The muscle fatigue can be expressed as decrease in maximal voluntary force generating capacity of the neuromuscular system as a result of peripheral changes at the level of the muscle, and also failure of the central nervous system to drive the motoneurons adequately. In this study, a muscle fatigue detection method based on frequency spectrum of electromyogram (EMG) and mechanomyogram (MMG) has been presented. The EMG and MMG data were obtained from 31 healthy, recreationally active men at the onset, and following exercise. All participants were performed a maximally exercise session in a motor-driven treadmill by using standard Bruce protocol which is the most widely used test to predict functional capacity. The method used in the present study consists of pre-processing, determination of the energy value based on wavelet packet transform, and classification phases. The results of the study demonstrated that changes in the MMG 176-234 Hz and EMG 254-313 Hz bands are critical to determine for muscle fatigue occurred following maximally exercise session. In conclusion, our study revealed that an algorithm with EMG and MMG combination based on frequency spectrum is more effective for the detection of muscle fatigue than EMG or MMG alone.
Cetin, E, Hindistan, IE, Ozkaya, YG. Effect of different training methods on stride parameters in speed maintenance phase of 100-m sprint running. J Strength Cond Res 32(5): 1263-1272, 2018-This study examined the effects of 2 different training methods relevant to sloping surface on stride parameters in speed maintenance phase of 100-m sprint running. Twenty recreationally active students were assigned into one of 3 groups: combined training (Com), horizontal training (H), and control (C) group. Com group performed uphill and downhill training on a sloping surface with an angle of 4°, whereas H group trained on a horizontal surface, 3 days a week for 8 weeks. Speed maintenance and deceleration phases were divided into distances with 10-m intervals, and running time (t), running velocity (RV), step frequency (SF), and step length (SL) were measured at preexercise, and postexercise period. After 8 weeks of training program, t was shortened by 3.97% in Com group, and 2.37% in H group. Running velocity also increased for totally 100 m of running distance by 4.13 and 2.35% in Com, and H groups, respectively. At the speed maintenance phase, although t and maximal RV (RVmax) found to be statistically unaltered during overall phase, t was found to be decreased, and RVmax was preceded by 10 m in distance in both training groups. Step length was increased at 60-70 m, and SF was decreased at 70-80 m in H group. Step length was increased with concomitant decrease in SF at 80-90 m in Com group. Both training groups maintained the RVmax with a great percentage at the speed maintenance phase. In conclusion, although both training methods resulted in an increase in running time and RV, Com training method was more prominently effective method in improving RV, and this improvement was originated from the positive changes in SL during the speed maintaining phase.
SummaryStudy aim: Several sprint interval training applications with different slope angles in the literature mostly focused on sprint running time and kinematic and dynamic properties of running. There is a lack of comparative studies investigating aerobic and anaerobic power. Therefore, this study aimed to examine the effects of sprint interval training on sloping surfaces on anaerobic and aerobic power.Material and methods: A total of 34 male recreationally active men aged 20.26 ± 1.68 years and having a BMI of 21.77 ± 1.74 were assigned to one of the five groups as control (CON), uphill training (EXP1), downhill training (EXP2), uphill + downhill training (EXP3) and horizontal running training (EXP4) groups. Gradually increased sprint interval training was performed on horizontal and sloping surfaces with an angle of 4°. The training period continued for three days a week for eight weeks. The initial and the final aerobic power was measured by an oxygen analyser and anaerobic power was calculated from the results of the Margaria-Kalamen staircase test.Results: Following the training programme, an increase in aerobic power was found in all training groups (EXP1 = 20.79%, EXP2 = 14.95%, EXP3 = 26.85%, p < 0.01) and EXP4 = 20.46%) (p < 0.05) in comparison with the CON group (0.12%), but there were no differences among the training groups. However, significant increases in anaerobic power were found in uphill training (4.91%) and uphill + downhill training (8.35%) groups (p < 0.05).Conclusion: This study showed that all sprint interval studies on horizontal and sloping surfaces have a positive effect on aerobic power, and uphill and combined training are the most effective methods for the improvement of anaerobic power.
Maksimal oksijen tüketimi (maxVO2) aerobik kapasitenin doğrudan göstergesidir. Bu sebeple hem spor branşlarında hem de klinikte maxVO2 ölçümü oldukça büyük öneme sahiptir. Ancak maxVO2 ölçüm sistemlerinin maliyetli oluşu farklı analiz yöntemlerinin belirlenmesi ihtiyacını ortaya çıkarmıştır. Bu çalışmada da antropometrik, kinematik, kalp atım hızı ve adım parametreleri kullanılarak makine öğrenme modelleri ile maxVO2 değerlerinin tahmin edilmesi amaçlanmıştır. Çalışmaya katılan 52 erkek sporcunun koşu bandında yapılan üç farklı koşu hızında maxVO2 değerleri ve kalp atım hızları belirlenmiş, antropometrik ve kinematik veriler ile birlikte değerlendirilmiştir. Yaş, boy, vücut ağırlığı, kalp atım hızı, bacak uzunluğu, uyluk uzunluğu, hız, adım frekansı, adım uzunluğu parametreleri makine öğrenme modellerine girdi olarak sunularak maxVO2 değerinin hesaplanması istenmiştir. Ayrıca dört farklı makine öğrenme modeli (lineer regresyon, destek vektör makineleri, karar ağaçları ve gauss süreç regresyonu) denenerek en başarılı yaklaşımın hangisi olduğu incelenmiştir. Gauss Süreç Regresyonu modelinin en başarılı tahmin (R2=0.99) ve en düşük hata oranı (RMSE=0.012) ile maxVO2 değerini tahmin ettiği belirlenmiştir. Sonuç olarak çalışma kapsamında temel antropometrik ölçümler (boy, vücut ağırlığı, bacak ve uyluk uzunluğu), kalp atım hızı, hız ve adım parametreleri (adım frekansı ve adım uzunluğu) kullanılarak maxVO2 değerleri hem submaksimal hem de maksimal değerlerde başarılı olarak tahmin edilmiştir.
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