The breaststroke technique is considered one of the least economic of the four swimming styles (Barbosa et al., 2006), which can lead to early fatigue while swimming. Neuromuscular fatigue can be defined as: (i) the failure to maintain the required or expected force, accompanied by changes in muscular activity (Dimitrova et al., 2003); and (ii) the inability of skeletal muscle to generate high levels of muscular strength or maintain these levels over time (Enoka and Stuart, 1992). Additionally, the manifestations of fatigue have been associated with (Allen et al., 1995; Pagala et al., 1994): (i) a decline in muscle tension produced during and after submaximal and maximal exercise; (ii) an inability to maintain a given exercise intensity over time, reducing the speed of contraction and increased muscle relaxation time, as well as; (iii) the variation of intra and extracellular concentrations of certain metabolites and ions.
Neuromuscular fatigue can be divided into central and peripheral fatigue. Central fatigue comprises of decreases in the voluntary activation of the muscle, which is due to decreases in the number of recruited motor units and their discharge rate (Gonzalez et al., 2012). Peripheral fatigue involves changes in neuromuscular transmission, muscle action potential propagation and decreases in the contractile tension of the muscle fibers (Boyas and Guevel, 2011). Peripheral fatigue during exercise is considered an impairment of the peripheral mechanisms from excitation to muscle contraction. Peripheral regulation is, therefore, related to a perturbation of calcium ion movements, an accumulation of phosphate, and/or a decrease of adenosine triphosphate stores (Boyas and Guevel, 2011).
Some studies attempted to relate the onset of fatigue with the execution of various sport techniques (Camata et al., 2011; Giangarra et al., 1993; Pink et al., 1993; Robineau et al., 2012). While swimming, neuromuscular mechanisms related to fatigue remain unclear, and the few studies which carried out research, focused mainly on the front-crawl technique (Caty et al., 2006; Figueiredo et al., 2011; Ikuta et al., 2012; Stirn et al., 2011).
Caty et al. (2006) observed a decrease in the instantaneous mean frequency in the extensor carpi ulnaris muscles (11.4% and 8.5%, respectively) after a 4x50m high intensity front crawl. Stirn et al. (2011) reported that at the end of a 100m front crawl at a maximal effort, the mean power frequency decreased by 20-25%. Ikuta et al. (2012) suggested that the decrease in swimming velocity was related to a decrease in the activity of several muscles coordinated with each other, and that a compensating strategy was involved between the pectoralis major and other muscles during the last lap of a 4x50m front crawl test.
Important muscles activated in breaststroke swimming seem to be the biceps brachii, triceps brachii, subscapularis, latissimus dorsi, pectoralis major, supraspinatus, infraspinatus, serratus anterior, and deltoid anterior, teres minor and trapezio) for the upper limbs (Conceicao et al., 2010; Nuber et al., 1986; Ruwe et al., 1994; Yoshizawa et al., 1976). While gluteus maximus, vastus medialis, rectus femoris, biceps femoris, abductor magnus, quadriceps, gastrocnemius, tibialis anterior, abductor hallucius, abductor digiti minimi, flexor digitorum brevis are for the lower limbs (Mcleod, 2010; Yoshizawa et al., 1976).
It is clear that EMG can be useful in tracking muscle fatigue for many reasons (the relationships found between sEMG features and muscle fatigue, and the possibility of recording them in almost any type of situation). Therefore, some studies have examined the relationship between muscle fatigue and EMG variables and, consequently, the possibility of using EMG models to accurately track muscle fatigue (Gonzalez-Izal et al., 2012).
The linear techniques that are used to estimate muscle fatigue are based on linear regression, which relates changes in EMG parameters to changes in power loss (as a direct measurement of muscle fatigue). The non-linear techniques that are used to estimate muscle fatigue are based on neural networks to relate EMG parameters to muscle fatigue. Both linear and non-linear techniques provided good results for mapping changes in power or force loss during dynamic exercises based on sets of EMG parameters (Gonzalez-Izal et al., 2012).
Apart from this, however, we have no idea of the appearance of fatigue of the upper limbs in breaststroke. Since there is a lack of studies to quantify the neuromuscular fatigue in the upper limbs, using spectral parameters, we believe that this research would have the potential to make a contribution to the limited body of knowledge in this field. The main aim for swimming coaches and researchers relies on the identification of the factors that might predict performance with higher validity and accuracy.
