Literature DB >> 18002670

Distinguishing falls from normal ADL using vertical velocity profiles.

Alan K Bourke1, Karol J O'Donovan, Gearoid M OLaighin.   

Abstract

This paper describes a technique for distinguishing falls from activities of daily living (ADL) through vertical velocity thresholding (VVT). To verify that VVT can be used to distinguish falls from ADL and to detect falls prior to impact, simulated fall and ADL testing was carried out on five young healthy subjects. Results show that the VVT method can distinguish falls from ADL with 100% accuracy and with an average lead-time of 323ms prior to trunk impact and 140ms prior to knee impact.

Mesh:

Year:  2007        PMID: 18002670     DOI: 10.1109/IEMBS.2007.4353004

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  2 in total

Review 1.  Fall detection devices and their use with older adults: a systematic review.

Authors:  Shomir Chaudhuri; Hilaire Thompson; George Demiris
Journal:  J Geriatr Phys Ther       Date:  2014 Oct-Dec       Impact factor: 3.381

2.  A distributed multiagent system architecture for body area networks applied to healthcare monitoring.

Authors:  Filipe Felisberto; Rosalía Laza; Florentino Fdez-Riverola; António Pereira
Journal:  Biomed Res Int       Date:  2015-03-22       Impact factor: 3.411

  2 in total

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