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Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses’ Perceptions and Lessons Learned

Published online by Cambridge University Press:  03 July 2018

Marge Coahran ,
Loretta M. Hillier ,
Lisa Van Bussel ,
Edward Black ,
Rebekah Churchyard ,
Iris Gutmanis ,
Yani Ioannou ,
Kathleen Michael ,
Tom Ross  and
Alex Mihailidis
Marge Coahran*
Affiliation:
Toronto Rehabilitation Institute, Toronto
Loretta M. Hillier
Affiliation:
St. Joseph’s Health Care London, Parkwood Institute, Ontario
Lisa Van Bussel
Affiliation:
St. Joseph’s Health Care London, Parkwood Institute, Ontario
Edward Black
Affiliation:
St. Joseph’s Health Care London, Parkwood Institute, Ontario
Rebekah Churchyard
Affiliation:
University of Toronto, Factor-Inwentash Faculty of Social Work
Iris Gutmanis
Affiliation:
Lawson Health Research Institute, London, Ontario
Yani Ioannou
Affiliation:
University of Cambridge, Machine Intelligence Lab, Cambridge, UK
Kathleen Michael
Affiliation:
St. Joseph’s Health Care London, Parkwood Institute, Ontario
Tom Ross
Affiliation:
St. Joseph’s Health Care London, Parkwood Institute, Ontario
Alex Mihailidis
Affiliation:
University of Toronto, Dept. of Occupational Science & Occupational Therapy / Toronto Rehabilitation Institute – University Health Network
*
†La correspondance et les demandes de tirés-à-part doivent être adressées à : / Correspondence and requests for offprints should be sent to: Marge Coahran, MSc Toronto Rehabilitation Institute 550 University Avenue, Room 12-019 Toronto, ON, M5G2A2 <mcoahran@dgp.toronto.edu>
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Abstract

Hospitalized older adults are at high risk of falling. The HELPER system is a ceiling-mounted fall detection system that sends an alert to a smartphone when a fall is detected. This article describes the performance of the HELPER system, which was pilot tested in a geriatric mental health hospital. The system’s accuracy in detecting falls was measured against the hospital records documenting falls. Following the pilot test, nurses were interviewed regarding their perceptions of this technology. In this study, the HELPER system missed one documented fall but detected four falls that were not documented. Although sensitivity (.80) of the system was high, numerous false alarms brought down positive predictive value (.01). Interviews with nurses provided valuable insights based on the operation of the technology in a real environment; these and other lessons learned will be particularly valuable to engineers developing this and other health and social care technologies.

Résumé

Les personnes âgées hospitalisées présentent un haut risque de chute. Le système HELPER est un système de détection des chutes fixé au plafond qui envoie une alerte à un téléphone intelligent lorsqu’une chute est détectée. Cet article décrit la performance du système HELPER, qui a été testé dans un projet pilote mené dans un centre de santé mentale gériatrique. La précision du système pour la détection des chutes a été comparée aux données de l’hôpital liées à la documentation des chutes. Au terme du projet pilote, le personnel infirmier a été interviewé afin de documenter comment cette technologie était perçue. Dans cette étude, le système HELPER n’a pas permis de détecter une chute qui a été documentée par le personnel, mais en a détecté 4 autres qui n’avaient pas été documentées. Bien que la sensibilité du système soit élevée (0.80), les fausses alarmes qu’il génère diminuent sa valeur prédictive (0.01). Les entrevues avec le personnel infirmier ont permis de recueillir plusieurs informations utiles liées au fonctionnement de cette technologie dans un environnement réel; ces données seront utiles aux ingénieurs travaillant sur de tels systèmes et sur des technologies associées aux soins de santé et aux services sociaux.

