TY - JOUR
T1 - Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification
AU - Silva, Luís
AU - Vaz, João Rocha
AU - Castro, Maria António
AU - Serranho, Pedro
AU - Cabri, Jan
AU - Pezarat-Correia, Pedro
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - The quantification of non-linear characteristics of electromyography (EMG) must contain information allowing to discriminate neuromuscular strategies during dynamic skills. There are a lack of studies about muscle coordination under motor constrains during dynamic contractions. In golf, both handicap (Hc) and low back pain (LBP) are the 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 (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 power. The Hc accuracy for the swing, backswing, and downswing were 94.4 ± 2.7%, 97.1 ± 2.3%, and 95.3 ± 2.6%, respectively. For LBP, the accuracy was 96.9 ± 3.8% for the swing, and 99.7 ± 0.4% in the backswing. 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 muscle discriminant capacity within swing phases by Hc and by LBP. Low back pain golfers showed different neuromuscular coordination strategies when compared with asymptomatic.
AB - The quantification of non-linear characteristics of electromyography (EMG) must contain information allowing to discriminate neuromuscular strategies during dynamic skills. There are a lack of studies about muscle coordination under motor constrains during dynamic contractions. In golf, both handicap (Hc) and low back pain (LBP) are the 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 (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 power. The Hc accuracy for the swing, backswing, and downswing were 94.4 ± 2.7%, 97.1 ± 2.3%, and 95.3 ± 2.6%, respectively. For LBP, the accuracy was 96.9 ± 3.8% for the swing, and 99.7 ± 0.4% in the backswing. 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 muscle discriminant capacity within swing phases by Hc and by LBP. Low back pain golfers showed different neuromuscular coordination strategies when compared with asymptomatic.
KW - Electromyography
KW - Golf
KW - Pattern recognition
KW - RQA
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=84931571321&partnerID=8YFLogxK
U2 - 10.1016/j.jelekin.2015.04.008
DO - 10.1016/j.jelekin.2015.04.008
M3 - Article
C2 - 26027794
AN - SCOPUS:84931571321
SN - 1050-6411
VL - 25
SP - 637
EP - 647
JO - Journal of Electromyography and Kinesiology
JF - Journal of Electromyography and Kinesiology
IS - 4
ER -