AIMC Topic: Oxygen Consumption

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Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion.

Sensors (Basel, Switzerland)
The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO) while performing the work. The ...

Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study.

PloS one
Accurate assessment of training status in team sports is crucial for optimising performance and reducing injury risk. This pilot study investigates the feasibility of using machine learning (ML) models to estimate oxygen uptake (VO2) with wearable se...

Machine Learning-Based VO Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

Biosensors
The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO is a powerf...

Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.

Expert review of respiratory medicine
BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise that is often not available. We aim to evaluate the utility of ChatGPT (GPT) in interpreting CPET res...

Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.

PloS one
Oxygen consumption ([Formula: see text]) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use ...

Machine learning prediction of pulmonary oxygen uptake from muscle oxygen in cycling.

Journal of sports sciences
The purpose of this study was to test whether a machine learning model can accurately predict VO across different exercise intensities by combining muscle oxygen (MO) with heart rate (HR). Twenty young highly trained athletes performed the following ...

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

European journal of applied physiology
PURPOSE: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical ...

Effect of robot-assisted gait training on improving cardiopulmonary function in stroke patients: a meta-analysis.

Journal of neuroengineering and rehabilitation
OBJECTIVE: Understanding the characteristics related to cardiorespiratory fitness after stroke can provide reference values for patients in clinical rehabilitation exercise. This meta- analysis aimed to investigate the effect of robot-assisted gait t...

Development, validation, and transportability of several machine-learned, non-exercise-based VO prediction models for older adults.

Journal of sport and health science
BACKGROUND: There exist few maximal oxygen uptake (VO) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO is infeasible in large epidemiologic cohort stud...

Mechanisms of Exercise Intolerance Across the Breast Cancer Continuum: A Pooled Analysis of Individual Patient Data.

Medicine and science in sports and exercise
PURPOSE: The purpose of this study is to evaluate the prevalence of abnormal cardiopulmonary responses to exercise and pathophysiological mechanism(s) underpinning exercise intolerance across the continuum of breast cancer (BC) care from diagnosis to...