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Mallor F.,University of Pamplona | Leon T.,University of Valencia | Gaston M.,University of Pamplona | Izquierdo M.,Research and Sport Medicine Center
Journal of Biomechanics | Year: 2010

The purpose of this study was to analyze exercise-induced leg fatigue during a dynamic fatiguing task by examining the shapes of power vs. time curves through the combined use of several statistical methods: B-spline smoothing, functional principal components and (supervised and unsupervised) classification. In addition, granulometric size distributions were also computed to allow for comparison of curves coming from different subjects. Twelve physically active men participated in one acute heavy-resistance exercise protocol which consisted of five sets of 10 repetition maximum leg press with 120. s of rest between sets. To obtain a smooth and accurate representation of the data, a basis of 180 B-splines was used. Functional principal component (FPC) analysis was used to find the dominant modes of variation in the curves. A multivariate cluster over the FPC scores and a k-nearest neighbor classification led to three interpretable groups corresponding to different levels of fatigue. Fatigue-induced changes in the shapes of the power curves were evident, in which curves progressively flatten and develop a second power peak. In a practical setting FPC analysis greatly reduces dimensionality and the use of granulometries allows for comparison of the curve shapes without distorting the time scale.In contrast to the present methodology, which considers each curve as a datum, classical statistical approaches using summary parameters of time series may lead to limited information about the impact of dynamic fatiguing protocols on kinematic and kinetic time-course changes in curve shapes. © 2010 Elsevier Ltd. Source

Gonzalez-Izal M.,Public University of Navarra | Malanda A.,Public University of Navarra | Gorostiaga E.,Research and Sport Medicine Center | Izquierdo M.,Health Science University
Journal of Electromyography and Kinesiology | Year: 2012

Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables combined with the measurement of surface electromyography (sEMG) recordings (which can be measured during all types of movements) during exercise may be useful to assess and understand muscle fatigue. Therefore, there is a need to develop muscle fatigue models that relate changes in sEMG variables with muscle fatigue. However, the main issue when using conventional sEMG variables to quantify fatigue is their poor association with direct measures of fatigue. Therefore, using different techniques, several authors have combined sets of sEMG parameters to assess muscle fatigue. The aim of this paper is to serve as a state-of-the-art summary of different sEMG models used to assess muscle fatigue. This paper provides an overview of linear and non-linear sEMG models for estimating muscle fatigue, their ability to assess power loss and their limitations due to neuromuscular changes after a training period. © 2012 Elsevier Ltd. Source

Brughelli M.,Edith Cowan University | Cronin J.,Edith Cowan University | Cronin J.,University of Auckland | Mendiguchia J.,Research and Sport Medicine Center | And 2 more authors.
Journal of Strength and Conditioning Research | Year: 2010

Contralateral leg deficits between lower limbs during athletic movements are thought to increase the risk of injury and compromise performance. The purpose of this study was to quantify the magnitude of leg deficits during running in noninjured and previously injured Australian Rules football (ARF) players. The players included a group of noninjured ARF players (n = 11) and a group of previously injured ARF players (n = 11; hamstring injuries only). The players in the injured group (IG) had at least 1 acute hamstring injury in the previous 2 years. The legs of the noninjured players (NIG) were classified as right and left, whereas the legs of the injured players were classified as injured or noninjured. The players ran on a nonmotorized force treadmill at approximately 80% of their maximum velocity (Vmax). For the NIG, there were no significant differences between right and left legs for any of the variables. For the IG, the only variable that was significantly (p < 0.001) different between the injured and noninjured leg was horizontal force (175 ± 30 vs. 326 ± 44 N). Furthermore, horizontal force was significantly greater in the noninjured leg (IG) in comparison with either legs in the NIG (19.2% and 20.5%) and significantly less in the injured leg (IG) in comparison with either legs of the NIG (31.5% and 32.7%). In the present study, athletes with previous hamstring injuries had contralateral leg deficits in horizontal but not vertical force during running at submaximal velocities. © 2010 National Strength and Conditioning Association. Source

Mendiguchia J.,Sports Medicine Biodynamics Center and Human Performance Laboratory | Mendiguchia J.,Research and Sport Medicine Center | Ford K.R.,Sports Medicine Biodynamics Center and Human Performance Laboratory | Ford K.R.,University of Cincinnati | And 6 more authors.
Sports Medicine | Year: 2011

Following the onset of maturation, female athletes have a significantly higher risk for anterior cruciate ligament (ACL) injury compared with male athletes. While multiple sex differences in lower-extremity neuromuscular control and biomechanics have been identified as potential risk factors for ACL injury in females, the majority of these studies have focused specifically on the knee joint. However, increasing evidence in the literature indicates that lumbo-pelvic (core) control may have a large effect on knee-joint control and injury risk. This review examines the published evidence on the contributions of the trunk and hip to knee-joint control. Specifically, the sex differences in potential proximal controllers of the knee as risk factors for ACL injury are identified and discussed. Sex differences in trunk and hip biomechanics have been identified in all planes of motion (sagittal, coronal and transverse). Essentially, female athletes show greater lateral trunk displacement, altered trunk and hip flexion angles, greater ranges of trunk motion, and increased hip adduction and internal rotation during sport manoeuvres, compared with their male counterparts. These differences may increase the risk of ACL injury among female athletes. Prevention programmes targeted towards trunk and hip neuromuscular control may decrease the risk for ACL injuries. © 2011 Adis Data Information BV. All rights reserved. Source

Gonzalez-Izal M.,University of Pamplona | Falla D.,University of Aalborg | Izquierdo M.,Research and Sport Medicine Center | Farina D.,University of Aalborg
Journal of Neuroscience Methods | Year: 2010

This study proposes a method for estimating force loss during fatiguing maximal isokinetic knee extension contractions using a set of features from surface EMG signals recorded from multiple locations over the quadriceps muscle. Nine healthy participants performed fatiguing tests which consisted of 50 and 75 isokinetic leg extensions at a speed of 30°/s and 80°/s in two experimental sessions on different days. The set of data recorded from one of the experimental sessions (at both velocities) was used to train a multi-layer perceptron neural network to associate force loss (direct measure of fatigue) to EMG features. The data from the other session (obtained from the tests at both velocities) were used for testing the neural network performance. The proposed method was compared with a previous approach for the assessment of fatigue (Mapping Index, MI) using a signal to noise metrics computed on the estimated trend of fatigue. The signal to noise ratio obtained with the proposed approach was greater (8.83 ± 1.07) than that obtained with the MI (5.67 ± 1.17) (P<0.05) when the subjects were analyzed individually and when the network was trained over the entire subject group (8.07 vs. 4.42). In conclusion, the proposed approach allows estimation of force loss during maximal dynamic knee extensions from surface EMG signals with greater accuracy than previous methods. © 2010. Source

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