title: Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification.
abstract: 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.
authors.affiliation: Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal. Electronic address: lsilva0a@gmail.com.
authors.forename: Luís
authors.initials: L
authors.lastname: Silva
authors.affiliation: Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
authors.forename: João Rocha
authors.initials: JR
authors.lastname: Vaz
authors.affiliation: Coimbra College of Health Technology, Polytechnic Institute of Coimbra, Portugal.
authors.forename: Maria António
authors.initials: MA
authors.lastname: Castro
authors.affiliation: Departamento de Ciências e Tecnologia, Universidade Aberta, Portugal.
authors.forename: Pedro
authors.initials: P
authors.lastname: Serranho
authors.affiliation: Norwegian School of Sport Sciences, Norway.
authors.forename: Jan
authors.initials: J
authors.lastname: Cabri
authors.affiliation: Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
authors.forename: Pedro
authors.initials: P
authors.lastname: Pezarat-Correia
mesh_terms: D000328:Adult; D000368:Aged; D004576:Electromyography / Q000145:classification* / Q000379:methods; D005069:Evaluation Studies as Topic; D005260:Female; D006055:Golf / Q000502:physiology*; D006801:Humans; D017116:Low Back Pain / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000502:physiology; D060388:Support Vector Machine
keywords: Electromyography; Golf; Pattern recognition; RQA; SVM