The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach

Abstract

Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30–15IFT is a field test reflecting the effort elicited by HIIT, and the final velocity reached in the test is used to set the intensity of HIIT during the training session. In order to have a valid measure of the velocity during training, devices such as GPS can be used. However, in several situations (e.g., indoor setting), such devices do not provide reliable measures. The aim of the study was to predict exact running velocity during the 30–15IFT using accelerometry-derived metrics (i.e., Player Load and Average Net Force) and heart rate (HR) through a machine learning (ML) approach (i.e., Support Vector Machine) with a leave-one-subject-out cross-validation. The SVM approach showed the highest performance to predict running velocity (r = 0.91) when compared to univariate approaches using PL (r = 0.62), AvNetForce (r = 0.73) and HR only (r = 0.87). In conclusion, the presented multivariate ML approach is able to predict running velocity better than univariate ones, and the model is generalizable across subjects.

Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue?

“…By increasing the sample size, it may be possible to obtain more precise results and to test more intricate machines, such as deep learning. However, it has been showed that ML approaches similar to those used in this study can provide very highly accurate estimations of physiological and performance parameters, and that they are superior to univariate methods [ 62 , 63 ]. Nevertheless, the proposed model was developed using a cross-validation procedure, thereby ensuring its generalization performance.…”

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson’s Disease: A Study on Speaker Diarization and Classification Techniques

“…The training was performed using nested cross-validation (nCV), where the dataset is divided into folds, and the model is trained repeatedly on all but one fold of the data. The inner loop determines the most effective hyperparameters via validation, while the outer loop assesses the model’s performance over iterations through testing [ 32 , 33 , 34 ]. Specifically, 5-fold cross validation was implemented.…”

Estimation of Heart Rate Variability Parameters by Machine Learning Approaches Applied to Facial Infrared Thermal Imaging

“…An SVR approach was used as previously described ( 44 ). In this case, the SVR-based models with linear kernel were developed to predict the different HRV metrics [i.e., mean HR (beats/min), mean RR (ms), LF/HF, SDDN (ms), RMSSD (ms), pNN50 (%), and TINN (ms)] evaluated from the PPG signals.…”