Today’s anthropomorphic test devices (ATD) derive their behavior from cadaver test data. This same statement also applies to numerical models of these physical ATDs, and equally to the more sophisticated numerical human body models. Across the wide spectrum of automotive and aerospace crash scenarios, the prediction of occupant responses relies mainly on joint properties that are inherent in such behavioral representations. These do not account for the muscle reflexes of tensing and bracing. Most ATDs, and especially the Hybrid IIIs, are relatively rigid and their response will effectively represent occupants subjected to high speed impacts.

A series of numerical active human (AH) body models have been developed for the 5th, 50th and 95th percentile of human subjects using multi-body modelling that incorporates joints with active torque behavior. In addition to the standard joint torque resistance, active joint behavior is implemented numerically in these AH-models using proportional-integral-derivative (PID) control methods to deliver torque resistance representative of active muscle responses. Active torque behavior for selected human body joints is achieved by optimizing PID gain parameters to correlate with test responses of human volunteer test data. The result of this work was applied to this first generation of active joint human models.

The potential of human body models with active joints is demonstrated in a vehicle rollover situation. The specific case of vehicle rollover provides a crash scenario where the occupant’s accident awareness response is likely to influence tensing and active joint behavior at various stages during the accident. These simulations highlight the influence of muscle tensing and joint bracing on potential injury risk.

This method of modelling the active joint torque seeks to mimic the complex behavior of muscles. It provides an efficient modelling technique that can be used to simulate long duration events (such as vehicle rollover) that in the past may have been considered less than optimal for the more complex human models. The ability to activate or deactivate the joint behavior to account for conscious muscle tensing will allow the analysis of various occupant awareness states during a rollover accident.

It is anticipated that the addition of active joint behavior will provide a more accurate numerical representation of human body kinematics and hence improve the quality of the prediction of the risk of injury that can be deduced from simulations. The ability to activate joint behavior to account for conscious muscle reflexes will also extend the range of crash scenarios which can be modelled effectively.