Simon Taylor
Victoria University, Australia
48 Papers
195 Citations
Simon Taylor is an academic researcher from Victoria University, Australia. The author has contributed to research in topics: Medicine & Gait analysis. The author has an hindex of 12, co-authored 43 publications.
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Papers
Ageing and limb dominance effects on foot-ground clearance during treadmill and overground walking.
TL;DR: The high positive correlation between first maximum and minimum foot-ground clearances suggests that intervention designed to increase first maximum clearance may also increase minimumFoot-ground clearance in older adults may reflect functional asymmetry, in which the non-dominant limb primarily secures or stabilizes gait.
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Investigating Scale Invariant Dynamics in Minimum Toe Clearance Variability of the Young and Elderly During Treadmill Walking
Ahsan H. Khandoker,Simon Taylor,Chandan Karmakar,Rezaul Begg,Marimuthu Palaniswami +4 more
- 16 May 2008
TL;DR: Investigation of the magnitude and dynamic structure from the MTC time series fluctuations due to aging and locomotor disorder showed that stride-to-stride M TC time series has a nonlinear structure in all three groups when compared against randomly shuffled surrogate MTC data.
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A Comparison of Treadmill and Overground Walking Effects on Step Cycle Asymmetry in Young and Older Individuals
TL;DR: Older adults increased the proportion of double support in step time when treadmill walking and this adaptation combined with reduced step velocity and length may preserve balance.
A performance analysis of a wireless body-area network monitoring system for professional cycling
Raluca Marin-Perianu,Mihai Marin-Perianu,Paul J.M. Havinga,Simon Taylor,Rezaul Begg,Marimuthu Palaniswami,David M. Rouffet +6 more
- 01 Jan 2013
TL;DR: A radically different approach that focuses on determining the actual status of the cyclist’s lower limb segments in real-time and real-life conditions is proposed, based on body area wireless motion sensor nodes that can collaboratively process the sensory information and provide the cyclists with immediate feedback about their pedalling movement.
Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines
TL;DR: An intelligent gait detection system for screening elderly individuals at risk of suffering tripping falls and demonstrating a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.
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