The aims of this study were: i) to analyze the activation patterns (duration of active and non-active phase) of the upper limbs' four muscles during each lap for 200m breaststroke, ii) quantify neuromuscular fatigue, through kinematics and physiologic assessment. It was hypothesized that an increase in signal amplitude and a decrease in spectral parameters due to repetitive sub-maximal contractions, characterized a non-linear fatigue process. Also considered was that the fatigue process occurs differently for each of the muscles studied.
Nine male swimmers (age: 22.3 [+ or -] 2.9 years; height: 1.81 [+ or -] 0.05 m; body mass: 73.60 [+ or -] 3.82 kg; mean [+ or -] SD) volunteered to participate in this study and provided written consent. They were all swimmers competing at the national level with an average personal best result for 200-m breaststroke (149.44 [+ or -] 6.59 s, corresponding respectively to 643.75 [+ or -] 53.77 FINA ranking points). All the procedures were approved by the institutional Ethics Committee and carried out according to the Helsinki Declaration.
The experiments were performed in a 50m indoor swimming pool at a water temperature of 27.5[degrees] C and 75[degrees]% humidity. Subjects performed a standard warm-up of 800m front crawl, and a specific warm-up of 200m breaststroke at a medium level of effort. After a twenty minute rest subjects performed a maximal 200m breaststroke trial with a push off start.
EMG data collection
Surface EMG signals from the biceps brachii (BB), deltoid anterior (DA), pectoralis major (PM) and triceps brachii (TB) on the right side of the body were collected. These muscles were selected according to their importance in breaststroke (Conceicao et al., 2010; Nuber et al., 1986; Ruwe et al., 1994; Yoshizawa et al., 1976).
Bipolar surface electrodes were used (10-mm diameter discs, Plux, Lisbon, Portugal) with an inter-electrode distance of 20mm. Electrodes on the upper part of the PM were placed in the middle of the line that connects the acromion process and the manubrium (sternum) two fingers below the clavicle (Stirn et al., 2011). The electrodes on the long head of the BB, DA and TB were placed in accordance with SENIAM recommendations (Herrmens and Freriks, 1999).
The skin was shaved, rubbed with sandpaper and cleaned with alcohol so that the inter-electrode resistance did not exceed 5 KOhm (Basmajian and De Luca, 1985). The ground electrode was positioned over the cervical vertebrae. Transparent dressings with labels (Hydrofilm[R], 10 cm x 12,5 cm, USA) were used to cover the electrodes to isolate them from the water (Hohmann et al., 2006). All cables were fixed to the skin by adhesive tape in several places to minimize their movement and any signal interference. Swimmers wore a full body swimming suit to further immobilize the cables (Fastskin Speedo[R], Speedo Aqualab, USA).
The wireless EMG device (BioPLUX.research, Lisbon, Portugal) with 8 analog channels (12-bit), sampling rate at 1 kHz, was put in a waterproof bag (84x53x18mm) and placed inside the swimmer's cap. Data was transmitted to the PC in real time via Bluetooth.
The EMG and video data were recorded simultaneously and were not synchronized with video recording, because the aim of this study was to understand the appearance of fatigue in relation to race parameters rather than detailed within cycle swimming kinematics.
All EMG analysis was conducted with a MATLAB routine (Mathworks, Inc., Natick MA, USA). The process of determining the muscle activity boundaries consists of finding the neighborhood points, where the energy was 30% of muscle activation maximum peak within a stroke (Stirn et al., 2011). These were calculated by segmenting the muscle input signal energy according to the same criteria described in Stirn et al. (2011). Starting from the raw signal, DC components were removed and thereafter filtered with a fifth-order Butterworth band pass filter where the lower and upper cut-off frequencies were set to 10 and 500Hz respectively. The signal energy was then determined with a 250 ms sliding window (Stirn et al., 2011) and according to:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
Even though the high frequencies of the input signal were filtered with a Butterworth filter, muscle energy is very noisy and presents several local maximum peaks that didn't correspond to the muscle active window center, as shown in Figure 2. To overcome this difficulty, a strategy to determine the muscle's "true" maximum energy peaks was devised. Each stroke taken by a swimmer produces patterns in the signal, these patterns are mainly translated by a periodicity in EMG energy, see Figure 1.
By defining the signal mean period, one can use this information to determine the maximum peak candidates with the highest and with minimal differences between two maximum candidates and the expected period.
Once the maximum candidates have been determined, the muscle activity boundaries were then selected by determining the neighborhood points where the energy...