title: Principal components analysis as an evaluation and classification tool for lower torso sEMG data.
abstract: The use of univariate statistical techniques on multivariate electromyography data can fail to uncover important relationships between variables. Principal components analysis (PCA) is a multivariate statistical technique that can be used as a data exploration tool, both by classifying participants and simplifying data structures. Past research using this technique has focused on discriminating between "patients" and "normals". This investigation explored the use of PCA on electromyography data from healthy participants, with the objective of elucidating any between-participant differences in the multivariate patterns of muscle coactivation. Results indicated that, even between healthy participants, quantitative and qualitative differences in muscle coactivation patterns exist and that, in the context of the lower torso, a large portion (>70%) of the empirically determined muscle activation could be synthesized in a theoretical three-parameter control model.
authors.affiliation: Grado Department of Industrial & Systems Engineering, Virginia Polytechnic Institute, Virginia Tech. State University, 250 Durham Hall (0118), Blacksburg, VA 24061, USA.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Perez
authors.forename: Maury A
authors.initials: MA
authors.lastname: Nussbaum
mesh_terms: D000009:Abdominal Muscles / Q000502:physiology; D001415:Back / Q000502:physiology; D004576:Electromyography / Q000145:classification* / Q000379:methods*; D006801:Humans; D008159:Lumbar Vertebrae / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D005082:Physical Exertion / Q000502:physiology; D004856:Postural Balance / Q000502:physiology*; D025341:Principal Component Analysis / Q000379:methods*; D012044:Regression Analysis