{"id":81974,"date":"2019-01-30T12:17:02","date_gmt":"2019-01-30T17:17:02","guid":{"rendered":"https:\/\/mrnf.gouv.qc.ca\/nos-publications\/neuro-fuzzy-inference-systems-k-fold-cross-validation\/"},"modified":"2019-01-30T12:17:02","modified_gmt":"2019-01-30T17:17:02","slug":"neuro-fuzzy-inference-systems-k-fold-cross-validation","status":"publish","type":"cpt_publication","link":"https:\/\/mrnf.gouv.qc.ca\/en\/our-publications\/neuro-fuzzy-inference-systems-k-fold-cross-validation\/","title":{"rendered":"Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate"},"content":{"rendered":"\n<p>Published in <b>Applied Ergonomics 50: 68-78.<a href=\"http:\/\/dx.doi.org\/10.1016\/j.apergo.2015.03.001\" target=\"_blank\" rel=\"noopener noreferrer\"> http:\/\/dx.doi.org\/10.1016\/j.apergo.2015.03.001<\/a><\/b><\/p>\n<p>This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (VO<sub>2<\/sub>) 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 VO<sub>2<\/sub> were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated VO<sub>2<\/sub> in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg<sup>-1<\/sup> min<sup>-1<\/sup>) demonstrated better performance than Rennie et al.&#8217;s (MAE = 7 ml kg<sup>-1<\/sup> min<sup>-1<\/sup>) and Keytel et al.&#8217;s (MAE = 6 ml kg<sup>-1<\/sup> min<sup>-1<\/sup>) models, and comparable performance with the standard Flex-HR method (MAE= 2.3 ml kg<sup>-1<\/sup> min<sup>-1<\/sup>) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for VO<sub>2<\/sub> estimation without the need for individual calibration.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"template":"","format":"standard","cptt_secteur":[1179],"cptt_theme":[1180,1197,1181],"cptt_categ_publi":[1149],"cptt_auteurs_ministeriels":[1080],"class_list":["post-81974","cpt_publication","type-cpt_publication","status-publish","format-standard","hentry","cptt_secteur-forests","cptt_theme-forestry-research","cptt_theme-forestry-work","cptt_theme-forests","cptt_categ_publi-scientific-article","cptt_auteurs_ministeriels-dubeau-denise"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate - Minist\u00e8re des Ressources naturelles et des For\u00eats<\/title>\n<meta name=\"description\" content=\"Study on adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mrnf.gouv.qc.ca\/en\/our-publications\/neuro-fuzzy-inference-systems-k-fold-cross-validation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate - 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