Simulation Notes Europe, Volume 27(1), March 2017

Case Studies for a Markov Chain Approach to Analyze Agent-based Models

Simulation Notes Europe SNE 27(1), 2017, 33-36
DOI: 10.11128/


Digital human models are already in use for validating manual work in terms of risk prevention and ergonomics. However, modelling different work activities is mostly very time-consuming and inefficient. This is because digital human models are considered as machines with more than 100 degrees of freedom to be specified for one pose. ema, however, the so called editor for manual work activities, treats its digital humans as virtual workers. By defining work instructions, the modelling process is much faster and more intuitive compared to efforts specifying individual poses. Furthermore, the implemented work instructions are more accurate and realistic as a result of theoretical development and empirical validation by means of motion capturing technologies. Newest work operations also allow the planning of human-machine-collaboration leading to the validation of interactive human-robot-scenarios. In this paper, features of ema are presented, including manual work modelling, time analysis and ergonomic evaluation.