, J., de Keizer, N., Nykänen, P., Rigby, M., Ammenwerth, E.: STARE-HI—statement on reporting of evaluation studies in health informatics: explanation and elaboration. Appl. Clin. Inform. 4(3), 331–358 (2013).  https://doi.org/10.4338/aci-2013-04-ra-0024CrossRefGoogle Scholar
  7. 7.
    Cameron, I.D., Gillespie, L.D., Robertson, M.C., Murray, G.R., Hill, K.D., Cumming, R.G., Kerse, N.: Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database Syst. Rev. 12(Art. No.: CD005465) (2012).  https://doi.org/10.1002/14651858.cd005465.pub3
  8. 8.
    Chaudhuri, S., Thompson, H., Demiris, G.: Fall detection devices and their use with older adults: a systematic review. J. Geriatr. Phys. Therapy 00, 1–19 (2013).  https://doi.org/10.1519/JPT.0b013e3182abe779CrossRefGoogle Scholar
  9. 9.
    Farshchian, B.A., Dahl, Y.: The role of ICT in addressing the challenges of age-related falls: a research agenda based on a systematic mapping of the literature. Pers. Ubiquit. Comput. 19(3–4), 649–666 (2015).  https://doi.org/10.1007/s00779-015-0852-1CrossRefGoogle Scholar
  10. 10.
    Feldwieser, F., Gietzelt, M., Goevercin, M., Marschollek, M., Meis, M., Winkelbach, S., Wolf, K.H., Spehr, J., Steinhagen-Thiessen, E.: Multimodal sensor-based fall detection within the domestic environment of elderly people. Zeitschrift für Gerontologie und Geriatrie 47(8), 661–665 (2014).  https://doi.org/10.1007/s00391-014-0805-8CrossRefGoogle Scholar
  11. 11.
    Fischer, S.H., David, D., Crotty, B.H., Dierks, M., Safran, C.: Acceptance and use of health information technology by community-dwelling elders. Int. J. Med. Inform. 83(9), 624–635 (2014). https://doi.org/10.1016/j.ijmedinf.2014.06.005CrossRefGoogle Scholar
  12. 12.
    Fitzgerald, T.D., Hadjistavropoulos, T., Williams, J., et al.: The impact of fall risk assessment on nurse-led fears, patient falls and functional ability in long term care. Disabil. Rehabil. 38(11), 1041–1052 (2016)CrossRefGoogle Scholar
  13. 13.
    Francis-Coad, Jacqueline, Etherton-Beer, Christopher, Burton, Elissa, Naseri, Chiara, Hill, Anne-Marie: Effectiveness of complex falls prevention interventions in residential aged care settings: a systematic review. JBI Database Syst. Rev. Implementation Rep. 16(4), 973–1002 (2018).  https://doi.org/10.11124/JBISRIR-2017-003485CrossRefGoogle Scholar
  14. 14.
    Gietzelt, M., Spehr, J., Ehmen, Y., Wegel, S., Feldwieser, F., Meis, M., Marschollek, M., Wolf, K.H., Steinhagen-Thiessen, E., Govercin, M.: GAL@Home: a feasibility study of sensor-based in-home fall detection. Zeitschrift für Gerontologie und Geriatrie 45(8), 716–721 (2012).  https://doi.org/10.1007/s00391-012-0400-9CrossRefGoogle Scholar
  15. 15.
    Greenhalgh, T., Shaw, S., Wherton, J., Hughes, G., Lynch, J., A’Court, C., Hinder, S., Fahy, N., Byrne, E., Finlayson, A., Sorell, T., Procter, R., Stones, R.: SCALS: a fourth-generation study of assisted living technologies in their organisational, social, political and policy context. BMJ Open 6(2), e010208 (2016).  https://doi.org/10.1136/bmjopen-2015-010208CrossRefGoogle Scholar
  16. 16.
    Hawley-Hague, H., Boulton, E., Hall, A., Pfeiffer, K., Todd, C.: Older adults’ perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review. Int. J. Med. Informatics 83(6), 416–426 (2014).  https://doi.org/10.1016/j.ijmedinf.2014.03.002CrossRefGoogle Scholar
  17. 17.
    Jancey, J., Wold, C., Meade, R., Sweeney, R., Davison, E., Leavy, J.: A balanced approach to falls prevention: application in the real world. Health Promot. J. Austral 00, 1–5 (2018).  https://doi.org/10.1002/hpja.42CrossRefGoogle Scholar
  18. 18.
    Klenk, J., et al.: The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls. Eur. Rev. Aging Phys. Act. 13, 8 (2016).  https://doi.org/10.1186/s11556-016-0168-9CrossRefGoogle Scholar
  19. 19.
