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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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
Online AccessGet full text

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Abstract 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
AbstractList In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living. We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants. Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
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
Objective In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. Methods Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living. Results We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants. Conclusions Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
OBJECTIVEIn this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data.METHODSOur methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living.RESULTSWe show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants.CONCLUSIONSOur investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
•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. In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living. We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants. Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
Author Amft, Oliver
Casale, Pierluigi
Altini, Marco
Penders, Julien
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Keywords Bayesian models
Cardiorespiratory fitness
Topic models
Context recognition
Language English
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SSID ssj0007416
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Snippet Highlights • Used machine learning methods to determine multiple level of context in free living and contextualize heart rate data • Estimated...
•Used machine learning methods to determine multiple level of context in free living and contextualize heart rate data.•Estimated cardiorespiratory fitness...
In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. Our methods...
OBJECTIVEIn this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor...
Objective In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data....
SourceID proquest
crossref
pubmed
elsevier
SourceType Aggregation Database
Index Database
Publisher
StartPage 37
SubjectTerms 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
Title Cardiorespiratory fitness estimation in free-living using wearable sensors
URI https://www.clinicalkey.es/playcontent/1-s2.0-S0933365716300598
https://dx.doi.org/10.1016/j.artmed.2016.02.002
https://www.ncbi.nlm.nih.gov/pubmed/26948954
https://search.proquest.com/docview/1782214716
https://search.proquest.com/docview/1787981696
https://search.proquest.com/docview/1808121668
Volume 68
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