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  • Title: Development of a standard fall data format for signals from body-worn sensors : the FARSEEING consensus.
    Author: Klenk J, Chiari L, Helbostad JL, Zijlstra W, Aminian K, Todd C, Bandinelli S, Kerse N, Schwickert L, Mellone S, Bagalá F, Delbaere K, Hauer K, Redmond SJ, Robinovitch S, Aziz O, Schwenk M, Zecevic A, Zieschang T, Becker C, FARSEEING Consortium and the FARSEEING Meta-Database Consensus Group.
    Journal: Z Gerontol Geriatr; 2013 Dec; 46(8):720-6. PubMed ID: 24271252.
    Abstract:
    Objective measurement of real-world fall events by using body-worn sensor devices can improve the understanding of falls in older people and enable new technology to prevent, predict, and automatically recognize falls. However, these events are rare and hence challenging to capture. The FARSEEING (FAll Repository for the design of Smart and sElf-adapaive Environments prolonging INdependent livinG) consortium and associated partners strongly argue that a sufficient dataset of real-world falls can only be acquired through a collaboration of many research groups. Therefore, the major aim of the FARSEEING project is to build a meta-database of real-world falls. To establish this meta-database, standardization of data is necessary to make it possible to combine different sources for analysis and to guarantee data quality. A consensus process was started in January 2012 to propose a standard fall data format, involving 40 experts from different countries and different disciplines working in the field of fall recording and fall prevention. During a web-based Delphi process, possible variables to describe participants, falls, and fall signals were collected and rated by the experts. The summarized results were presented and finally discussed during a workshop at the 20th Conference of the International Society of Posture and Gait Research 2012, in Trondheim, Norway. The consensus includes recommendations for a fall definition, fall reporting (including fall reporting frequency, and fall reporting variables), a minimum clinical dataset, a sensor configuration, and variables to describe the signal characteristics.
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