Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification

Abstract

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.

Shoulder girdle muscle activity and fatigue during automobile chassis repair

“…It is one of the most effective tools and provides distinct feasibilities to get seasonable information, highly relevant from several ergonomic perspectives [17]. In addition, SEMG is one of the reliable methods applied to quantify muscle fatigue levels and physical exposure [18][19][20]. Surface electromyography signal information can be used in several ways, depending on the analyzed questions.…”

“…Hagberg et al [35,36] believed that it was insufficient to merely apply the surface EMG technique to measure shoulder muscle load. The previous research focused on MSD evolution or ergonomic interventions has emphasized the interactions between the objective and subjective fatigue in connection with occupational activities [1,37,38].…”

“…There were 2 dumbbell weights: 0.48 kg and 0.75 kg, and 4 shoulder postures: 150°/180° (shoulder also commonly applied an average of the peaks obtained from each successive MVC. The numerous normalization procedures reflect the lack of conformity, which makes comparisons between various research studies challenging [36]. The highest peak value recorded for each muscle was used for normalization procedures in this study.…”

“…Figure 2 shows one of the postures. The most common approach is to refer all muscle activity to an %MVE [36], which is a percentage of the maximal voluntary electrical activation [34]. It should be noted that this is only a linear conversion of the original SEMG amplitude parameter (RMS) [29].…”

Quantification of lower extremity physical exposures in various combinations of sit/stand time duration associated with sit-stand workstation

“…In addition, EMG is one of the reliable approaches that is used for quantifying muscle fatigue levels and physical exposure [14][15][16]. The musculoskeletal load was measured by several researchers associated with EMG for analyzing muscular response of upper limb [15][16][17], and for studying the muscle activities of lower limb [18,19].…”