Farklı Ağırlıklar ile Yapılan Squat Sıçramanın Makine Öğrenme Yöntemleri ile Değerlendirilmesi

Year 2022, Volume: 5 Issue: 1, 1 - 12, 28.03.2022

Serkan Uslu , Emel Çetin

https://doi.org/10.38021/asbid.1071466

Cited By: 1

Abstract

Kuvvet-Hız profili sporcunun performansının ve uygun olan antrenman programının belirlenmesi için hem antrenörler hem de araştırmacılar tarafından sıklıkla kullanılan bir test yöntemidir. Ancak test protokolünde sporcunun yüksek ağırlıklar ve çok sayıda tekrar yapması hem sporcu yaralanmasına hem de yorgunluk kaynaklı performansın doğru ölçülememesine sebep olmaktadır. Bu sebeple çalışma kapsamında farklı ağırlıklardaki sıçrama yüksekliğinin tek tekrarlı ölçüm verisi kullanılarak makine öğrenme modeller ile tahmin edilmesi amaçlanmıştır. Çalışmaya Akdeniz Üniversitesi’nde öğrenim gören 52 sporcu katılmıştır. Tüm katılımcıların öncelikle demografik özellikleri, ardından dikey sıçrama protokolüne göre dört farklı ağırlıkta sıçrama yükseklikleri belirlenmiştir. Ölçülen veriler normalize edilerek makine öğrenme modellerine girdi olarak verilmiş ve dikey sıçrama yükseklikleri tahmin edilmiştir. Beş farklı makine öğrenme modeli arasından dikey sıçrama yüksekliğini en yüksek başarı ile tahmin eden makine öğrenme modeli Gaussian Süreç Regresyonu olduğu gözlenmiştir. Sporcularda yaralanmaya sebep olabilecek yüksek ağırlıklardaki farklı sayıda sıçrama yerine tek tekrarlı sıçrama yaparak diğer ağırlıklardaki sıçrama yüksekliğinin belirlenmesi ile çalışmanın literatüre hem sporcu sağlığı hem de testin daha rahat uygulanabilirliği açısından literatüre katkı sağlaması beklenmektedir.

Keywords

Kuvvet-Hız Profili, Dikey Sıçrama, Makine Öğrenme

Evaluation of Squat Jumping with Different Weights by Machine Learning

Abstract

The Force-Velocity profile is a test method that is frequently used by both trainers and researchers to determine the athlete's performance and the appropriate training program. However, performing a large number of repetitions under high weights in the test protocol causes both athlete injury and failure to measure performance due to fatigue. For this reason, within the scope of the study, it is aimed to predict the jump height at different weights with machine learning models using single repetitive measurement data. 52 athletes studying at Akdeniz University participated in the study. First of all, demographic characteristics of all participants were measured, and then jump heights were determined at four different weights according to the vertical jump protocol. The measured data were normalized and given as input to the machine learning models and the vertical jump heights were estimated. It has been observed that the machine learning model that predicts the vertical jump distance with the highest success among the five different machine learning models is the Gaussian Process Regression. It is expected that the study will contribute to the literature in terms of both athlete health and the easier applicability of the test by determining the jump distance by making a single repetitive jump instead of different numbers of jumps at high weights that may cause injury to the athletes.

Keywords

Force-Velocity profile, Vertical Jump, Machine Learning

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