title: Gait quality assessment using self-organising artificial neural networks.
abstract: In this study, the challenge to maximise the potential of gait analysis by employing advanced methods was addressed by using self-organising neural networks to quantify the deviation of patients' gait from normal. Data including three-dimensional joint angles, moments and powers of the two lower limbs and the pelvis were used to train Kohonen artificial neural networks to learn an abstract definition of normal gait. Subsequently, data from patients with gait problems were presented to the network which quantified the quality of gait in the form of a single curve by calculating the quantisation error during the gait cycle. A sensitivity analysis involving the manipulation of gait variables' weighting was able to highlight specific causes of the deviation including the anatomical location and the timing of wrong gait patterns. Use of the quantisation error can be regarded as an extension of previously described gait indices because it measures the goodness of gait and additionally provides information related to the causes underlying gait deviations.
authors.affiliation: Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, L3 2ET, UK. G.J.Barton@livjm.ac.uk
authors.forename: Gabor
authors.initials: G
authors.lastname: Barton
authors.forename: Paulo
authors.initials: P
authors.lastname: Lisboa
authors.forename: Adrian
authors.initials: A
authors.lastname: Lees
authors.forename: Steve
authors.initials: S
authors.lastname: Attfield
mesh_terms: D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D002648:Child; D005684:Gait / Q000502:physiology*; D020233:Gait Disorders, Neurologic / Q000503:physiopathology*; D006801:Humans; D007596:Joints / Q000502:physiology; D035002:Lower Extremity / Q000502:physiology*; D016571:Neural Networks, Computer; D010388:Pelvis / Q000502:physiology; D012016:Reference Values