학술논문
Estimation of Instantaneous Oxygen Uptake During Exercise and Daily Activities Using a Wearable Cardio-Electromechanical and Environmental Sensor
Document Type
Periodical
Source
IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. Biomedical and Health Informatics, IEEE Journal of. 25(3):634-646 Mar, 2021
Subject
Language
ISSN
2168-2194
2168-2208
2168-2208
Abstract
Objective: To estimate instantaneous oxygen uptake ${\rm VO}_{2}$ with a small, low-cost wearable sensor during exercise and daily activities in order to enable monitoring of energy expenditure (EE) in uncontrolled settings. We aim to do so using a combination of seismocardiogram (SCG), electrocardiogram (ECG) and atmospheric pressure (AP) signals obtained from a minimally obtrusive wearable device. Methods: In this study, subjects performed a treadmill protocol in a controlled environment and an outside walking protocol in an uncontrolled environment. During testing, the COSMED K5 metabolic system collected gold standard breath-by-breath (BxB) data and a custom-built wearable patch placed on the mid-sternum collected SCG, ECG and AP signals. We extracted features from these signals to estimate the BxB ${\rm VO}_{2}$ data obtained from the COSMED system. Results: In estimating instantaneous ${\rm VO}_{2}$, we achieved our best results on the treadmill protocol using a combination of SCG (frequency) and AP features (RMSE of 3.68 $\pm$ 0.98 ml/kg/min and R 2 of 0.77). For the outside protocol, we achieved our best results using a combination of SCG (frequency), ECG and AP features (RMSE of 4.3 $\pm$ 1.47 ml/kg/min and R 2 of 0.64). In estimating ${\rm VO}_{2}$ consumed over one minute intervals during the protocols, our median percentage error was 15.8$\text{}\%$ for the treadmill protocol and 20.5$\text{}\%$ for the outside protocol. Conclusion: SCG, ECG and AP signals from a small wearable patch can enable accurate estimation of instantaneous ${\rm VO}_{2}$ in both controlled and uncontrolled settings. SCG signals capturing variation in cardio-mechanical processes, AP signals, and state of the art machine learning models contribute significantly to the accurate estimation of instantaneous ${\rm VO}_{2}$. Significance: Accurate estimation of ${\rm VO}_{2}$ with a low cost, minimally obtrusive wearable patch can enable the monitoring of ${\rm VO}_{2}$ and EE in everyday settings and make the many applications of these measurements more accessible to the general public.