Characterising leg-dominance in healthy netballers using 3D kinematics-electromyography features' integration and machine learning techniques
by Umar Yahya; S.M.N. Arosha Senanayake; Abdul Ghani Naim
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 39, No. 1, 2022

Abstract: The present study utilised machine learning techniques to characterise differences between dominant (DL) and non-dominant (nDL) legs of healthy female netballers during single-leg lateral jump. Electromyography (EMG) activity of eight lower-extremity muscles and three-dimensional motion of the ankle, knee, and hip joints were recorded for both jumping (JL) and landing (LL) legs. Integrated EMG of each muscle and joints' range-of-motion (ROM) in all three planes were computed. Using hierarchical clustering, two subgroups were identified in both feature subsets JL and LL. LL's subgroups exhibited significant differences (p < 0.05) in ROM of all joints in at-least one plane. Support vector machine classifier outperformed artificial neural networks' at recognising DL and nDL patterns in subsets LL and JL with accuracy (F-measure) of 86.21% and 81.36% respectively. These findings suggest DL-nDL differences are more manifested during landing than during jumping, a vital coaches' insight as both legs are alternatingly used during single-leg jump-landing tasks.

Online publication date: Tue, 07-Jun-2022

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