Determining a long term ambulatory load profile of the shoulder joint: Neural networks predicting input for a musculoskeletal model

W. de Vries, H.E.J. Veeger, C.T.M. Baten, F.C.T. van der Helm

    Research output: Contribution to JournalArticleAcademicpeer-review

    Abstract

    To gain more insight in the development of joint damage, a long term load profile of the shoulder joint under daily living conditions is desirable. Standard musculoskeletal models estimate joint load using kinematics and exerted force. However, the latter cannot be measured continuously in ambulatory settings, hampering the use of these models. This paper describes a method for obtaining such a load profile, by training a Neural Network (NN), using kinematics and EMG. A small data set of specified movements with known exerted forces is used in two ways. First, the model calculates several variables of joint load, and a set of Generalized Forces and Net Moments (GFNM) around the model's degrees of freedom. Second, using kinematics and EMG, an NN is trained to predict these GFNM, which can concurrently be used as input for the model, resulting in full model output independent of exerted force. The method is validated with an independent trial. The NN could predict GFNM within 10% relative RMS, compared to output of the model. The NN-model combination estimated joint reaction forces with relative RMS values of 7 to 17%, enabling the estimation of a detailed load profile of the shoulder under daily conditions. © 2011 Elsevier B.V.
    Original languageEnglish
    Pages (from-to)419-428
    JournalHuman Movement Science
    Volume31
    Issue number2
    DOIs
    Publication statusPublished - 2012

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