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

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Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 170))

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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.

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Notes

  1. 1.

    Australian and New Zealand Falls Prevention Society. http://www.anzfallsprevention.org/info/. Accessed 1 May 2018.

  2. 2.

    The full version of the taxonomy and the accompanying handbook are available on the FARSEEING project website: farseeingresearch.eu. The online application can be viewed at http://taxonomy.farseeingresearch.eu.

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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.

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Authors and Affiliations

  1. Melbourne Medical School, Health and Biomedical Informatics Centre, University of Melbourne (FMDHS), Melbourne, Australia

    Ann Borda, Cecily Gilbert, Frank Smolenaers & Kathleen Gray

  2. Director of Physiotherapy Research, Austin Health/Physiotherapy, University of Melbourne (FMDHS), Melbourne, Australia

    Cathy Said

  3. Australian Centre for Health Innovation, Alfred Health, Melbourne, Australia

    Frank Smolenaers

  4. Semantrix Pty Ltd., Melbourne, Australia

    Michael McGrath

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  1. Ann Borda
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Correspondence to Ann Borda .

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Editors and Affiliations

  1. Faculty of Medical Bioengineering, University of Medicine and Pharmacy, Iași, Romania

    Hariton Costin

  2. University of Augsburg, Augsburg, Germany

    Björn Schuller

  3. Faculty of Automatic Control and Computers, University Polytechnica of Bucharest, Bucharest, Romania

    Adina Magda Florea

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Borda, A., Said, C., Gilbert, C., Smolenaers, F., McGrath, M., Gray, K. (2020). Feasibility of Non-contact Smart Sensor-Based Falls Detection in a Residential Aged Care Environment. In: Costin, H., Schuller, B., Florea, A. (eds) Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications. Intelligent Systems Reference Library, vol 170. Springer, Cham. https://doi.org/10.1007/978-3-030-30817-9_7

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