title: Classification between non-multiple fallers and multiple fallers using a triaxial accelerometry-based system.
abstract: Falls are a prominent problem facing older adults and a common cause of hospitalized injuries. Accurate falls-risk assessment and classification of falls-risk levels will provide useful information for the prevention of future falls. This study presents a triaxial accelerometer (TA) based two-class classifier, which discriminates between multiple fallers and non-multiple fallers, using a directed-routine (DR) movement test. One-hundred-and-twenty-six features were extracted from the accelerometry signals, recorded during the DR tests using a waist mounted TA, from 68 subjects. A linear multiple regression model was employed to map a subset of these features to an estimate of the number of previous falls experienced in the preceding twelve months. A simple threshold is applied to this estimated number of falls to create a basic linear discriminant classifier to separate multiple from non-multiple fallers. The system attained an accuracy of 71% in classifying the exact number of falls experienced in the last 12 months and 97% in identifying multiple fallers.
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
authors.forename: Michael R
authors.initials: MR
authors.lastname: Narayanan
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
mesh_terms: D000054:Acceleration; D000058:Accidental Falls / Q000517:prevention & control* / Q000706:statistics & numerical data; D056044:Actigraphy / Q000295:instrumentation*; D000328:Adult; D000465:Algorithms; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity