title: A simple method to choose the most representative stride and detect outliers.
abstract: Kinematic data for gait analysis consists of joint angle curves plotted against percentages of the gait cycle. A typical gait analysis entails repeated measurement of the kinematic data. We present an automatic and computationally inexpensive method to choose the most representative curve and detect outliers amongst repeated curves. The method is based on the notion of depth, where the deepest curve is the equivalent to the median for univariate data. The method applies to single kinematic variable or multi-kinematic variables such as the gait profile. It is sensitive to both shape and position of the curves. A comparison with an existing statistical method is presented as well as an example on one patient's data.
authors.affiliation: The Royal Children's Hospital, Australia; The Murdoch Childrens Research Institute, Australia; The University of Melbourne, School of Engineering, Australia. Electronic address: morgan.sangeux@gmail.com.
authors.forename: Morgan
authors.initials: M
authors.lastname: Sangeux
authors.affiliation: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Australia; The Victorian Centre for Biostatistics (ViCBiostat), Australia; Department of Econometrics & Business Statistics, Monash University, Australia.
authors.forename: Julia
authors.initials: J
authors.lastname: Polak
mesh_terms: D001696:Biomechanical Phenomena; D005684:Gait / Q000502:physiology*; D006801:Humans; D007596:Joints / Q000502:physiology*; D015233:Models, Statistical
keywords: Functional data; Kinematics; Outliers; Representative stride