Keywords

agingfall detectiontechnologynursinggeriatric psychiatryinpatient carevieillissementdétection des chutestechnologiesoins infirmierspsychiatrie gériatriquesoins hospitaliers
Type
Article
Information
Canadian Journal on Aging / La Revue canadienne du vieillissement , Volume 37 , Issue 3 , September 2018 , pp. 245 - 260
Copyright
Copyright © Canadian Association on Gerontology 2018 

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Footnotes

*

This work would not have been possible without the contributions of several research staff members including Amer Burhan, Colin Harry, Leander Pereira, Luli Pallaveshi, and Bing Ye. The authors are immeasurably indebted to the study participants, both nursing staff and patients, at the geriatric mental health hospital where the system was deployed. This work has been possible through support from the St. Joseph Healthcare London President’s Grants for Innovation, the Academic Medical Organization of Southwestern Ontario Opportunities Fund, and the Ontario Centres of Excellence Market Readiness Program. It has been strengthened by insightful comments from the reviewers.

References

Abreu, C., Mendes, A., Monteiro, J., & Santos, F. R. (2012). Falls in hospital settings: A longitudinal study. Revista Latino-Americano de Enfermagem, 20(3), 597–603.CrossRefGoogle ScholarPubMed
Bates, D. W., Pruess, K., Souney, P., & Platt, R. (1995). Serious falls in hospitalized patients: Correlates and resource utilization. American Journal of Medicine, 99, 137–143.CrossRefGoogle ScholarPubMed
Belshaw, M., Taati, B., Giesbrecht, D., & Mihailidis, A. (2011, May). Intelligent vision-based fall detection system: Preliminary results from a real-world deployment. Paper presented at the annual meeting of the Rehabilitation Engineering and Assistive Technology Society of North America (RESNA), Toronto, ON.Google Scholar
Belshaw, M., Taati, B., Snoek, J., & Mihailidis, A. (2011). Towards a single sensor passive solution for automated fall detection. Conference Proceedings Annual International Conference of the IEEE Engineering in Medical and Biology Society, 2011, 1773–1776.Google ScholarPubMed
Bergstrom, N., Braden, B. J., Laguzza, A., & Holman, V. (1987). The Braden scale for predicting pressure sore risk. Nursing Research, 36, 205–210.CrossRefGoogle ScholarPubMed
Blair, E., & Gruman, C. (2005). Falls in an inpatient geriatric psychiatric population. Journal of the American Psychiatric Nurses Association, 11(6), 351–354.CrossRefGoogle Scholar
Bonner, A. F. (2006). Falling in place: A practical approach to interdisciplinary education on falls prevention in long-term care. Annals of Long-Term Care, 14, 21–29.Google Scholar
Bouldin, E. L., Andresen, E. M., Dunton, N. E., Simon, M., Waters, T. M., Liu, M., … Shorr, R. I. (2013). Falls among adult patients hospitalized in the United States: Prevalence and trends. Journal of Patient Safety, 9, 13–17.Google ScholarPubMed
Brand, C. A., & Sundararajan, V. (2010). A 10-year cohort study of the burden and risk of in-hospital falls and fractures using routinely collected hospital data. Quality and Safety in Health Care, 19, e51.Google ScholarPubMed
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research, 3, 101.Google Scholar
Centres for Disease Control and Prevention (2015). Cost of falls among older adults. Retrieved from http://www.cdc.gov/homeandrecreationalsafety/falls/fallcost.htmlGoogle Scholar
Chua, J., Chang, Y. C., & Lim, W. K. (2015). A simple vision-based fall detection technique for indoor video surveillance. Signal, Image and Video Processing, 9, 623–633.CrossRefGoogle Scholar
