DETERMINATION OF MAXIMUM OXYGEN CONSUMPTION BY MACHINE LEARNING METHODS USING STEP KINEMATICS

Abstract

Maximal oxygen consumption (maxVO2) is a direct indicator of aerobic capacity. For this reason, maxVO2 measurement is of great importance both in sport branches and also in clinic. However, the fact that maxVO2 measurement systems are costly has led to the need to determine different analysis methods. In this study, it was aimed to predict maxVO2 values with machine learning models using anthropometric, kinematic, heart rate and step parameters. MaxVO2 values and heart rates of 52 male athletes participating in the study at three different running speeds on the treadmill were determined and evaluated together with anthropometric and kinematic data. Age, height, body weight, heart rate, leg length, thigh length, running speed, stride frequency, stride length parameters were presented as input to the machine learning models and the calculation of the maxVO2 value was made. In addition, four different machine learning models (Linear Regression, Support Vector Machines, Decision Trees, and Gaussian Process Regression) were used and the most successful approach was examined. The Gaussian Process Regression model was able to determine the maxVO2 value with the most successful prediction (R2=0.99) and the lowest error rate (RMSE=0.012). As a result, maxVO2 values were successfully estimated in both submaximal and maximal values using basic anthropometric measurements (height, body weight, leg and thigh length), heart rate, speed and stride parameters (stride frequency and stride length) within the scope of the study.

Keywords

• Aerobic capasity
• Machine learning
• Maximal Oxygen Consumption

MAKSİMUM OKSİJEN TÜKETİMİNİN ADIM KİNEMATİKLERİ KULLANILARAK MAKİNE ÖĞRENME YÖNTEMLERİYLE BELİRLENMESİ

Abstract

Maksimal oksijen tüketimi (maxVO2) aerobik kapasitenin doğrudan göstergesidir. Bu sebeple hem spor branşlarında hem de klinikte maxVO2 ölçümü oldukça büyük öneme sahiptir. Ancak maxVO2 ölçüm sistemlerinin maliyetli oluşu farklı analiz yöntemlerinin belirlenmesi ihtiyacını ortaya çıkarmıştır. Bu çalışmada da antropometrik, kinematik, kalp atım hızı ve adım parametreleri kullanılarak makine öğrenme modelleri ile maxVO2 değerlerinin tahmin edilmesi amaçlanmıştır. Çalışmaya katılan 52 erkek sporcunun koşu bandında yapılan üç farklı koşu hızında maxVO2 değerleri ve kalp atım hızları belirlenmiş, antropometrik ve kinematik veriler ile birlikte değerlendirilmiştir. Yaş, boy, vücut ağırlığı, kalp atım hızı, bacak uzunluğu, uyluk uzunluğu, hız, adım frekansı, adım uzunluğu parametreleri makine öğrenme modellerine girdi olarak sunularak maxVO2 değerinin hesaplanması istenmiştir. Ayrıca dört farklı makine öğrenme modeli (lineer regresyon, destek vektör makineleri, karar ağaçları ve gauss süreç regresyonu) denenerek en başarılı yaklaşımın hangisi olduğu incelenmiştir. Gauss Süreç Regresyonu modelinin en başarılı tahmin (R2=0.99) ve en düşük hata oranı (RMSE=0.012) ile maxVO2 değerini tahmin ettiği belirlenmiştir. Sonuç olarak çalışma kapsamında temel antropometrik ölçümler (boy, vücut ağırlığı, bacak ve uyluk uzunluğu), kalp atım hızı, hız ve adım parametreleri (adım frekansı ve adım uzunluğu) kullanılarak maxVO2 değerleri hem submaksimal hem de maksimal değerlerde başarılı olarak tahmin edilmiştir.

Keywords

• aerobik kapasite
• makine öğrenmesi
• maksimal oksijen tüketimi

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