title: Distinguishing falls from normal ADL using vertical velocity profiles.
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.
authors.affiliation: CAALYX FP6 project, Wireless Access Research Centre, Department of Electronic and Computer Engineering, University of Limerick, Ireland. alan.bourke@ul.ie
authors.forename: Alan K
authors.initials: AK
authors.lastname: Bourke
authors.forename: Karol J
authors.initials: KJ
authors.lastname: O'Donovan
authors.forename: Gearoid M
authors.initials: GM
authors.lastname: OLaighin
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000203:Activities of Daily Living; D000328:Adult; D003198:Computer Simulation; D003937:Diagnosis, Differential; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008954:Models, Biological; D018670:Monitoring, Ambulatory / Q000379:methods*; D009068:Movement / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity