Principal component analysis-based muscle identification for myoelectric-controlled exoskeleton knee

I.S. Dhindsa, R. Agarwal and H.S. Ryait

Journal of Applied Statistics, 2017, vol. 44, issue 10, 1707-1720

Abstract: This paper is an attempt to identify a set of muscles which are sufficient to control a myoelectic-controlled exoskeleton knee. A musculoskeletal model of the human body available in the anybody modelling system was scaled to match the subject-specific parameters. It was made to perform a task of sitting in a squat position from a standing position. Internal forces developed in 18 muscles of lower limb during the task were predicted by the inverse dynamic analysis. Principal component analysis was then conducted on the predicted force variable. The eigenvector coefficients of the principal components were evaluated. Significant variables were retained and redundant variables were rejected by the method of principal variable. Subjects were asked to perform the same task of sitting in a squat position from a standing position. Surface-electromyography (sEMG) signals were recorded from the selected muscles. The force developed in the subject's muscles were obtained from the sEMG signals. Force developed in the selected muscle was compared with the force obtained from the musculoskeletal model. A four channel system VastusLateralis, RectusFemoris, Semitendinosus and GluteusMedius and a five channel system VastusLateralis, BicepsFemoris, RectusFemoris, Semitendinosus and GluteusMedius are suitable muscles to control exoskeleton knee.

Date: 2017
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DOI: 10.1080/02664763.2016.1221907

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