Dynamic Digital Human Model for ergonomic assessment based on human-like behaviour and requiring a reduced set of data for a simulation

Ergonomic methods for biomechanical risk factors assessment during work activity are usually based on human operator’s postures and forces while performing the work task. A basic analysis of the task can rely on questionnaires, interviews and video analysis, but a more accurate and comprehensive analysis requires collection of exertion (force sensor and/or electromyography) and posture data (e.g. motion capture technique). Such an analysis entails complex and expensive instrumentation that may hamper task performance. In recent years, an alternative solution appeared with the use of digital human models (DHM) for ergonomics analysis. Actually, using industrial DHM software packages available for ergonomic assessment is usually a complex and time-consuming task. For instance, simulation may be built up like a cartoon through interactive manual positioning of the DHM with mouse, menus and keyboard, which entails expert skills in ergonomics and human motion. One can also use a tracking system or motion capture to get realistic simulations but this requires extensive instrumentation, a full scale mock-up of the future workstation, or a similar one, and tricky motion capture data processing. Simulations can eventually be based on pre-defined human motion libraries (reach, grasp...), which usually look quite unnatural. Besides postures, ergonomics assessment also needs at least an estimate of exertions and task execution time. For these reasons, using industrial DHM software for biomechanical risk factors assessment based on simulations of industrial or experimental work task situations may lead to significantly misleading stress estimation.
A challenging aim therefore consists in developing a virtual human model capable of computing automatic, dynamic, realistic movements and internal characteristics (position, velocities, accelerations and torques) in quasi-real time, based on a simple description of the future work task, to achieve realistic ergonomics assessments of various work task scenarii at an early stage of the design process. We have developed such a dynamic DHM automatically controlled in force and acceleration [1], inspired by human motor control [2] and based on robotics and physics simulation (in our simulation framework, the DHM motion is dictated by real-world Newtonian physical and mechanical simulation, along with automatic control of applied forces and torques). Our controller performs multiple tasks simultaneously (balance, contacts, manipulation) in real time with human-like feedforward force and impedance [3].
An experimental insert-fitting activity has been simulated and assessed based on OCRA ergonomic index. In comparison with a real human data-based assessment, consistent results have been obtained. The most interesting property of our DHM is that it requires minimal information for a simulation: a starting point, an intermediate point for obstacle avoidance and an end point, along with the applied force for insert clip- ping. Moreover, changing the subject’s anthropometry and the scenario does not require new trajectory specification nor additional tuning. Joint torques, DHM movements and trajectory and their related OCRA assessment are realistic and consistent with human-like behaviour and performance.

References
[1] G. De Magistris, A. Micaelli, C. Andriot, J. Savin, and J. Marsot, ”Dynamic virtual manikin control design for the assessment of the workstation ergonomy,” in: First International Symposium on Digital Human Modeling, Lyon, 2011.
[2] E. Todorov and M. I. Jordan, ”Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements,” Journal of Neurophysiology, vol. 80, pp. 697-714, 1998.
[3] E. Burdet, R. Osu, D. W. Franklin, T. E. Milner, and M. Kawato, ”The central nervous system stabilizes unstable dynamics by learning optimal impedance,” Nature, vol. 414, pp. 446-449, 2001.