The aim of this study was to compare the musculature activity and kinematics of knee and hip joints during front and back squat with maximal loading. Two-dimensional kinematical data were collected and electromyographic activities of vastus lateralis, vastus medialis, rectus femoris, semitendinosus, biceps femoris, gluteus maximus and erector spinae were measured while participants (n = 12, 21.2 ± 1.9 years old) were completing front and back squat exercises with maximum loading. Paired sample t-test was used for comparisons between two techniques. Results showed that the electromyographic activity of vastus medialis was found to be greater in the front squat compared to the back squat during the ascending phase (P < 0.05, d = 0.62; 95% CI, -15.0/-4.17) and the whole manoeuvre (P < 0.05, d = 0.41; 95% CI, -12.8/-0.43), while semitendinosus (P < 0.05, d = -0.79; 95% CI, 0.62/20.59) electromyographic activity was greater in the back squat during the ascending phase. Compared to the front squat version, back squat exhibited significantly greater trunk lean, with no differences occurring in the knee joint kinematics throughout the movement. Results may suggest that the front squat may be preferred to the back squat for knee extensor development and for preventing possible lumbar injuries during maximum loading.

The aim of this study was to investigate the possible kinematic and muscular activity changes with maximal loading during squat maneuver. Fourteen healthy male individuals, who were experienced at performing squats, participated in this study. Each subject performed squats with 80%, 90%, and 100% of the previously established 1 repetition maximum (1RM). Electromyographic (EMG) activities were measured for the vastus lateralis, vastus medialis, rectus femoris, semitendinosus, biceps femoris, gluteus maximus, and erector spinae by using an 8-channel dual-mode portable EMG and physiological signal data acquisition system (Myomonitor IV, Delsys Inc., Boston, MA, USA). Kinematical data were analyzed by using saSuite 2D kinematical analysis program. Data were analyzed with repeated measures analysis of variance (p < 0.05). Overall muscle activities increased with increasing loads, but significant increases were seen only for vastus medialis and gluteus maximus during 90% and 100% of 1RM compared to 80% while there was no significant difference between 90% and 100% for any muscle. The movement pattern in the hip joint changed with an increase in forward lean during maximal loading. Results may suggest that maximal loading during squat may not be necessary for focusing on knee extensor improvement and may increase the lumbar injury risk.

Dietary supplements containing arginine are among the most popular ergogenics intended to enhance strength, power and muscle recovery associated with both anaerobic and aerobic exercise. The aim of the present study was to evaluate the possible effect of pre-exercise acute intake of arginine on performance and exercise metabolism during incremental exhaustive exercise in elite male wrestlers. Nine volunteer elite male wrestlers (24.7±3.8 years) participated in this study. The test-retest protocol was used on the same subjects. The study was conducted using a cross-over design. A single dose of arginine (1.5 g · 10 kg-1 body weight) or placebo was given to the subjects after 12 hours fasting (during the night) for both test and retest. Subjects were allowed to drink water but not allowed to eat anything between arginine or placebo ingestion and the exercise protocol. An incremental exercise protocol was applied and oxygen consumption was measured during the exercise. Heart rate and plasma lactate levels were measured during the exercise and recovery. Results showed that in the same working loads there was no significant difference for the mean lactate levels and no difference in maximum oxygen consumption (arginine 52.47±4.01 mL · kg-1 · min-1, placebo 52.07±5.21 mL · kg-1 · min-1) or in maximum heart rates (arginine 181.09±13.57 bpm, placebo 185.89±7.38 bpm) between arginine and placebo trials. Time to exhaustion was longer with arginine supplementation (1386.8±69.8 s) compared to placebo (1313±90.8 s) (p < 0.05). These results suggest that L-arginine supplementation can have beneficial effects on exercise performance in elite male wrestlers but cannot explain the metabolic pathways which are responsible from these effects.

Machine learning models are implemented to perform tasks that human beings have difficulty completing. The analysis and prediction of players' performance of specific athletic tasks have increasing importance in both game and training planning. The diversity and complexity of specific types of athletic performance and the mostly nonlinear relationships between them make analysis and prediction tasks complicated when using conventional methods. Therefore, the use of effective machine learning models may contribute to the ability to achieve high accuracy predictions of players' athletic performance. The aim of this study was to evaluate different machine learning models for predicting particular types of athletic performance in female handball players and to determine the significant factors influencing predicted performances by using the superior model. Linear regression, decision tree, support vector regression, radialbasis function neural network, backpropagation neural network and long short-term memory neural network models were implemented to predict the performance of female handball players in countermovement jumps with hands-free and hands-on-hips, 10 meter and 20-meter sprints, a 20-meter shuttle run test and a handball agility specific test. A total of 23 properties and measurements of attributes and 118 instances of training patterns were recorded for each machine learning models. The results showed that the radial-basis function neural network outperformed the other models and was capable of predicting the studied types of athletic performance with R 2 scores between 0.86 and 0.97. Finally, significant factors influencing predicted performance were determined by retraining the superior model. This is one of the first studies using machine learning in sport sciences for handball players, and the results are encouraging for future studies. INDEX TERMS Artificial intelligence, athletic performance, machine learning models, radial-basis function neural network.

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