Regression shrinkage and neural models in predicting the results of 400-metres hurdles races

Self Cite

The disabled population varies significantly in regard to physical fitness, what is conditioned by the damage to the locomotor system. Recently there has been an increased emphasis on the role of competitive sport in enhancing health and the quality of life of individuals with disability. One of the sport disciplines of Paralympics is the flat bench press. The bench press is one of the most popular resistance exercises used for the upper body in healthy individuals. It is used not only by powerlifters, but also by athletes in most strength-speed oriented sport disciplines. The objective of the study was to compare neuromuscular control for various external loads (from 60 to 100% 1RM) during the flat bench press performed by an elite able-bodied athlete and an athlete with lower limb disability. The research project is a case study of two elite bench press athletes with similar sport results: an able-bodied athlete (M.W., age 34 years, body mass 103 kg, body height 1.72 m, 1RM in the flat bench press 200 kg) and a disabled athlete (M.T., age 31 years, body mass 92 kg, body height 1.70 m, 1RM in the flat bench press 190 kg). The activity was recorded for four muscles: pectoralis major (PM), anterior deltoid (AD), as well as for the lateral and long heads of the triceps brachii (TBlat and TBlong). The T-test revealed statistically significant differences between peak activity of all the considered muscles (AD with p = 0.001; PM with p = 0.001; TBlat with p = 0.0021 and TBlong with p = 0.002) between the 2 athletes. The analysis of peak activity differences of M.W and M.T. in relation to the load revealed statistically significant differences for load changes between: 60 to 100% 1RM (p = 0.007), 70 to 100% 1RM (p = 0.016) and 80 to 100% 1RM (p = 0.032). The flat bench press performed without legs resting firmly on the ground leads to the increased engagement of upper body muscles and to their greater activation. Isolated initial positions can be used to generate greater engagement of muscle groups during the bench press exercise and evoke their higher activation.

Section: Methodsmentioning

confidence: 99%

Neuromuscular Control During the Bench Press Movement in an Elite Disabled and Able-Bodied Athlete

Gołaś

Zwierzchowska

Maszczyk

et al. 2017

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“…Thus, the ANN enables a coach to model the future level of athlete’s performance and supports the process of sports selection ( Maszczyk et al, 2012 , 2013, 2016 ; Pfeiffer and Hohmann, 2012 ; Silva et al, 2007 ). Artificial neural networks are also widely used in the process of planning training loads ( Maszczyk et al, 2016 ; Przednowek and Wiktorowicz, 2011 ; Przednowek et al, 2016 ; Roczniok et al, 2007 ; Ryguła, 2005 ). Another study has proposed the use of ANNs to classify kicking techniques ( Lapkova et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

Przednówek

Iskra

Wiktorowicz

et al. 2017

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This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

“…Despite both, non-linear and linear methods had evidenced values lower than 10% ( Tsai et al, 2013 ), ANN outperformed LM throughout training and validation phases with reduced prediction errors, except the validation phase of the horizontal start variant. In times of ubiquitous information technology, coaches and athletes are able to use advanced mathematical methods in modelling the training process ( Przednowek et al, 2016 ). From a coach’s point of view, the prediction of results is very important in the sports training process ( Przednowek et al, 2016 ; Wiktorowicz et al, 2015 ), since backstroke start performance modelling can show how kinematic and kinetic changes will influence the start time.…”

Section: Discussionmentioning

confidence: 99%

“…In times of ubiquitous information technology, coaches and athletes are able to use advanced mathematical methods in modelling the training process ( Przednowek et al, 2016 ). From a coach’s point of view, the prediction of results is very important in the sports training process ( Przednowek et al, 2016 ; Wiktorowicz et al, 2015 ), since backstroke start performance modelling can show how kinematic and kinetic changes will influence the start time.…”

Section: Discussionmentioning

confidence: 99%

Modelling and Predicting Backstroke Start Performance Using Non-Linear And Linear Models

Jesus

Ayala

Jesus

et al. 2018

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.