Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification. Issue 4 (August 2015)
- Record Type:
- Journal Article
- Title:
- Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification. Issue 4 (August 2015)
- Main Title:
- Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification
- Authors:
- Silva, Luís
Vaz, João Rocha
Castro, Maria António
Serranho, Pedro
Cabri, Jan
Pezarat-Correia, Pedro - Abstract:
- <abstract xml:lang="en" abstract-type="author" id="ab005"> <title id="st005">Abstract</title> <sec> <p id="sp0005">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<abstract xml:lang="en" abstract-type="author" id="ab005"> <title id="st005">Abstract</title> <sec> <p id="sp0005">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.</p> </sec> </abstract> … (more)
- Is Part Of:
- Journal of electromyography and kinesiology. Volume 25:Issue 4(2015:Aug.)
- Journal:
- Journal of electromyography and kinesiology
- Issue:
- Volume 25:Issue 4(2015:Aug.)
- Issue Display:
- Volume 25, Issue 4 (2015)
- Year:
- 2015
- Volume:
- 25
- Issue:
- 4
- Issue Sort Value:
- 2015-0025-0004-0000
- Page Start:
- 637
- Page End:
- 647
- Publication Date:
- 2015-08
- Subjects:
- Electromyography -- Periodicals
Kinesiology -- Periodicals
Electromyography -- Periodicals
Movement -- physiology -- Periodicals
Muscles -- physiology -- Periodicals
Électromyographie -- Périodiques
Cinésiologie -- Périodiques
Electromyography
Kinesiology
Electronic journals
Periodicals
616.740757 - Journal URLs:
- http://www.sciencedirect.com/science/journal/10506411 ↗
http://www.clinicalkey.com/dura/browse/journalIssue/10506411 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.jelekin.2015.04.008 ↗
- Languages:
- English
- ISSNs:
- 1050-6411
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4974.855000
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