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J Appl Physiol (1985). 2018 Feb 1;124(2):473-481. doi: 10.1152/japplphysiol.00299.2017. Epub 2017 Jun 8.

Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

Author information

1
Faculty of Applied Health Sciences, University of Waterloo , Waterloo, Ontario , Canada.
2
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasília, Distrito Federal , Brazil.
3
Department of Systems Design Engineering, University of Waterloo , Waterloo, Ontario , Canada.
4
Schlegel-University of Waterloo Research Institute for Aging , Waterloo, Ontario , Canada.

Abstract

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o2) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o2 kinetics). This study evaluated aerobic system dynamics based on predicted V̇o2 data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACCHIP), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o2. The aerobic system analysis was based on the frequency-domain analysis of ACCHIP and predicted V̇o2 data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACCHIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o2 data during μADL correlated with the temporal characteristics of measured V̇o2 data during laboratory-controlled protocol ([Formula: see text] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

KEYWORDS:

aerobic fitness; kinetics; machine learning; oxygen uptake; smart devices

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