Summary
Published in Applied Ergonomics 50: 68-78. http://dx.doi.org/10.1016/j.apergo.2015.03.001
This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (VO2) 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 VO2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated VO2 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 VO2 estimation without the need for individual calibration.
Sector(s):
Forests
Categorie(s):
Scientific Article
Theme(s):
Forestry Research, Forestry Work, Forests
Departmental author(s):
Author(s)
KOLUS, Ahmet, Daniel IMBEAU, Philippe-Antoine DUBÉ and Denise DUBEAU
Year of publication :
2015
Format :
PDF available upon request
How to get the publication :
Keywords :
méthode FlexeHR, Charge de travail physique, systèmes d'inférence neuronaux adaptifs, débroussailleur, article scientifique de recherche forestière, travail forestier, forestry work, FlexeHR method, physical workload, adaptive neuro-fuzzy inference system (ANFIS), brushcutter
