Support vector machines for predicting the hamstring and quadriceps muscle   strength of college-aged athletes

MEHMET FATİH AKAY; FATİH ABUT; EBRU ÇETİN; İMDAT YARIM; BOUBACAR SOW

Makale detayları

Support vector machines for predicting the hamstring and quadriceps muscle strength of college-aged athletes

Yazarlar:

MEHMET FATİH AKAY | FATİH ABUT | EBRU ÇETİN | İMDAT YARIM | BOUBACAR SOW

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 4, pp.2567-2582

Belge Tipi:

Makale

Yayın Tarihi:

7/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000736016

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Hamstring and quadriceps muscles are essential for the   performance of athletes in various sport branches. Hamstring muscles control   running activities and stabilize the knee during turns or tackles, while   quadriceps muscles play an important role in jumping and kicking. Although   hamstring and quadriceps muscle strength in athletes can be accurately   measured using isokinetic dynamometry, practical difficulties, such as the   requirement of nonportable and costly equipment as well as a long period of   measurement time, motivate the researcher to predict hamstring and quadriceps   muscle strength using promising machine-learning methods. The purpose of   this study is to build prediction models for estimating the hamstring and   quadriceps muscle strength of college-aged athletes using a support vector   machine (SVM). The data set included 75 athletes selected from the College   of Physical Education and Sport, Gazi University, Turkey. The predictor   variables of sex, age, height, weight, body mass index, and sport branch   were utilized to build the hamstring and quadriceps muscle strength   prediction models for various types of training methods. The generalization   error of the prediction models was calculated by carrying out 10-fold   cross-validation, and the prediction errors were evaluated using several   performance metrics. For comparison purposes, prediction models based on a   radial basis function neural network (RBFNN) and single decision tree (SDT)   were also developed. The results reveal that the SVM-based hamstring and   quadriceps strength prediction models significantly outperform the   RBFNN-based and SDT-based models and can be safely utilized to produce   predictions regarding new data with acceptable accuracy.

Makale detayları

Support vector machines for predicting the hamstring and quadriceps muscle strength of college-aged athletes

Yazarlar: MEHMET FATİH AKAY | FATİH ABUT | EBRU ÇETİN | İMDAT YARIM | BOUBACAR SOW

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 4, pp.2567-2582

Belge Tipi: Makale

Yayın Tarihi: 7/2017

Belge Dili: Türkçe

Document ID (Org No.): 2000736016

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

MEHMET FATİH AKAY | FATİH ABUT | EBRU ÇETİN | İMDAT YARIM | BOUBACAR SOW

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 4, pp.2567-2582

Belge Tipi:

Makale

Yayın Tarihi:

7/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000736016

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632