Predicting centre of mass horizontal speed in low to severe swimming intensities with linear and non-linear models |
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Authors: | Kelly de Jesus Helon Vicente Hultmann Ayala Leandro dos Santos Coelho João Paulo Vilas-Boas Ricardo Jorge Pinto Fernandes |
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Institution: | 1. Centre of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto (FADE-UP), Porto, Portugal;2. Porto Biomechanics Laboratory (LABIOMEP), University of Porto, Porto, Portugal;3. Human Performance Laboratory (LEDEHU), Faculty of Physical Education and Physiotherapy, Federal University of Amazonas, Manaus, Brazil;4. Human Motor Behaviour Laboratory (LECOHM), Faculty of Physical Education and Physiotherapy, Federal University of Amazonas, Manaus, Brazil;5. Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil;6. Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Paraná, Curitiba, Brazil;7. Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Paraná, Curitiba, Brazil;8. Electrical Engineering Graduate Program (PGEE), Federal University of Paraná, Curitiba, Brazil |
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Abstract: | We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities using physiological and biomechanical dataset. Ten trained male swimmers completed a 7 × 200 m front crawl protocol (0.05 m.s?1 increments and 30 s intervals) to assess expiratory gases and blood lactate concentrations. Two surface and four underwater cameras recorded independent images subsequently processed focusing a three-dimensional reconstruction of two upper limb cycles at 25 and 175 m laps. Eight physiological and 13 biomechanical variables were inputted to predict CM horizontal speed. MLP, RBF and LM were implemented with the Levenberg-Marquardt algorithm (feed forward with a six-neuron hidden layer), orthogonal least squares algorithm and decomposition of matrices. MLP revealed higher prediction error than LM at low-moderate intensity (2.43 ± 1.44 and 1.67 ± 0.60%), MLP and RBF depicted lower mean absolute percentage errors than LM at heavy intensity (2.45 ± 1.61, 1.82 ± 0.92 and 3.72 ± 1.67%) and RBF neural networks registered lower errors than MLP and LM at severe intensity (2.78 ± 0.96, 3.89 ± 1.78 and 4.47 ± 2.36%). Artificial neural networks are suitable for speed model-fit at heavy and severe swimming intensities when considering physiological and biomechanical background. |
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Keywords: | Multilayer perceptron artificial neural networks radial basis function artificial neural networks mathematical linear model incremental protocol front crawl swimming |
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