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Open AccessReview

Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

1
School of Health Sciences, University of Salford, Salford, M6 6PU, UK
2
Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany
3
Institute of Epidemiology and Medical Biometry, Ulm University, 89081 Ulm, Germany
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2060; https://doi.org/10.3390/s18072060
Received: 31 May 2018 / Revised: 18 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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. View Full-Text
Keywords: accidental falls; fall detection; real-world; signal analysis; performance measures; wearable sensors; non-wearable sensors; accelerometers; cameras accidental falls; fall detection; real-world; signal analysis; performance measures; wearable sensors; non-wearable sensors; accelerometers; cameras
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MDPI and ACS Style

Broadley, R.W.; Klenk, J.; Thies, S.B.; Kenney, L.P.J.; Granat, M.H. Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review. Sensors 2018, 18, 2060. https://doi.org/10.3390/s18072060

AMA Style

Broadley RW, Klenk J, Thies SB, Kenney LPJ, Granat MH. Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review. Sensors. 2018; 18(7):2060. https://doi.org/10.3390/s18072060

Chicago/Turabian Style

Broadley, Robert W.; Klenk, Jochen; Thies, Sibylle B.; Kenney, Laurence P.J.; Granat, Malcolm H. 2018. "Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review" Sensors 18, no. 7: 2060. https://doi.org/10.3390/s18072060

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