Estimating oxygen uptake and energy expenditure during treadmill walking by neural network analysis of easy-to-obtain inputs

Abstract

The study of oxygen uptake (V̇o₂) dynamics during walking exercise transitions adds valuable information regarding fitness. However, direct V̇o₂ measurements are not practical for general population under realistic settings. Devices to measure V̇o₂ are associated with elevated cost, uncomfortable use of a mask, need of trained technicians, and impossibility of long-term data collection. The objective of this study was to predict the V̇o₂ dynamics from heart rate and inputs from the treadmill ergometer by a novel artificial neural network approach. To accomplish this, 10 healthy young participants performed one incremental and three moderate constant work rate treadmill walking exercises. The speed and grade used for the moderate-intensity protocol was related to 80% of the V̇o₂ response at the gas exchange threshold estimated during the incremental exercise. The measured V̇o₂ was used to train an artificial neural network to create an algorithm able to predict the V̇o₂ based on easy-to-obtain inputs. The dynamics of the V̇o₂ response during exercise transition were evaluated by exponential modeling. Within each participant, the predicted V̇o₂ was strongly correlated to the measured V̇o₂ ( = 0.97 ± 0.0) and presented a low bias (~0.2%), enabling the characterization of the V̇o₂ dynamics during treadmill walking exercise. The proposed algorithm could be incorporated into smart devices and fitness equipment, making them suitable for tracking changes in aerobic fitness and physical health beyond the infrequent monitoring of patients during clinical interventions and rehabilitation programs.

Keywords: aerobic fitness; machine learning; oxygen uptake kinetics.