title: Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate.
abstract: This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration.
authors.affiliation: Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, Canada. Electronic address: ahmet-2.kolus@polymtl.ca.
authors.forename: Ahmet
authors.initials: A
authors.lastname: Kolus
authors.affiliation: Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, Canada.
authors.forename: Daniel
authors.initials: D
authors.lastname: Imbeau
authors.affiliation: Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, Canada.
authors.forename: Philippe-Antoine
authors.initials: PA
authors.lastname: Dubé
authors.affiliation: Ministère des Forêts, de la Faune et des Parcs, Direction de la recherche forestière, Québec, Canada.
authors.forename: Denise
authors.initials: D
authors.lastname: Dubeau
mesh_terms: D000328:Adult; D004734:Energy Metabolism / Q000502:physiology*; D017143:Fuzzy Logic; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology; D016571:Neural Networks, Computer; D010101:Oxygen Consumption / Q000502:physiology; D055815:Young Adult
keywords: Adaptive neuro-fuzzy inference system (ANFIS); Flex–HR method; Physical workload