Thoracic Injury Prediction via Digital Convolution Theory

A dynamic characterization of the human thorax, in the form of a digital impulsive response signature, has been obtained which links the acceleration response of the struck side with the far side of the thorax under side impact conditions. This dynamic characterization was obtained by a unique combination of digital convolution theory, least squares approximation techniques, and a digital set of cadaver impact data. It has proven itself accurate in predicting the maximum relative acceleration, velocity and displacement between the left and the right lateral aspects of the thorax for a variety of impact conditions including lateral pendulum impacts, lateral rigid walls impacts at 15 and 20 mph and lateral impacts into padded walls at 20 mph.

Detailed discussions of the theory, the derivation of the various thoracic response signatures and their correspondence with actual data, the utilization of these response functions to predict injury, and the application of this technique to identify promising safety systems design strategies is presented.