Identification of pre-impact conditions of a cyclist involved in a vehicle-bicycle accident using an optimized MADYMO reconstruction combined with motion capture.

The aim of the present study was to develop an improved method, using MADYMO multi-body simulation software combined with an optimization method and three-dimensional (3D) motion capture, for identifying the pre-impact conditions of a cyclist (walking or cycling) involved in a vehicle-bicycle accident. First, a 3D motion capture system was used to analyze coupled motions of a volunteer while walking and cycling. The motion capture results were used to define the posture of the human model during walking and cycling simulations. Then, cyclist, bicycle and vehicle models were developed. Pre-impact parameters of the models were treated as unknown design variables. Finally, a multi-objective genetic algorithm, the nondominated sorting genetic algorithm II, was used to find optimal solutions. The objective functions of the walk parameter were significantly lower than cycle parameter; thus, the cyclist was more likely to have been walking with the bicycle than riding the bicycle. In the most closely matched result found, all observed contact points matched and the injury parameters correlated well with the real injuries sustained by the cyclist. Based on the real accident reconstruction, the present study indicates that MADYMO multi-body simulation software, combined with an optimization method and 3D motion capture, can be used to identify the pre-impact conditions of a cyclist involved in a vehicle-bicycle accident.