Title: Characterising leg-dominance in healthy netballers using 3D kinematics-electromyography features' integration and machine learning techniques

Authors: Umar Yahya; S.M.N. Arosha Senanayake; Abdul Ghani Naim

Addresses: Motion Analysis Lab, Integrated Science Building, Brunei; Mathematical and Computing Sciences, Faculty of Science, Universiti Brunei Darussalam, BE1410 Jalan Tungku Link Gadong, Bandar Seri Begawan, Darussalam, Brunei ' Motion Analysis Lab, Integrated Science Building, Brunei; Mathematical and Computing Sciences, Faculty of Science, Universiti Brunei Darussalam, BE1410 Jalan Tungku Link Gadong, Bandar Seri Begawan, Darussalam, Brunei ' Mathematical and Computing Sciences, Faculty of Science, Universiti Brunei Darussalam, BE1410 Jalan Tungku Link Gadong, Bandar Seri Begawan, Darussalam, Brunei

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.

Keywords: leg dominance; netball; machine learning; surface EMG; sEMG; 3D-kinematics; single-leg jump; dominant leg; non-dominant leg; nDL; lower extremity; functional asymmetry; support vector machine; SVM; artificial neural network; ANN; hierarchical clustering; principal component analysis; PCA.

DOI: 10.1504/IJBET.2022.123259

International Journal of Biomedical Engineering and Technology, 2022 Vol.39 No.1, pp.65 - 92

Received: 30 Nov 2018
Accepted: 04 Apr 2019
Published online: 07 Jun 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article