Cardiorespiratory fitness estimation in free-living using wearable sensors

Highlights • Used machine learning methods to determine multiple level of context in free living and contextualize heart rate data • Estimated cardiorespiratory fitness (CRF) using contextualized heart rate in free living, without laboratory protocols • Reduced CRF estimation error by up to 22.6% co...

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Bibliographic Details
Published inArtificial intelligence in medicine Vol. 68; pp. 37 - 46
Main Authors Altini, Marco, Casale, Pierluigi, Penders, Julien, Amft, Oliver
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 01.03.2016
Subjects
Adult
Artificial Intelligence
Bayesian models
Biosensing Techniques
Cardiorespiratory Fitness
Context recognition
Estimates
Expert systems
Female
Fitness
Heart rate
Humans
Internal Medicine
Male
Mathematical models
Other
Sensors
Topic models
Wearable
Young Adult
Bayesian models
Cardiorespiratory fitness
Topic models
Context recognition
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Summary:Highlights • Used machine learning methods to determine multiple level of context in free living and contextualize heart rate data • Estimated cardiorespiratory fitness (CRF) using contextualized heart rate in free living, without laboratory protocols • Reduced CRF estimation error by up to 22.6% compared to other methods • The proposed CRF estimation method does not require specific exercise and was validated against VO2max
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ISSN:0933-3657
1873-2860
DOI:10.1016/j.artmed.2016.02.002