    Kosse, N.M., Brands, K., Bauer, J.M., Hortobagyi, T., Lamoth, C.J.: Sensor technologies aiming at fall prevention in institutionalized old adults: a synthesis of current knowledge. Int. J. Med. Inform. 82(9), 743–752 (2013).  https://doi.org/10.1016/j.ijmedinf.2013.06.001CrossRefGoogle Scholar
  20. 20.
    Lipsitz, L.A., Tchalla, A.E., Iloputaife, I., Gagnon, M., Dole, K., Su, Z.Z., Klickstein, L.: Evaluation of an automated falls detection device in nursing home residents. J. Am. Geriatr. Soc. 64(2), 365–368 (2016).  https://doi.org/10.1111/jgs.13708CrossRefGoogle Scholar
  21. 21.
    Ludwig, W., Wolf, K.H., Duwenkamp, C., et al.: Health-enabling technologies for the elderly—an overview of services based on a literature review. Comput. Methods Programs Biomed. 106(2), 70–78 (2012)CrossRefGoogle Scholar
  22. 22.
    Marschollek, M., Becker, M., Bauer, J.M., Bente, P., Dasenbrock, L., Elbers, K., Hein, A., Kolb, G., Kunemund, H., Lammel-Polchau, C., Meis, M., Meyer Zu Schwabedissen, H., Remmers, H., Schulze, M., Steen, E.E., Thoben, W., Wang, J., Wolf, K.H., Haux, R.: Multimodal activity monitoring for home rehabilitation of geriatric fracture patients–feasibility and acceptance of sensor systems in the GAL-NATARS study. Inform. Health Soc. Care 39(3–4), 262–271 (2014).  https://doi.org/10.3109/17538157.2014.931852CrossRefGoogle Scholar
  23. 23.
    Nijhof, N., van Gemert-Pijnen, L.J., Woolrych, R., Sixsmith, A.: An evaluation of preventive sensor technology for dementia care. J. Telemed. Telecare 19(2), 95–100 (2013).  https://doi.org/10.1258/jtt.2012.120605CrossRefGoogle Scholar
  24. 24.
    Nunan, S., Wilson, C.B., Henwood, T., Parker, D.: Fall risk assessment tools for use among older adults in long-term care settings: a systematic review of the literature. Aust. J. Ageing 37(1), 23–33 (2017).  https://doi.org/10.1111/ajag.12476CrossRefGoogle Scholar
  25. 25.
    Pang, I., Okubo, Y., Sturnieks, D., Lord, S.R., Brodie, M.A.: Detection of near falls using wearable devices. Syst. Rev. J. Geriatr. Phys. Therapy (2018).  https://doi.org/10.1519/jpt.0000000000000181 (Epub ahead of print)CrossRefGoogle Scholar
  26. 26.
    Peek, S.T., Wouters, E.J., van Hoof, J., Luijkx, K.G., Boeije, H.R., Vrijhoef, H.J.: Factors influencing acceptance of technology for aging in place: a systematic review. Int. J. Med. Inform. 83(4), 235–248 (2014).  https://doi.org/10.1016/j.ijmedinf.2014.01.004CrossRefGoogle Scholar
  27. 27.
    Peetoom, K.K., Lexis, M.A., Joore, M., Dirksen, C.D., De Witte, L.P.: Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil. Rehabil. Assist Technol 10(4), 271–294 (2015).  https://doi.org/10.3109/17483107.2014.961179CrossRefGoogle Scholar
  28. 28.
    Pol, M., Poerbodipoero, S., Robben, S., Daams, J., van Hartingsveldt, M., de Vos, R., de Rooij, S., Krose, B., Buurman, M.B.: Sensor monitoring to measure and support daily functioning for independently living older people: a systematic review and road map for further development. JAGS 61(12), 219–227 (2013).  https://doi.org/10.1111/jgs.12563CrossRefGoogle Scholar
  29. 29.
    Potter, P., et al.: Evaluation of sensor technology to detect fall risk and prevent falls in acute care. Joint Commission J. Qual. Patient Safety 43(8), 414–421 (2017)CrossRefGoogle Scholar
  30. 30.
    Potter, P., Allen, K., Costantinou, E., Klinkenberg, D., Malen, J., Norris, T., O’Connor, E., Roney, W., Tymkew, H.H.: Anatomy of inpatient falls: examining fall events captured by depth-sensor technology. Joint Commission J. Qual. Patient Safety 42(5), 225–231 (2016)CrossRefGoogle Scholar
  31. 31.
    Rantz, M.J., Skubic, M., Miller, S.J., Galambos, C., Alexander, G., Keller, J., Popescu, M.: Sensor technology to support aging in place. J. Am. Med. Dir. Assoc. 14(6), 386–391 (2013).  https://doi.org/10.1016/j.jamda.2013.02.018CrossRefGoogle Scholar
  32. 32.
