A modified version of the three-compartment model to predict fatigue during submaximal tasks with complex force-time histories

The three-compartment model (3CM) was validated previously for prediction of endurance times by modifying its fatigue and recovery rates. However, endurance times do not typically represent work demands, and it is unknown if the current version of the 3CM is applicable for ergonomics analysis of all...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ergonomics 2016-01, Vol.59 (1), p.85-98
Hauptverfasser: Sonne, Michael W., Potvin, Jim R.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The three-compartment model (3CM) was validated previously for prediction of endurance times by modifying its fatigue and recovery rates. However, endurance times do not typically represent work demands, and it is unknown if the current version of the 3CM is applicable for ergonomics analysis of all occupational tasks. The purpose of this study was to add biological fidelity to the 3CM, and validate the model against a series of submaximal force plateaus. The fatigue and recovery rates were modified to represent graded physiological motor unit characteristics (termed 3CM GMU ). In nine experiments of submaximal efforts, the 3CM GMU produced a root-mean squared difference (RMSD) of 4.1 ± 0.5% MVC over experiments with an average strength loss (i.e. fatigue) of 31.0 ± 1.1% MVC. The 3CM GMU model performed poorly for endurance tasks. The 3CM GMU model is an improvement for evaluating submaximal force patterns consisting of intermittent muscle contractions of the hand and forearm. Practitioner Summary: We modified an existing fatigue model using known physiological properties in order to predict fatigue during nine different submaximal force profiles; consistent with efforts seen in industrial work. We expect this model to be included in digital human modelling software, for the assessment of repetitive work and muscle fatigue in repetitive tasks. Social Media Summary: The proposed model has applications for estimating task fatigue in proactive ergonomic analyses of complex force patterns using digital human models.
ISSN:0014-0139
1366-5847
DOI:10.1080/00140139.2015.1051597