Cucchiara, R., Prati, A., & Vezzani, R. (2007). A multi-camera vision system for fall detection and alarm generation. Expert Systems, 24, 334–345.CrossRefGoogle Scholar
Cumming, R. G., Sherrington, C., Lord, S. R., Simpson, J. M., Vogler, C., Cameron, I. D., & Naganathan, V. (2008). Cluster randomised trial of a targeted multifactorial intervention to prevent falls among older people in hospital. British Medical Journal, 336, 758–760.CrossRefGoogle ScholarPubMed
Debard, G., Mertens, M., Deschodt, M., Vlaeyen, E., Devriendt, E., Dejaeger, E., … Venrumste, B. (2016). Camera-based fall detection using real-world versus simulated data: How far are we from the solution? Journal of Ambient Intelligence and Smart Environments, 8(2), 149–168.CrossRefGoogle Scholar
Diduszyn, J., Hofmann, M. T., Naglak, M., & Smith, D. G. (2008). Use of a wireless nurse alert fall monitor to prevent inpatient falls. Journal of Clinical Outcomes Management, 15, 293–296.Google Scholar
Edelberg, H. K. (2001). Falls and function. How to prevent falls and injuries in patients with impaired mobility. Geriatrics, 56, 41–45.Google ScholarPubMed
Enloe, M., Wells, T. J., Mahoney, J., Pak, M., Gangnon, R. E., Pellino, T. A., … Leahy-Gross, K. (2005). Falls in acute care: An academic medical centre six-year review. Journal of Patient Safety, 1, 208–214.CrossRefGoogle Scholar
Feng, W., Liu, R., & Zhu, M. (2014). Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera. Signal, Image and Video Processing, 8, 1129–1138.CrossRefGoogle Scholar
Gietzelt, M., Spechr, J., Ehmen, Y., Wegel, S., Feldwieser, F., Meis, M., … Gövercin, M. (2012). GAL@Home: A feasibility study of sensor-based in-home fall detection. Zeitschrift für Gerontologie und Geriatrie, 45, 716–721.CrossRefGoogle ScholarPubMed
Gulliksen, J., Goransson, B., Boivie, I., Blomkvist, S., Persson, J., & Cajander, A. (2003). Key principles for user-centered systems design. Behaviour and Information Technology, 22, 397–409.CrossRefGoogle Scholar
Habib, M. A., Mohktar, M. S., Kamaruzzaman, S. B., Lim, K. S., Pin, T. M., & Ibrahim, F. (2014). Smartphone-based solutions for fall detection and prevention: Challenges and open issues. Sensors. (Basel), 14, 7181–7208.CrossRefGoogle ScholarPubMed
Hitcho, E. B., Krauss, M. J., Birge, S., Dunagan, W. C., Fischer, I., Johnson, S., … Fraser, V. J. (2004). Characteristics and circumstances of falls in a hospital setting. A prospective analysis. Journal of General Internal Medicine, 19, 732–739.CrossRefGoogle Scholar
Hubbartt, B., Davis, S. G., & Kautz, D. D. (2011). Nurses’ experiences with bed exit alarms may lead to ambivalence about their effectiveness. Rehabilitation Nursing, 36, 196–199.CrossRefGoogle ScholarPubMed
Igual, R., Medrano, C., & Plaza, I. (2013). Challenges, issues and trends in fall detection systems. BioMedical Engineering OnLine, 12, 66.CrossRefGoogle ScholarPubMed
Institute of Medicine (US) Division of Health Promotion and Disease Prevention (1992). Falls in older persons: Risk factors and prevention. In Berg, R. L. & Cassells, J. S. (Eds.), The second fifty years: Promoting health and preventing disability. Washington, DC: National Academies Press. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK235613/Google Scholar
Kallin, K., Jensen, J., Olsson, L. L., Nyberg, L., & Gustafson, Y. (2004). Why the elderly fall in residential care facilities, and suggested remedies. Journal of Family Practice, 53, 41–52.Google ScholarPubMed
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jamsa, T. (2015). Sensitivity and false alarm rate of a fall sensor in long-term fall detection in the elderly. Gerontology, 61, 61–68.CrossRefGoogle ScholarPubMed
