Metabolic Oxygen Uptake Prediction
Overview of the metabolic oxygen uptake prediction system.
The body's ability to metabolize oxygen is an important indicator of cardiorespiratory health. The rate of oxygen consumption (VO2) is a vitally important indicator of cardiorespiratory health. Specifically, measuring how the body's VO2 changes according to physical demand may provide important indicators of cardiac and cellular metabolic function. However, VO2 is currently assessed in a laboratory environment with uncomfortable gas exchange mask devices. Knowing VO2 dynamics during activities of daily living may provide important pre-clinical indicators of cardiorespiratory health.
In collaboration with the Vascular Aging and Brain Health lab, we developed a non-linear deep regression model for estimating real-time VO2 using non-invasive commercially available wearable sensors. Using a smart shirt with embedded cardiorespiratory sensors, non-linear regression models were developed using a pseudorandom ternary sequence with controlled system inputs. The resulting VO2 estimates were comparable to results using a gas exchange mask, enabling continuous VO2 monitoring during treadmill running and activities of daily living. Please see the publications below for more information.
In collaboration with the Vascular Aging and Brain Health lab, we developed a non-linear deep regression model for estimating real-time VO2 using non-invasive commercially available wearable sensors. Using a smart shirt with embedded cardiorespiratory sensors, non-linear regression models were developed using a pseudorandom ternary sequence with controlled system inputs. The resulting VO2 estimates were comparable to results using a gas exchange mask, enabling continuous VO2 monitoring during treadmill running and activities of daily living. Please see the publications below for more information.
Related Publications
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Journal Publications
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T. Beltrame, R. Amelard, A. Wong, R. L. Hughson, "Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living," Scientific Reports, Submitted.
T. Beltrame, R. Amelard, R. Villar, M. J. Shafiee, A. Wong, R. L. Hughson, “Estimating oxygen uptake and energy expenditure during treadmill walking by neural network analysis of easy-to-obtain inputs,” Journal of Applied Physiology, vol. 121, pp. 1226-1233, 2016. |