Modelling the dynamic response of the human spine to shock and vibration using a recurrent neural network
by J. Nicol, J. Morrison, G. Roddan, A. Rawicz
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 4, No. 2/3/4, 1997

Abstract: The ability to model the spine's response to mechanical shock and vibration is an important step in assessing the health hazards of repeated impacts to vehicle passengers. Current linear models, such as the Dynamic Response Index (DRI) and the British Standard 6841 filter (BS 6841), perform poorly when the input consists of large-magnitude shocks typical of those experienced by personnel in military vehicles. In this study, a recurrent neural network (RNN) was developed which models the spinal acceleration response of the seated passenger at the LA vertebra to vertical accelerations applied at the seat. ARNN is a universal nonlinear approximator that can, in theory, model any system with memory if trained with a representative set of measured input-output data. The seat-spine system was modelled as a network with four inputs and one output. The back propagation algorithm was used to train the network by adjusting network parameters to minimise the square of the prediction error. The inputs to the network were delayed values of the inputs and outputs. The trained network significantly outperformed the two linear models examined for predicting the z-axis acceleration at the L4 vertebra.

Online publication date: Tue, 18-Jun-2013

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