title: Online learning of gait models for calculation of gait parameters.
abstract: This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intra-personal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters. The approach is implemented and tested on a human motion dataset where heel impact and toe takeoff events are extracted with an average error of 0.04 cycles.
authors.forename: Jamie L S
authors.initials: JL
authors.lastname: Waugh
authors.forename: Anton
authors.initials: A
authors.lastname: Trinh
authors.forename: Ryan R
authors.initials: RR
authors.lastname: Mohammed
authors.forename: William E
authors.initials: WE
authors.lastname: McIlroy
authors.forename: Dana
authors.initials: D
authors.lastname: Kulic
mesh_terms: D000465:Algorithms; D005684:Gait / Q000502:physiology*; D006365:Heel; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D059015:Wireless Technology