Correia, P. (2015). Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification. Journal of Electromyography & Kinesiology, 25,
AbstractThe quantification of non-linear characteristics of electromyography (EMG) must contain information allowing to discriminate neuromuscular strategies during dynamic skills. In golf, both handicap (Hc) and low back pain (LBP) are main factors associated with the occurrence of injuries. The aim of this study was to analyze the accuracy of support vector machines SVM on EMG-based classification to discriminate Hc (low and high handicap) and LBP prevalence (with and without LPB) in the main phases of golf swing. For this purpose recurrence quantification analysis (RQA) features of the trunk and the lower limb muscles were used to feed a SVM classifier. Recurrence rate (RR) and the ratio between determinism (DET) and RR showed a high discriminant weight. The Hc classifications accuracy for the swing, backswing (BS), and downswing (DS) were 94.4±2.7%, 97.1±2.3%, and 95.3±2.6%, respectively. For LBP, the accuracy was 96.9±3.8% in the swing, and 99.7%±0.4% in BS. External oblique (EO), biceps femoris (BF), semitendinosus (ST) and rectus femoris (RF) showed high accuracy depending on the laterality within the phase. RQA features and SVM showed a high capacity in discriminating muscles within swing phases by Hc and by LBP. Low back pain golfers showed less neuromuscular coordination strategies than asymptomatic.