On Nonlinear Regression for Trends in Split-belt Treadmill Training

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aut.relation.journalComputer Methods and Programs in Biomedicineen_NZ
aut.researcherSignal, Nada
dc.contributor.authorRashid, Uen_NZ
dc.contributor.authorKumari, Nen_NZ
dc.contributor.authorSignal, Nen_NZ
dc.contributor.authorTaylor, Den_NZ
dc.contributor.authorVandal, Aen_NZ
dc.date.accessioned2020-10-27T22:08:34Z
dc.date.available2020-10-27T22:08:34Z
dc.date.copyright2020-07-31en_NZ
dc.date.issued2020-07-31en_NZ
dc.description.abstractSingle and double exponential models fitted to step length symmetry series are used to evaluate the timecourse of adaptation and de-adaptation in instrumented split-belt treadmill tasks. Whilst the nonlinear regression literature has developed substantially over time, the split-belt treadmill training literature has not been fully utilising the fruits of these developments. In this research area, the current methods of model fitting and evaluation have three significant limitations: (i) optimisation algorithms that are used for model fitting require a good initial guess for regression parameters; (ii) the coefficient of determination (R2) is used for comparing and evaluating models, yet it is considered to be an inadequate measure of fit for nonlinear regression; and, (iii) inference is based on comparison of the confidence intervals for the regression parameters that are obtained under the untested assumption that the nonlinear model has a good linear approximation. In this research, we propose a transformed set of parameters with a common language interpretation that is relevant to split-belt treadmill training for both the single and double exponential models. We propose parameter bounds for the exponential models which allow the use of particle swarm optimisation for model fitting without an initial guess for the regression parameters. For model evaluation and comparison, we propose the use of residual plots and Akaike’s information criterion (AIC). A method for obtaining confidence intervals that does not require the assumption of a good linear approximation is also suggested. A set of MATLAB (MathWorks, Inc., Natick, MA, USA) functions developed in order to apply these methods are also presented. Single and double exponential models are fitted to both the group-averaged and participant step length symmetry series in an experimental dataset generating new insights into split-belt treadmill training. The proposed methods may be useful for research involving analysis of gait symmetry with instrumented split-belt treadmills. Moreover, the demonstration of the suggested statistical methods on an experimental dataset may help the uptake of these methods by a wider community of researchers that are interested in timecourse of motor training.
dc.identifier.citationBrain Sciences, 10(10), 737.
dc.identifier.doi10.3390/brainsci10100737
dc.identifier.issn0169-2607en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13750
dc.publisherMDPIen_NZ
dc.relation.urihttps://www.mdpi.com/2076-3425/10/10/737en_NZ
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectMotor training; gait analysis; Split-belt treadmill; Step length symmetry; Nonlinear regression; Particle swarm optimisation (PSO); Akaike’s information criterion (AIC)
dc.titleOn Nonlinear Regression for Trends in Split-belt Treadmill Trainingen_NZ
dc.typeJournal Article
pubs.elements-id385390
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Health & Environmental Science
pubs.organisational-data/AUT/Health & Environmental Science/Clinical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HH Clinical Sciences 2018 PBRF
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