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

Broadley, RW; Klenk, J; Thies, SBA; Kenney, LPJ; Granat, MH

Abstract

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.

Citation

Broadley, R., Klenk, J., Thies, S., Kenney, L., & Granat, M. (2018). Methods for the real-world evaluation of fall detection technology : a scoping review. Sensors, 18(7), https://doi.org/10.3390/s18072060

• Journal Article Type: Article
• Acceptance Date: Jun 25, 2018
• Publication Date: Jun 27, 2018
• Deposit Date: Jun 26, 2018
• Publicly Available Date: Jun 28, 2018
• Journal: Sensors
• Publisher: MDPI
• Volume: 18
• Issue: 7
• DOI: https://doi.org/10.3390/s18072060
• Publisher URL: https://doi.org/10.3390/s18072060
• Related Public URLs: http://www.mdpi.com/journal/sensors