Predictive model of the human muscle fatigue: application to repetitive push-pull tasks with light external load
This work addresses muscle fatigue prediction for industrial workers to potentially reduce musculo-skeletal disorders, but it is incremental as it builds on existing models with a specific extension.
The paper tackles the problem of muscle fatigue in repetitive industrial push-pull tasks by proposing a predictive model that extends a static fatigue model to quasi-static situations, incorporating time-varying maximal torque based on posture changes, and evaluates it in a simulation.
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.