Kelly, K. E., Phillips, C. L., Cain, K. C., Polissar, N. L., & Kelly, P. B. (2002). Evaluation of a nonintrusive monitor to reduce falls in nursing home patients. Journal of the American Medical Directors Association, 3, 377–382.CrossRefGoogle ScholarPubMed
Kepski, M., & Kwolek, B. (2014). Fall detection using ceiling-mounted 3D depth camera. 2014 International Conference on Computer Vision Theory and Applications (VISAPP), 2, 640–647.Google Scholar
Kosse, N. M., Brands, K., Bauer, J. M., Hortobagyi, T., & Lamoth, C. J. (2013). Sensor technologies aiming at fall prevention in institutionalized old adults: A synthesis of current knowledge. International Journal of Medical Informatics, 82, 743–752.CrossRefGoogle ScholarPubMed
Kwok, T., Mok, F., Chien, W. T., & Tam, E. (2006). Does access to bed-chair pressure sensors reduce physical restraint use in the rehabilitative care setting? Journal of Clinical Nursing, 15, 581–587.CrossRefGoogle ScholarPubMed
Lee, T., & Mihailidis, A. (2005). An intelligent emergency response system: Preliminary development and testing of automated fall detection. Journal of Telemedicine and Telecare, 11, 194–198.CrossRefGoogle ScholarPubMed
Li, Y., Ho, K. C., & Popescu, M. (2012). A microphone array system for automatic fall detection. IEEE Transactions on Biomedical Engineering, 59, 1291–1301.Google ScholarPubMed
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.Google Scholar
Lu, E. C., Wang, R., Huq, R., Gardner, D., Karam, P., Zabjek, K., … Mihailidis, A. (2011). Development of a robotic device for upper limb stroke rehabilitation: A user-centered design approach. Journal of Behavioral Robotics, 2, 176–784.Google Scholar
Mastorakis, G., & Makris, D. (2014). Fall detection system using Kinect’s infrared sensor. Journal of Real-Time Image Processing, 9, 635–646.CrossRefGoogle Scholar
Menant, J. C., Steele, J. R., Menz, H. B., Munro, B. J., & Lord, S. R. (2008). Optimizing footwear for older people at risk of falls. Journal of Rehabilitation Research and Development, 45, 1167–1181.CrossRefGoogle ScholarPubMed
Mihailidis, A., Giesbrecht, D, Hoey, J., Lee, T., Young, V., Hamill, M., … Taati, B. (2011). U.S. Patent No. 8.063,764. Washington, DC: U.S. Patent and Trademark Office.Google Scholar
Mirmahboub, B., Samavi, S., Karimi, N., & Shirani, S. (2013). Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Transactional Biomedical Engineering, 60, 427–436.CrossRefGoogle ScholarPubMed
Morse, J. M., Black, C., Oberle, K., & Donahue, P. (1989). A prospective study to identify the fall-prone patient. Social Science and Medicine, 28, 81–86.CrossRefGoogle ScholarPubMed
Morton, D. (1989). Five years of fewer falls. American Journal of Nursing, 89, 204–205.Google ScholarPubMed
Planinc, R., & Kampel, M. (2013). Introducing the use of depth data for fall detection. Personal and Ubiquitous Computing, 17, 1063–1072.CrossRefGoogle Scholar
Public Health Agency of Canada (2005). Report on seniors’ falls in Canada. Ottawa, ON: Division of Aging and Seniors, Author. http://publications.gc.ca/collections/Collection/HP25-1-2005E.pdfGoogle Scholar
Rantz, M. J., Banerjee, T. S., Cattoor, E., Scott, S. D., Skubic, M., & Popescu, M. (2014). Automated fall detection with quality improvement “rewind” to reduce falls in hospital rooms. Journal of Gerontological Nursing, 40, 13–17.CrossRefGoogle ScholarPubMed
Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2011). Robust video surveillance for fall detection based on human shape deformation. IEEE Transactions on Circuits and Systems for Video Technology, 21, 611–622.CrossRefGoogle Scholar
Rubenstein, L. Z., & Josephson, K. R. (2002). The epidemiology of falls and syncope. Clinics in Geriatric Medicine, 18, 141–158.CrossRefGoogle ScholarPubMed
