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TECHNICAL PAPERS: Joint/Whole Body

An Adaptive System Identification Model of the Biomechanical Response of the Human Trunk During Sudden Loading

[+] Author and Article Information
Brad M. Lawrence1

Department of Industrial Engineering,  North Carolina State University, Raleigh, NC 27695Bradley_Lawrence@dell.com

Gregory D. Buckner

Department of Mechanical and Aerospace Engineering,  North Carolina State University, Raleigh, NC 27695

Gary A. Mirka

Department of Industrial Engineering,  North Carolina State University, Raleigh, NC 27695

1

Corresponding author. Present address: 6907 Rimner Cove, Austin, TX 78759.

J Biomech Eng 128(2), 235-241 (Nov 03, 2005) (7 pages) doi:10.1115/1.2165696 History: Received October 30, 2004; Revised November 03, 2005

Sudden loading injuries to the low back are a concern. Current models are limited in their ability to quantify the time-varying nature of the sudden loading event. The method of approach used six males who were subjected to sudden loads. Response data (EMG and kinematics) were input into a system identification model to yield time-varying torso stiffness estimates. The results show estimates of system stiffness in good agreement with values in the literature. The average root mean square error of the model’s predictions of sagittal motion was equal to 0.1deg. In conclusion, system identification can be implemented with minimal error and used to gain more insight into the time-dependent trunk response to sudden loads.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

A rotational biomechanical system representing the trunk during a sudden load

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Figure 2

(a) RBFN structure; (b) function approximation using an RBFN with seven activation functions (c=[−0.50.51.52.53.54.55.5], σ=1.0, w=[1.050.940.901.081.622.142.07])

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Figure 6

Predicted time-varying stiffness from an unexpected loading response

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Figure 7

Predicted time-varying stiffness from an expected loading response

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Figure 3

Adaptive system identification and preprocessing procedure

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Figure 4

Convergence of the network preprocessing algorithm after (a) 5, (b) 50, and (c) 200 iterations

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Figure 5

Convergence of the RBFN after (a) 10, (b) 100, and (c) 170 iterations

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