Emg analysis and modelling of flat bench press using artificial neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11510%2F16%3A10324076" target="_blank" >RIV/00216208:11510/16:10324076 - isvavai.cz</a>

Výsledek na webu

<a href="http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=213029c0-8430-4462-bab5-1bf80d91c52f%40sessionmgr104&vid=1&hid=124" target="_blank" >http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=213029c0-8430-4462-bab5-1bf80d91c52f%40sessionmgr104&vid=1&hid=124</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Emg analysis and modelling of flat bench press using artificial neural networks

Popis výsledku v původním jazyce

The objective of this study was to evaluate the contribution of particular muscle groups during the Flat Bench Press (FBP) with different external loads. Additionally, the authors attempted to determine whether regression models or Artificial Neural Networks (ANNs) can predict FBP results more precisely and whether they can optimise the training process. A total of 61 strength-trained athletes performed four single repetitions with 70, 80, 90 and 100% of one repetition maximum (1RM). Based on both kinematic and electromyography results, a regression model and ANNs for predicting the FBP performance was created. In an additional study, 15 athletes performed the training session in order to verify the created model. The results of the investigation show that the created neural models 9-4-1 structure (NRMSE [Normalised Root Mean Squared Error], for the learning series was 0.114, and for the validation and test series 0.133 and 0.118, respectively), offer a much higher quality of prediction than a non-linear regression model (Absolute regression error - Absolute network error =47kg-17kg=30kg). (C) 2016, University of Stellenbosch. All rights reserved.

Název v anglickém jazyce

Emg analysis and modelling of flat bench press using artificial neural networks

Popis výsledku anglicky

The objective of this study was to evaluate the contribution of particular muscle groups during the Flat Bench Press (FBP) with different external loads. Additionally, the authors attempted to determine whether regression models or Artificial Neural Networks (ANNs) can predict FBP results more precisely and whether they can optimise the training process. A total of 61 strength-trained athletes performed four single repetitions with 70, 80, 90 and 100% of one repetition maximum (1RM). Based on both kinematic and electromyography results, a regression model and ANNs for predicting the FBP performance was created. In an additional study, 15 athletes performed the training session in order to verify the created model. The results of the investigation show that the created neural models 9-4-1 structure (NRMSE [Normalised Root Mean Squared Error], for the learning series was 0.114, and for the validation and test series 0.133 and 0.118, respectively), offer a much higher quality of prediction than a non-linear regression model (Absolute regression error - Absolute network error =47kg-17kg=30kg). (C) 2016, University of Stellenbosch. All rights reserved.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AK - Sport a aktivity volného času

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

South African Journal for Research in Sport, Physical Education and Recreation

ISSN

0379-9069

e-ISSN

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Svazek periodika

38

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

ZA - Jihoafrická republika

Počet stran výsledku

13

Strana od-do

91-103

Kód UT WoS článku

000374814300007

EID výsledku v databázi Scopus

2-s2.0-84963620613