Principal components analysis as an evaluation and classification tool for lower torso sEMG data.

Miguel A Perez; Maury A Nussbaum

doi:10.1016/s0021-9290(03)00090-3

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Principal components analysis as an evaluation and classification tool for lower torso sEMG data.

Miguel A Perez·Maury A Nussbaum

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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.

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Abdominal Muscles Back Electromyography Humans Lumbar Vertebrae Muscle Contraction Muscle, Skeletal Physical Exertion Postural Balance Principal Component Analysis Regression Analysis Evaluation Study Journal Article Validation Study

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Miguel A Perez, Maury A Nussbaum. 2003. Principal components analysis as an evaluation and classification tool for lower torso sEMG data.. https://doi.org/10.1016/s0021-9290(03)00090-3

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