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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/1570

Title: Use of a Hill-Based Muscle Model in the Fast Orthogonal Search Method to Estimate Wrist Force and Upper Arm Physiological Parameters
Authors: Mountjoy, KATHERINE

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Keywords: Fast Orthogonal Search, sEMG, Surface Electromyography, Hill-based model, optimal joint angle, elbow flexion/extension
Issue Date: 2008
Series/Report no.: Canadian theses
Abstract: Modelling of human motion is used in a wide range of applications. An important aspect of accurate representation of human movement is the ability to customize models to account for individual differences. The following work proposes a methodology using Hill-based candidate functions in the Fast Orthogonal Search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force estimation framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. Surface EMG data from three muscles of the upper arm (biceps brachii, brachioradialis and triceps brachii) were recorded from 10 subjects as they performed isometric contractions at varying elbow joint angles. Estimated muscle activation level and joint kinematic data (joint angle and angular velocity) were utilized as inputs to the FOS model. The resulting wrist force estimations were found to be more accurate for models utilizing Hill-based candidate functions, than models utilizing candidate functions that were not physiologically relevant. Subject-specific estimates of optimal joint angle were determined via frequency analysis of the selected FOS candidate functions. Subject-specific optimal joint angle estimates demonstrated low variability and fell within the range of angles presented in the literature.
Description: Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-10-30 01:32:01.606
URI: http://hdl.handle.net/1974/1570
Appears in Collections:Queen's Theses & Dissertations
Electrical and Computer Engineering Graduate Theses

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