Energy expenditure estimation during normal ambulation using triaxial accelerometry and barometric pressure

Energy expenditure (EE) is an important parameter in the assessment of physical activity. Most reliable techniques for EE estimation are too impractical for deployment in unsupervised free-living environments; those which do prove practical for unsupervised use often poorly estimate EE when the subject is working to change their altitude by walking up or down stairs or inclines. This study evaluates the augmentation of a standard triaxial accelerometry waist-worn wearable sensor with a barometric pressure sensor (as a surrogate measure for altitude) to improve EE estimates, particularly when the subject is ascending or descending stairs. Using a number of features extracted from the accelerometry and barometric pressure signals, a state space model is trained for EE estimation. An activity classification algorithm is also presented, and this activity classification output is also investigated as a model input parameter when estimating EE. This EE estimation model is compared against a similar model which solely utilizes accelerometry-derived features. A protocol (comprising lying, sitting, standing, walking, walking up stairs, walking down stairs and transitioning between activities) was performed by 13 healthy volunteers (8 males and 5 females; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system (K4b(2), COSMED, Italy). Activity classification improves from 81.65% to 90.91% when including barometric pressure information; when analyzing walking activities alone the accuracy increases from 70.23% to 98.54%. Using features derived from both accelerometry and barometry signals, combined with features relating to the activity classification in a state space model, resulted in a VO(2) estimation bias of -0.00 095 and precision (1.96SD) of 3.54 ml min(-1) kg(-1). Using only accelerometry features gives a relatively worse performance, with a bias of -0.09 and precision (1.96SD) of 5.99 ml min(-1) kg(-1), with the largest errors due to an underestimation of VO(2) when walking up stairs.