Cardiorespiratory fitness estimation in free-living using wearable sensors

Artificial Intelligence in Medicine

Volumn 68, Issue , 2016, Pages 37-46

  Altini, Marco ^a,b   Casale, Pierluigi ^c   Penders, Julien ^c   Amft, Oliver ^d  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b BLOOM TECHNOLOGIES   (Belgium)

^c Imec Netherlands   (Netherlands)

^d UNIVERSITY OF PASSAU   (Germany)

Author keywords

Bayesian models; Cardiorespiratory fitness; Context recognition; Topic models

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; HEALTH; REGRESSION ANALYSIS; WEARABLE TECHNOLOGY;

ARTIFICIAL INTELLIGENCE METHODS; BAYESIAN MODEL; CARDIORESPIRATORY FITNESS; COMPUTATIONAL FRAMEWORK; CONTEXT RECOGNITION; ESTIMATION MODELS; HIERARCHICAL BAYESIAN; TOPIC MODEL;

WEARABLE SENSORS;

ACCELEROMETER; ADULT; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CARDIORESPIRATORY FITNESS; CONCEPTUAL FRAMEWORK; ELECTROCARDIOGRAPH; FEMALE; HEART RATE; HUMAN; HUMAN EXPERIMENT; MALE; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PHYSICAL ACTIVITY; PRIORITY JOURNAL; SENSOR; YOUNG ADULT; GENETIC PROCEDURES;

ADULT; ARTIFICIAL INTELLIGENCE; BIOSENSING TECHNIQUES; CARDIORESPIRATORY FITNESS; FEMALE; HUMANS; MALE; YOUNG ADULT;

EID: 84977830651     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2016.02.002     Document Type: Article

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