Methods for the real-world evaluation of fall detection technology : a scoping review

Broadley, RW ORCID:, Klenk, J, Thies, SBA ORCID:, Kenney, LPJ ORCID: and Granat, MH ORCID: 2018, 'Methods for the real-world evaluation of fall detection technology : a scoping review' , Sensors, 18 (7) .

PDF - Published Version
Available under License Creative Commons Attribution 4.0.

Download (441kB) | Preview


Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-three articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in it’s infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures which depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems.

Item Type: Article
Schools: Schools > School of Health and Society > Centre for Health Sciences Research
Journal or Publication Title: Sensors
Publisher: MDPI
ISSN: 1424-8220
Related URLs:
Depositing User: MH Granat
Date Deposited: 26 Jun 2018 11:26
Last Modified: 15 Feb 2022 23:25

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)