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Sci Rep. 2017 Apr 5;7:45738. doi: 10.1038/srep45738.

Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living.

Author information

1
Faculty of Applied Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.
2
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brasília, Distrito Federal, Brazil.
3
Departamento de Fisioterapia, Universidade Ibirapuera, São Paulo, Brazil.
4
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.
5
Schlegel-University of Waterloo Research Institute for Aging, Waterloo, Ontario, Canada.

Abstract

Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured and the prediction bias was 0.2 ml·min-1·kg-1. The MNG calculated based on predicted was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.

PMID:
28378815
PMCID:
PMC5381118
DOI:
10.1038/srep45738
[Indexed for MEDLINE]
Free PMC Article

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