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 L4 vertebra to vertical accelerations applied at the seat. A RNN 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 backpropagation algorithm was used to train the network by adjusting network parameters to minimize 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.


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    Title :

    Modelling the dynamic response of the human spine to shock and vibration using a recurrent neural network


    Additional title:

    Modellierung des dynamischen Antwortverhaltens des menschlichen Rückgrats auf Stöße und Schwingungen mit Hilfe eines neuronalen Netzes


    Contributors:
    Nicol, J. (author) / Morrison, J. (author) / Roddan, G. (author) / Rawicz, A. (author)

    Published in:

    Heavy Vehicle Systems ; 4 , 2-4 ; 145-165


    Publication date :

    1997


    Size :

    21 Seiten, 13 Bilder, 1 Tabelle, 34 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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