    Rantz, M., Skubic, M., Abbott, C., Galambos, C., Popescu, M., Keller, J., Stone, E., Back, J., Miller, S.J., Petroski, G.F.: Automated in-home fall risk assessment and detection sensor system for elders. Gerontologist 55(Suppl 1), S78–S87 (2015a).  https://doi.org/10.1093/geront/gnv044CrossRefGoogle Scholar
  33. 33.
    Rantz, M.J., Skubic, M., Popescu, M., Galambos, C., Koopman, R.J., Alexander, G.L., Phillips, L.J., Musterman, K., Back, J., Miller, S.J.: A new paradigm of technology-enabled ‘vital signs’ for early detection of health change for older adults. Gerontology 61(3), 281–290 (2015b).  https://doi.org/10.1159/000366518CrossRefGoogle Scholar
  34. 34.
    Shinmoto Torres, R.L., Visvanathan, R., Abbott, D., Hill, K.D., Ranasinghe, D.C.: A battery-less and wireless wearable sensor system for identifying bed and chair exits in a pilot trial in hospitalized older people. PLoS ONE 12(10), e0185670 (2017)CrossRefGoogle Scholar
  35. 35.
    Stone, E.E., Skubic, M.: Fall detection in homes of older adults using the microsoft kinect. IEEE J. Biomed. Health Inform. 19(1), 290–301 (2015).  https://doi.org/10.1109/JBHI.2014.2312180CrossRefGoogle Scholar
  36. 36.
    Stucki, R.A., Urwyler, P., Rampa, L., Muri, R., Mosimann, U.P., Nef, T.: A web-based non-intrusive ambient system to measure and classify activities of daily living. J. Med. Internet Res. 16(7), e175 (2014).  https://doi.org/10.2196/jmir.3465CrossRefGoogle Scholar
  37. 37.
    Suzuki, R., Otake, S., Isutzu, T., Yoshida, M., Iwaya, T.: Monitoring daily living activities of elderly people in a nursing home using an infrared motion-detection system. Telemed. eHealth 12(2), 146–156 (2006)CrossRefGoogle Scholar
  38. 38.
    Teh, R.C., Mahajan, N., Visvanathan, R., Wilson, A.: Clinical effectiveness of and attitudes and beliefs of health professionals towards the use of health technology in falls prevention among older adults. Int. J. Evid. Based Healthcare 13(4), 213–223 (2015).  https://doi.org/10.1097/xeb.0000000000000029CrossRefGoogle Scholar
  39. 39.
    Teh, R.C., Mahajan, N., Visvanathan, R., Ranasinghe, D., Wilson, A.: Evaluation and refinement of a handheld health information technology tool to support the timely update of bedside visual cues to prevent falls in hospitals. Int. J. Evidence Based Healthcare 15 (2017)Google Scholar
  40. 40.
    Tinetti, M., Kumar, C.: The patient who falls: “It’s always a trade-off”. JAMA 303, 258–266 (2010)CrossRefGoogle Scholar
  41. 41.
    Tovell, A., Harrison, J.E., Pointer, S.: Hospitalised Injury in Older Australians, 2011–12. Injury Research and Statistics Series No. 90. Cat. No. INJCAT 166. AIHW, Canberra (2014)Google Scholar
  42. 42.
    Vandenberg, A.E., van Beijnum, B.-J., Overdevest, V.G.P., Capezuti, E., Johnson Ii, T.M.: US and Dutch nurse experiences with fall prevention technology within nursing home environment and workflow: A qualitative study. Geriatr. Nurs. (2016).  https://doi.org/10.1016/j.gerinurse.2016.11.005CrossRefGoogle Scholar
  43. 43.
    Whitney, J., Close, J.C.T., Lord, S.R., Jackson, S.H.D.: Identification of high risk fallers among older people living in residential care facilities: a simple screen based on easily collectable measures. Arch. Gerontol. Geriatr. 55, 690–695 (2012)CrossRefGoogle Scholar
  44. 44.
    Wong Shee, A., 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(3), 253–262 (2014).  https://doi.org/10.1097/ncq.0000000000000054CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ann Borda
    • 1
    Email author
  • Cathy Said
    • 2
  • Cecily Gilbert
    • 1
  • Frank Smolenaers
    • 1
    • 3
  • Michael McGrath
    • 4
  • Kathleen Gray
    • 1
  1. 1.Melbourne Medical SchoolHealth and Biomedical Informatics Centre, University of Melbourne (FMDHS)MelbourneAustralia
  2. 2.Director of Physiotherapy Research, Austin Health/PhysiotherapyUniversity of Melbourne (FMDHS)MelbourneAustralia
  3. 3.Australian Centre for Health Innovation, Alfred HealthMelbourneAustralia
  4. 4.Semantrix Pty Ltd.MelbourneAustralia

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