# Machine Learning Regressors to Estimate Continuous Oxygen Uptakees (\(\dot{VO}_{2}\))
## 4 Discussion
We use machine learning models like XGBoost and LightGBM for real-time continuous \(\dot{V}O_{2}\) estimation, relying on variables such as age, weight, height, heart rate, sex, and time--readily available from wearables. In contrast, the study focuses on submaximal exercise stages, which may limit real-time application. Our dataset, primarily composed of athletes from PhysioNet, offers a more homogeneous sample but limits generalizability to broader populations. The previous study likely includes a more diverse population, potentially increasing variability but broadening the applicability of its findings. However, we use 5-fold cross-validation to ensure robustness, while the [PERSON], [PERSON] et al. study may follow a different validation approach. Both studiesuse metrics like MAE and RMSE, allowing for direct comparison. However, our focus on minimizing real-time prediction errors better suits continuous monitoring applications. Hyperparameter tuning played a critical role in our model's optimization, particularly with XGBoost, using random search to refine key parameters. The [PERSON], [PERSON] et al. study may adopt a different strategy for model optimization, highlighting varying approaches to improving performance. In terms of practical application, our study is designed for real-time \(\dot{V}O_{2}\) estimation using wearable devices, whereas the study focuses on retrospective analysis. Both studies acknowledge limitations; our study highlights dataset imbalance and suggests more diverse samples for future research, while the [PERSON], [PERSON] et al. study likely addresses the limitations of submaximal stages and population diversity. The first significant limitation of this study is that the model relies primarily on data from athletes, which may not represent the general population or those with sedentary lifestyles. Additionally, the lack of data from individuals with chronic health conditions, such as cardiovascular diseases or COPD, restricts its applicability. Furthermore, the model's accuracy depends on the precision and consistency of data from wearable devices, which can be affected by sensor performance and user adherence, potentially impacting real-world reliability. By employing adaptive learning systems, wearables can be generalized across diverse populations, including non-athletic individuals and those with chronic conditions, addressing the generalizability and accuracy issues noted in traditional \(\dot{V}O_{2 max}\) estimation methods. Second, while our study provides valuable insights into \(\dot{V}O_{2}\) estimation, it is important to recognize the influence of gender imbalance in our sample. Specifically, 87.76% of the participants were male, and only 12.24% were female. This imbalance may limit the generalizability of our findings, particularly concerning female populations, as differences in physiological responses between genders could affect \(\dot{V}O_{2}\) estimation. The integration of such technologies not only addresses the specific limitations underlined by PhysioNet but also highlights a significant opportunity to revolutionize exercise monitoring and optimization. This approach ensures that individuals receive the most beneficial and safe exercise prescriptions that are tailored to their unique physiological profiles. By enhancing the capability of wearables to provide real-time feedback and personalized training regimens, this research contributes to the growing field of personalized medicine and digital health, demonstrating the extensive potential of wearable technology in improving health management and fitness strategies across various populations. In comparison to other studies, our findings underscore the significance of continuous \(\dot{V}O_{2}\) estimation in enhancing personalized training and health management. While traditional methods have not entailed providing real-time feedback, wearable devices offer a promising solution by continuously monitoring \(\dot{V}O_{2}\) levels during physical activity.