We are happy to announce that a conference paper was accepted for publication in October!
Using Machine Learning to Predict Perceived Exertion During Resistance Training With Wearable Heart Rate and Movement Sensors
Accepted at the IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Authors: Justin Albert, Arne Herdick, Clemens Markus Brahms, Urs Granacher, Bert Arnrich
Abstract: 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 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 R2 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.
This year, IEEE BIBM 2021 has received 727 paper submissions, each paper was assigned to 4 Program Committee members for review. After the rigorous review process, the conference has accepted 143 regular papers (acceptance rate: 19.6%).