Aging Clinical and Experimental Research

, Volume 30, Issue 11, pp 1275–1286 | Cite as

Falls management framework for supporting an independent lifestyle for older adults: a systematic review

  • Hoa NguyenEmail author
  • Farhaan Mirza
  • M. Asif Naeem
  • Mirza Mansoor Baig


Falls are one of the common health and well-being issues among the older adults. Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle. This research systematically reviews technological applications related to falls detection and falls management. The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA). Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review. Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 41.67%), environment sensors (10; 41.67%) and few studies used the combination of these types of sensors (4; 16.67%). As an outcome of this review, we postulated a falls management framework (FMF). FMF considered pre- and post-fall strategies to support older adults live independently. A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home. FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults’ independent living and well-being.


Falls detection Falls prediction Falls prevention Falls management Internet of things (IoT) Falls management framework and older adult falls 


Compliance with ethical standards

Conflict of interest

Authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

For this type of study, formal consent is not required.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hoa Nguyen
    • 1
    Email author
  • Farhaan Mirza
    • 1
  • M. Asif Naeem
    • 1
  • Mirza Mansoor Baig
    • 1
  1. 1.School of Engineering, Computer and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand

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