Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification performance. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification performance of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy participants performed 6 sessions of 15 gait trials for 1 day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRFs). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each preprocessing step were analyzed by comparing their prediction performance in a six-session classification using Support Vector Machines, Random Forest Classifiers, Multi-Layer Perceptrons, and Convolutional Neural Networks. The results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction performance of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

The researches which investigated the effects of core training on skill performance for badminton players are in sufficient. The current study aimed to examine the effects of core stability training on dynamic balance and smash stroke velocity and accuracy performance. Twenty badminton players under 19 years were divided into two groups, core training group (n = 10) and control group (n = 10). Core stability training group completed 8 weeks training programme, while control group did only their badminton traditional training. The effectiveness of core stability training intervention was assessed by performing core stability tests with four positions, dynamic balance test and forehand smash stroke performance test. Significant improvement was obtained only in the training group for core muscle endurance, total reached distance of three directions in star excursion balance test and smashing velocity and accuracy variables (p < 0.05). Consequently, eight weeks core stability training provides an improvement for maintained optimum lower limbs dynamic balance and increased the performance level of smash stroke in young badminton players.

The aims of this study were (a) to assess and correlate interval shuttle run test (ISRT) performance, maximum oxygen uptake (V[Combining Dot Above]O(2)max), running economy (RE), running velocity at the first rise in blood lactate concentrations above baseline (vLT) and running velocity at 4 mmol·L(-1) blood lactate concentration (v4) in professional soccer players and (b) to investigate whether a correlation exists between the respective results of time to exhaustion (T(lim)) from continuous and intermittent endurance tests, respectively. Eleven male professional field soccer players (mean ± SD: age 23.8 ± 3.0 years, V[Combining Dot Above]O(2)max 58.2 ± 4.9 ml·kg(-1)·min(-1)) completed a continuous Incremental Test with lactate measurements to determine vLT and v4, a continuous Ramp Test with gas exchange analysis to determine V[Combining Dot Above]O(2)max and RE, and an intermittent ISRT to determine intermittent endurance capacity during the first week of preseason preparation. There were significant correlations between ISRT performance and V[Combining Dot Above]O(2)max (r = 0.70, p < 0.05), and between T(lim) in both continuous endurance tests (r = 0.89, p < 0.01). Between all other variables no significant correlations were found overall (best r = 0.60, p > 0.05). The assessment of all values of V[Combining Dot Above]O(2)max, RE, vLT, and v4 should be included when investigating aerobic endurance performance among groups or over time in professional soccer players. Although V[Combining Dot Above]O(2)max, RE, vLT, and v4 have been regarded as important factors of aerobic performance in endurance related sports, the present data revealed that V[Combining Dot Above]O(2)max was the only factor, which correlated with intermittent endurance capacity in professional soccer players. Hence, V[Combining Dot Above]O(2)max should be increased through soccer-specific training interventions including universal agility components. The T(lim) in continuous and intermittent endurance tests differs and is therefore an independent endurance performance factor in professional soccer players.

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

The study aims to investigate: (1) the prevalence of cannabis among epileptic patients seen at Mansoura University Hospital, (2) serum levels and gene expression of cytokines in epilepsy patients and the controls. and (3) the possibility that cannabis use affects the cytokine levels in epilepsy patients, triggering its future use in treatment. We recruited 440 epilepsy patients and 200 controls matched for age, gender, and ethnicity. Of the epileptic patients, 37.5% demonstrated lifetime cannabis use with a mean duration of 15 ± 73 years. Serum levels of interleukin IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, and tumor necrosis factor-α (TNF-α), were analyzed and gene expression analysis was conducted only for those cytokines that were different between groups in the serum analysis. The “Epilepsy-only” patients had significantly higher serum and mRNA levels of IL-1α, β, IL-2,6,8, and TNF-α compared to the controls and the “Cannabis+Epilepsy” group (p = 0.0001). IL-10 showed significantly lower levels in the “Epilepsy-only” patients compared to the controls and “Cannabis+Epilepsy” (p = 0.0001). Cannabis use is prevalent among epilepsy patients. Epilepsy is characterized by a pro-inflammatory state supported by high serum and gene expression levels. Cannabis users demonstrated significantly lower levels of inflammatory cytokines compared to epilepsy non-cannabis users which might contribute to its use in the treatment of resistant epilepsy.

The purpose of this pilot study was to evaluate the relationship between the kinematic variables of the right hand and left leg with ball velocity during jump-throwing phases in handball for better-informed training. We investigated ball velocity and the key kinematic variables of jump throwing during different throwing phases in three strides. Ten right-handed male handball professional players who had competed in the Egyptian Handball Super League participated in this study. Jump throwing performance was divided into three phases (cocking, acceleration and follow-through), which included eight events during the throwing. Five trials were captured for each player, and a 3D analysis was performed on the best trial. Results indicated that the velocity of the throwing hand was the most important variable during jump throwing, which was correlated with ball velocity during the three phases of performance in four events: Initial contact (IC) (r = 0.66*), initial flight (IF) (r = 63*), maximum height of the throwing hand (Max-HH) (r = 0.78*) and ground contact (GC) (r = 0.83*). In addition, the initial flight was the most important event in which players need to be using the best angles during performance, particularly the shoulder angle.