Improved Screening of Fall Risk using Free-Living based Accelerometer Data

Daniel Kelly, Joan Condell, James Gillespie, Karla Munoz Esquivel, John Barton, S Tedesco, Anna Nordström, M. Åkerlund Larsson, Antti Alamaki

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
64 Downloads (Pure)


Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.
Original languageEnglish
Article number104116
Pages (from-to)1-13
Number of pages13
JournalJournal of Biomedical Informatics
Early online date8 Jun 2022
Publication statusPublished (in print/issue) - 31 Jul 2022

Bibliographical note

Funding Information:
We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175/1.

Funding Information:
This research was funded by the European Union Interreg Northern Periphery and Arctic 2014-2020 program.

Publisher Copyright:
© 2022 The Authors


  • fall risk assessment
  • machine learning
  • Wearable sensors


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