ArticleScientific reports2017
Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.Journal of applied physiology (Bethesda, Md. : 1985) · 2018Trial
- Prediction of oxygen uptake in dragon boat athletes using machine learning and multimodal physiological signals.Frontiers in physiology · 2026Article
- Estimation of walking energy expenditure using a single sacrum-mounted IMU based on biomechanically-informed machine learning.Scientific reports · 2025Article
- Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion.Sensors (Basel, Switzerland) · 2025Article
- Quantifying training response in cycling based on cardiovascular drift using machine learning.Frontiers in artificial intelligence · 2025Article
- Wearable Sensors as a Preoperative Assessment Tool: A Review.Sensors (Basel, Switzerland) · 2024Review
- The role of machine learning methods in physiological explorations of endurance trained athletes: a mini-review.Frontiers in sports and active living · 2024Review
- Article
- Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data.PloS one · 2023Article
- The role of the microcirculation and integrative cardiovascular physiology in the pathogenesis of ICU-acquired weakness.Frontiers in physiology · 2023Review
- Cardiorespiratory Fitness Estimation Based on Heart Rate and Body Acceleration in Adults With Cardiovascular Risk Factors: Validation Study.JMIR cardio · 2022Article
- Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors.Sensors (Basel, Switzerland) · 2022Article
- Enhancing instantaneous oxygen uptake estimation by non-linear model using cardio-pulmonary physiological and motion signals.Frontiers in physiology · 2022Article
- Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities.NPJ digital medicine · 2021Article
- Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities.NPJ digital medicine · 2021Article
- Article
- Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors.Journal of applied physiology (Bethesda, Md. : 1985) · 2019Article
- Mean Normalized Gain: A New Method for the Assessment of the Aerobic System Temporal Dynamics during Randomly Varying Exercise in Humans.Frontiers in physiology · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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