title: Determining a long term ambulatory load profile of the shoulder joint: neural networks predicting input for a musculoskeletal model.
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.
authors: Department of Biomechanical Engineering, Faculty of Mechanical, Maritime & Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands. w.devries@rrd.nl
authors: W H K
authors: WH
authors: de Vries
authors: H E J
authors: HE
authors: Veeger
authors: C T M
authors: CT
authors: Baten
authors: F C T
authors: FC
authors: van der Helm
mesh_terms: D000328:Adult; D001696:Biomechanical Phenomena / Q000502:physiology; D004576:Electromyography; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008953:Models, Anatomic; D009043:Motor Activity / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D016571:Neural Networks, Computer; D010865:Pilot Projects; D012016:Reference Values; D012785:Shoulder Joint / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D016474:Weight-Bearing / Q000502:physiology*