Sergio, Lauren E.Ghani, Sijad2021-11-152021-11-152021-082021-11-15http://hdl.handle.net/10315/38803The application of nonlinear analytical tools to motor control studies is a promising approach. Measuring the complexity of a time-series of kinematic variables to explore motor learning differences allows us to discriminate between groups. The aim of the current study was to test the efficacy of nonlinear analysis, such as approximate entropy (ApEn), to effectively discriminate between elite and non-elite athletes data. Using approximate entropy, we were able to discriminate between elite and non-elite athletes by discerning the level of regularity present in each groups time-series data of kinematic variables. An extension of our entropy analysis in conjunction with other nonlinear analytical tools affords us the possibility to better explore underlying neuromotor effects that may still be present in elite athletes with prior concussion.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.NeurosciencesElectronic Thesis or Dissertation2021-11-15Motor controlMotor learningNonlinearNeuroscienceEliteAnalysisMovementBiomechanicsConcussionSkillNovelSportsHealthyEntropyJerkVelocityApproximate entropyBiological systemsSystems