Predictive model of the human muscle fatigue: application to repetitive push-pull tasks with light external load

TL;DR

This paper develops a predictive model for human muscle fatigue during repetitive push-pull tasks, accounting for changing posture and torque, and evaluates it through simulation to improve ergonomic assessments.

Contribution

It extends existing static fatigue models to dynamic, quasi-static situations considering posture changes, providing a more accurate prediction of muscle fatigue in repetitive tasks.

Findings

The model accurately predicts fatigue progression during simulated tasks.

Posture variations significantly influence muscle fatigue levels.

The approach offers potential for ergonomic risk assessment improvements.

Abstract

Repetitive tasks in industrial works may contribute to health problems among operators, such as musculo-skeletal disorders, in part due to insufficient control of muscle fatigue. In this paper, a predictive model of fatigue is proposed for repetitive push/pull operations. Assumptions generally accepted in the literature are first explicitly set in this framework. Then, an earlier static fatigue model is recalled and extended to quasi-static situations. Specifically, the maximal torque that can be generated at a joint is not considered as constant, but instead varies over time accordingly to the operator's changing posture. The fatigue model is implemented with this new consideration and evaluated in a simulation of push/pull operation. Reference to this paper should be made as follows: Sakka, S., Chablat, D., Ma, R. and Bennis, F. (2015) 'Predictive model of the human muscle fatigue: application to repetitive push-pull tasks with light external load', Int.