Machine learning models using combined IMU and ECG-derived HRV data predicted perceived exertion during resistance training with a mean absolute percentage error of 7.71% (Pearson correlation 0.85).
Observational (n=16)
Does supplementing IMU data with ECG-derived HRV parameters improve the prediction of perceived exertion during resistance training?
Combining IMU and ECG-derived HRV parameters in machine learning models accurately predicts subjective exertion during resistance training.
Effect estimate: MAPE 7.71%, r=0.85, R2=0.48
The quantification of subjective exertion during training is an important measurement as it has the potential to avoid injuries resulting from overtraining. In this paper, we present a method to predict the subjective exertion during resistance training using Inertial Measurement Units (IMU) and electrocardiographical data. The participants’ subjective exertion was assessed using a Rating of Perceived Exertion (RPE) scale. We obtained data from 16 participants performing squats on a flywheel training machine while being equipped with six IMU sensors and an electrocardiography (ECG) sensor. Data was analyzed using multiple regressors, such as Support Vector Regression, Random Forests, and Gradient Boosting Regression Trees, to predict the personal exertion level on the processed IMU and ECG data. The best learning model achieved a mean absolute percentage error of 7.71% with a Pearson correlation coefficient of 0.85 and a R 2 of 0.48. Additionally, we investigated the impact of supplementing the IMU data features with ECG-derived heart rate variability (HRV) parameters in the training stage. Our results indicate that the HRV parameters derived from ECG significantly improve prediction results, with the training impulse (TRIMP) parameter acting as the most informative feature for predicting perceived exertion.
Albert et al. (Thu,) conducted a observational in Resistance training (n=16). Machine learning models using IMU and ECG data vs. IMU data alone was evaluated on Prediction of subjective exertion (Rating of Perceived Exertion) (MAPE 7.71%, r=0.85, R2=0.48). Machine learning models using combined IMU and ECG-derived HRV data predicted perceived exertion during resistance training with a mean absolute percentage error of 7.71% (Pearson correlation 0.85).