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

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

AK - Sport and leisure time activities

OECD FORD branch

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Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

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

Name of the periodical

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

ISSN

0379-9069

e-ISSN

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Volume of the periodical

38

Issue of the periodical within the volume

1

Country of publishing house

ZA - SOUTH AFRICA

Number of pages

13

Pages from-to

91-103

UT code for WoS article

000374814300007

EID of the result in the Scopus database

2-s2.0-84963620613