The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls

  • Jochen Klenk (Contributor)
  • Lars Schwickert (Contributor)
  • Luca Palmerini (Contributor)
  • Sabato Mellone (Contributor)
  • Alan Bourke (Contributor)
  • Espen A F Ihlen (Contributor)
  • Ngaire Kerse (Contributor)
  • Klaus A. Hauer (Contributor)
  • Mirjam Pijnappels (Contributor)
  • Matthis Synofzik (Contributor)
  • Karin Srulijes (Contributor)
  • Walter Maetzler (Contributor)
  • Jorunn Laegdheim Helbostad (Contributor)
  • Wiebren Zijlstra (Contributor)
  • Kamiar Aminian (Contributor)
  • Christopher Todd (Contributor)
  • Lorenzo Chiari (Contributor)
  • Clemens Becker (Contributor)



Abstract Background Real-world fall events objectively measured by body-worn sensors can improve the understanding of fall events in older people. However, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adaptive Environments prolonging Independent livinG) consortium and associated partners started to build up a meta-database of real-world falls. Results Between January 2012 and December 2015 more than 300 real-world fall events have been recorded. This is currently the largest collection of real-world fall data recorded with inertial sensors. A signal processing and fall verification procedure has been developed and applied to the data. Since the end of 2015, 208 verified real-world fall events are available for analyses. The fall events have been recorded within several studies, with different methods, and in different populations. All sensor signals include at least accelerometer measurements and 58 % additionally include gyroscope and magnetometer measurements. The collection of data is ongoing and open to further partners contributing with fall signals. The FARSEEING consortium also aims to share the collected real-world falls data with other researchers on request. Conclusions The FARSEEING meta-database will help to improve the understanding of falls and enable new approaches in fall risk assessment, fall prevention, and fall detection in both aging and disease.
Date made available30 Oct 2016

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