Workstation design is one of the most essential components of proactive ergonomics, and digital human models have gained increasing popularity in the analysis and design of current and future workstations (Chaffin 2001). Using digital human technology, it is possible to simulate interactions between humans and current or planned workstations, and conduct quantitative ergonomic analyses based on realistic human postures and motions.
Motion capture has served as the primary means by which to acquire and visualize human motions in a digital environment. However, motion capture only provides motions for a specific person performing specific tasks. Albeit useful, at best this allows for the analysis of current or mocked-up workstations only. The ability to subsequently modify these motions is required to efficiently evaluate alternative design possibilities and thus improve design layouts. Utilizing the Memory-Based Motion Simulation (MBMS) algorithm (Park et al. 2002), movements of a lifting task were recorded by motion capture and then modified to create realistic movements for different scenarios: different statures of the subject and alternative workstation geometries. Based on the motion simulations, the current study suggested a preferred height of the workstation, which was determined by the motion that minimized the calculated low back compression force and joint-strength requirements. Also, the effect of human stature on the biomechanical stresses was evaluated.