Estimation of Instantaneous Oxygen Uptake During Exercise and Daily Activities Using a Wearable Cardio-Electromechanical and Environmental Sensor

<italic>Objective:</italic> To estimate instantaneous oxygen uptake <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula> 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. <italic>Methods:</italic> 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 <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula> data obtained from the COSMED system. <italic>Results:</italic> In estimating instantaneous <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula>, we achieved our best results on the treadmill protocol using a combination of SCG (frequency) and AP features (RMSE of 3.68 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.98 ml/kg/min and R<sup>2</sup> 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 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 1.47 ml/kg/min and R<sup>2</sup> of 0.64). In estimating <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula> consumed over one minute intervals during the protocols, our median percentage error was 15.8<inline-formula><tex-math notation="LaTeX">$\text{}\%$</tex-math></inline-formula> for the treadmill protocol and 20.5<inline-formula><tex-math notation="LaTeX">$\text{}\%$</tex-math></inline-formula> for the outside protocol. <italic>Conclusion:</italic> SCG, ECG and AP signals from a small wearable patch can enable accurate estimation of instantaneous <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula>. <italic>Significance:</italic> Accurate estimation of <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula> with a low cost, minimally obtrusive wearable patch can enable the monitoring of <inline-formula><tex-math notation="LaTeX">${\rm VO}_{2}$</tex-math></inline-formula> and EE in everyday settings and make the many applications of these measurements more accessible to the general public.