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-two 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 its 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 that 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.
Highlights
Falls in older adults and their related consequences pose a major healthcare challenge that is set to grow over the coming decades [1]
Given that a robust evaluation of fall detection systems can be achieved without the need for true negatives, and non-fall or fall-like movements, we suggest that automated fall-like movement detection is unlikely to bring benefits which outweigh the required investment
As focus in fall detection performance evaluation shifts from simulated to real-world fall data, one must consider if the approach used for evaluating on simulations is optimum for real-world data
Summary
Falls in older adults and their related consequences pose a major healthcare challenge that is set to grow over the coming decades [1]. Six percent of older adult falls result in fractured bones [3,4]. Falls are estimated to cost the UK over one billion pounds each year, with fractures being the most costly fall related injury [5]. Even when the injuries are not so serious, fallers often struggle to get up unaided [6,7], sometimes leading to a ‘long-lie’ where the faller remains trapped on the floor for an extended period of time. Further to the physical consequences, the fear of falling can impact on older adults’
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