Experimental assessment of the quality of ergonomic indicators for dynamic systems computed using a digital human model

Abstract

The growing number of musculoskeletal disorders in industry could be addressed by the use of collaborative robots, which allow the joint manipulation of objects by both a robot and a person. Designing these robots requires to assess the ergonomic benefit they offer. Current methods use a posteriori assessment, i.e. observation of a worker performing the task, and require a physical mock-up of the robot. Moreover, they exclude dynamic phenomena because their measurements require heavy instrumentation. However, collaborative robots are not static objects, but dynamic systems which motion influences and is influenced by the physical interaction with the worker. Plus, the worker him/herself is also a dynamic system, on which dynamic phenomena have ergonomic consequences, even without the presence of a collaborative robot. In order to perform more thorough assessments of the ergonomic performances of dynamic systems, it is proposed to use a dynamic digital human model (DHM) for the evaluation, associated with a dedicated ergonomic metric. This paper presents preliminary results on three ergonomic indicators formulated to meet the requirements of ergonomic evaluations of dynamic systems. They evaluate respectively the position of the worker, his physical effort and the energy spent during the task. The same manual task is performed by seven human subjects under different time, load and geometric constraints. Each performance is recorded and replayed with a dynamic DHM in a dynamic simulation framework, in order to calculate the values of the indicators. All three indicators are strongly affected by the geometric parameters in a way that is consistent with ergonomic guidelines. Besides, a linear correlation between the values of the indicators and the strenuousness perceived by the subjects is observed. Moreover, the results show that the relevance of an indicator is strongly affected by the task features, especially its duration. Future work will be directed towards automatic selection of relevant indicators for a given task.