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

Article English OPEN
Bisele, M; Bencsik, M; Lewis, MGC; Barnett, CT;
(2017)
  • Publisher: Public Library of Science
  • Journal: PLoS ONE,volume 12,issue 9 (eissn: 1932-6203)
  • Related identifiers: doi: 10.1371/journal.pone.0183990, pmc: PMC5590884
  • Subject: Applied Mathematics | Algorithms | Research Article | Mathematics | Anatomy | Classical Mechanics | Mathematical and Statistical Techniques | Multivariate Analysis | Artificial Intelligence | Gait Analysis | Simulation and Modeling | Physical Sciences | Knee Joints | Physics | Machine Learning Algorithms | Ankle Joints | Biological Locomotion | Statistics (Mathematics) | Biology and Life Sciences | Computer and Information Sciences | Research and Analysis Methods | Physiology | Musculoskeletal System | Kinematics | Machine Learning | Medicine and Health Sciences | Joints (Anatomy) | Statistical Methods | Principal Component Analysis

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 ... View more
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