Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses

Journal article


Bisele, Maria, Bencsik, Martin, Lewis, Martin and Barnett, Cleveland 2017. Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses. PLos ONE. 12 (9), p. e0183990. https://doi.org/10.1371/journal.pone.0183990
AuthorsBisele, Maria, Bencsik, Martin, Lewis, Martin and Barnett, Cleveland
Abstract

Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg-1) and joint powers (W.kg-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors’ knowledge, this is the first study to optimise the development of a machine learning algorithm.

KeywordsBiomechanics, Machine Learning, Sport
Year2017
JournalPLos ONE
Journal citation12 (9), p. e0183990
PublisherPublic Library of Science (PLoS)
ISSN1932-6203
Digital Object Identifier (DOI)https://doi.org/10.1371/journal.pone.0183990
Web address (URL)http://hdl.handle.net/10545/624287
hdl:10545/624287
Publication dates08 Sep 2017
Publication process dates
Deposited04 Nov 2019, 09:22
Accepted15 Aug 2017
ContributorsNottingham Trent University
File
File Access Level
Open
File
Permalink -

https://repository.derby.ac.uk/item/92vv8/optimisation-of-a-machine-learning-algorithm-in-human-locomotion-using-principal-component-and-discriminant-function-analyses

Download files

  • 2
    total views
  • 1
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as