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

Autor: de Jesus K; Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Porto, Portugal.; Porto Biomechanics Laboratory, University of Porto, Porto, Portugal.; Human Performance Laboratory, Faculty of Physical Education and Physiotherapy, Federal University of Amazonas, Manaus, Brazil.; Human Motor Behaviour Laboratory, Faculty of Physical Education and Physiotherapy, Federal University of Amazonas, Manaus, Brazil., Ayala HVH; Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Paraná, Curitiba, Brazil., de Jesus K; Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Porto, Portugal.; Porto Biomechanics Laboratory, University of Porto, Porto, Portugal.; Human Performance Laboratory, Faculty of Physical Education and Physiotherapy, Federal University of Amazonas, Manaus, Brazil.; Human Motor Behaviour Laboratory, Faculty of Physical Education and Physiotherapy, Federal University of Amazonas, Manaus, Brazil., Coelho LDS; Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Paraná, Curitiba, Brazil.; Electrical Engineering Graduate Program, Federal University of Paraná, Curitiba, Brazil., Medeiros AIA; Research Group in Biodynamic Human Movement, Institute of Physical Education and Sport, Federal University of Ceara, Fortaleza, Brazil., Abraldes JA; Department of Physical Activity and Sport, Faculty of Sports Sciences. University of Murcia, Murcia, Spain., Vaz MAP; Porto Biomechanics Laboratory, University of Porto, Porto, Portugal.; Institute of Mechanical Engineering and Industrial Management, Faculty of Engineering, University of Porto, Porto, Portugal., Fernandes RJ; Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Porto, Portugal.; Porto Biomechanics Laboratory, University of Porto, Porto, Portugal., Vilas-Boas JP; Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Porto, Portugal.; Porto Biomechanics Laboratory, University of Porto, Porto, Portugal.
Jazyk: angličtina
Zdroj: Journal of human kinetics [J Hum Kinet] 2018 Mar 23; Vol. 61, pp. 29-38. Date of Electronic Publication: 2018 Mar 23 (Print Publication: 2018).
DOI: 10.1515/hukin-2017-0133
Abstrakt: 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.
Databáze: MEDLINE