Loss of balance is a common phenomenon in our society resulting in injuries and even deaths each year. Among other common sources of destabilization such as slips or trips from an obstacle, the public transportation vehicles are a major source of balance-related injuries to its passengers. Accidental data suggest that the passenger casualties in these vehicles are common, especially to the standing and the elderly passengers, mainly due to the sudden acceleration/deceleration changes of the vehicle. These injuries as well as associated discomfort may discourage people from using these means of transport resulting in adverse economic and societal effects. In this context, the security of the standing passengers in these vehicles constitutes the main motivation of this work.Recovering balance from an external disturbance is a complex process which involves a set of phenomenon such as the perception of the disturbance, information processing, decision making and its implementation. Even though experimental research in the fields of biomechanics and neurosciences provide us with a fair understanding of these phenomena separately, we are unaware of a global model which represents the reaction of people in response to the external disturbances to their equilibrium. In this context, the objective of this work is to develop such a numerical tool which can be used for the assessment of risks associated with the loss of balance of the standing passengers. The essential feature of this tool is the prediction of the post-disturbance kinematics of the subjects depending upon the disturbance characteristics (magnitude, duration etc.) as well as the active recovery response. Another key feature is the representation of the reaction of different populations, especially the elderly, by integrating age effects in the model. For the development of the tool, mathematical modeling (e.g. simplified body representations) and control ideas are borrowed from the field of biped robotics which explicitly deals with the balance issues of bipeds. Further development is done in view of human balance recovery (BR) characteristics. The resulting BR tool shows reasonable predictive capacity of a human balance recovery response confirmed by the comparison of model predictions with experimental balance recovery data.
