Estimating oxygen uptake and energy expenditure during treadmill walking by neural network analysis of easy-to-obtain inputs.
The study of oxygen uptake (V̇o2) dynamics during walking exercise transitions adds valuable information regarding fitness. However, direct V̇o2measurements are not practical for general population under realistic settings. Devices to measure V̇o2are 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 V̇o2dynamics 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 V̇o2response at the gas exchange threshold estimated during the incremental exercise. The measured V̇o2was used to train an artificial neural network to create an algorithm able to predict the V̇o2based on easy-to-obtain inputs. The dynamics of the V̇o2response during exercise transition were evaluated by exponential modeling. Within each participant, the predicted V̇o2was strongly correlated to the measured V̇o2( = 0.97 ± 0.0) and presented a low bias (~0.2%), enabling the characterization of the V̇o2dynamics 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.
Faculty of Applied Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.|Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasília, Brazil.
Thomas
T
Beltrame
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.|Schlegel-University of Waterloo Research Institute for Aging, Waterloo, Ontario, Canada; and.
Robert
R
Amelard
Faculty of Applied Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.|Faculty of Health Sciences, Division of Natural Sciences, Franklin Pierce University, Rindge, New Hampshire.
Rodrigo
R
Villar
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.
Mohammad J
MJ
Shafiee
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.|Schlegel-University of Waterloo Research Institute for Aging, Waterloo, Ontario, Canada; and.
Alexander
A
Wong
Faculty of Applied Health Sciences, University of Waterloo, Waterloo, Ontario, Canada; hughson@uwaterloo.ca.|Schlegel-University of Waterloo Research Institute for Aging, Waterloo, Ontario, Canada; and.
Richard L
RL
Hughson
D000328:Adult; D004734:Energy Metabolism / Q000502:physiology*; D015444:Exercise / Q000502:physiology*; D005080:Exercise Test / Q000379:methods; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D010100:Oxygen / Q000378:metabolism*; D010101:Oxygen Consumption / Q000502:physiology*; D005082:Physical Exertion / Q000502:physiology; D016138:Walking / Q000502:physiology*; D055815:Young Adult
D010100:Oxygen
aerobic fitness; machine learning; oxygen uptake kinetics