Shorr, R. I., Chandler, A. M., Mion, L. C., Waters, T. M., Liu, M., Daniels, M. J., … Miller, S. T. (2012). Effects of an intervention to increase bed alarm use to prevent falls in hospitalized patients: A cluster randomized trial. Annals of Internal Medicine, 157, 692–699.CrossRefGoogle ScholarPubMed
Skubic, M., Harris, B. H., Stone, E., Ho, K. C., Su, B., & Rantz, M. (2016). Testing non-wearable fall detection methods in the homes of older adults. Proceedings IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (pp. 557–560). doi: 10.1109/EMBC.2016.7590763Google ScholarPubMed
SMARTRISK. (2009). The economic burden of injury in Canada. Toronto, ON: SMARTRISK. Retrieved from http://www.parachutecanada.org/downloads/research/reports/EBI2009-Eng-Final.pdf.Google Scholar
Stalenhoef, P. A., Diederiks, J. P., Knottnerus, J. A., Kester, A. D., & Crebolder, H. F. (2002). A risk model for the prediction of recurrent falls in community-dwelling elderly: A prospective cohort study. Journal of Clinical Epidemiology, 55, 1088–1094.CrossRefGoogle ScholarPubMed
Stevens, J. A., Corso, P. S., Finkelstein, E. A., & Miller, T. R. (2006). The costs of fatal and non-fatal falls among older adults. Injury Prevention, 12, 290–295.CrossRefGoogle ScholarPubMed
Stone, E. E., & Skubic, M. (2015). Fall detection in homes of older adults using the Microsoft Kinect. IEEE Journal on Biomedical and Health Informatics, 19, 290–301.CrossRefGoogle ScholarPubMed
Strauss, A., & Corbin, J. (1998). Basics of qualitative research. Thousand Oaks, CA: Sage.Google Scholar
Tängman, S., Eriksson, S., Gustafson, Y., & Lundin-Olsson, L. (2010). Precipitating factors for falls among patients with dementia on a psychogeriatric ward. International Psychogeriatrics, 22, 641–649.CrossRefGoogle ScholarPubMed
Tideiksaar, R., Feiner, C. F., & Maby, J. (1993). Falls prevention: The efficacy of a bed alarm system in an acute-care setting. Mount Sinai Journal of Medicine, 60, 522–527.Google Scholar
Tiedemann, A. C., Murray, S. M., Munro, B., & Lord, S. R. (2008). Hospital and non-hospital costs for fall-related injury in community-dwelling older people. New South Wales Public Health Bulletin, 19, 161–165.CrossRefGoogle ScholarPubMed
Tinetti, M. E. (2003). Clinical practice. Preventing falls in elderly persons. New England Journal of Medicine, 348, 42–49.CrossRefGoogle ScholarPubMed
Tzeng, H. W., Chen, M. Y., & Chen, M. Y. (2010). Design of fall detection system with floor pressure and infrared image. Proceedings of the 2010 International Conference on System Science and Engineering, 131–135. doi: 10.1109/ICSSE.2010.5551751CrossRefGoogle Scholar
Vassallo, M., Vignaraja, R., Sharma, J. C., Briggs, R., & Allen, S. C. (2004). Predictors for falls among hospital inpatients with impaired mobility. Journal of the Royal Society of Medicine, 97, 266–269.CrossRefGoogle ScholarPubMed
Volkhardt, M., Schneemann, F., & Gross, H. M. (2013). Fallen person detection for mobile robots using 3D depth data. Proceedings 2013 IEEE International Conference on Systems, Man, and Cybernetics, 3573–3578. doi: 10.1109/SMC.2013.609CrossRefGoogle Scholar
Widder, B. (1985). A new device to decrease falls. Geriatric Nursing, 6, 287–288.CrossRefGoogle ScholarPubMed
World Health Organization (2007). WHO global report on falls prevention in older age. Retrieved from http://www.who.int/ageing/publications/Falls_prevention7March.pdfGoogle Scholar
Zhang, Z., Conly, C., & Athitsos, V. (2014). Evaluating depth-based computer vision methods for fall detection under occlusions. Advances in Visual Computing, 8888, 196–207.Google Scholar