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Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system

Summary

Published in Applied Ergonomics 54: 158-168 https://doi.org/10.1016/j.apergo.2015.12.006

In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (VO2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment.

Sector(s): 

Forests

Categorie(s): 

Scientific Article

Theme(s): 

Forestry Research, Forestry Work, Forests

Departmental author(s): 

Dubeau, Denise

Author(s)

KOLUS, Ahmet, Daniel IMBEAU, Philippe-Antoine DUBÉ and Denise DUBEAU

Year of publication :

2016

Format :

PDF available upon request

Keywords :

astreinte physique, fréquence cardiaque, système adaptif d'inférence neuro-floue, travail forestier, article scientifique de recherche forestière, forestry work, work rate, heart rate, Adaptive neuro-fuzzy inference system (ANFIS)

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