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research-article

Optimal Estimation of Anthropometric Parameters for Quantifying Multi-Segment Trunk Kinetics

[+] Author and Article Information
Alireza Noamani

Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada
noamani@ualberta.ca

Albert Vette

Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada; Glenrose Rehabilitation Hospital, Alberta Health Services, 10230 111 Avenue NW, Edmonton, Alberta, T5G 0B7, Canada
vette@ualberta.ca

Richard Preuss

School of Physical & Occupational Therapy, McGill University, Montreal, Quebec, H3G 1Y5, Canada
richard.preuss@mcgill.ca

Milos R. Popovic

Rehabilitation Engineering Laboratory, Lyndhurst Centre, Toronto Rehabilitation Institute –University Health Network, Toronto, Ontario, M4G 3V9, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, M5S 3G9, Canada
milos.popovic@utoronto.ca

Hossein Rouhani

Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada
hrouhani@ualberta.ca

1Corresponding author.

ASME doi:10.1115/1.4040247 History: Received August 08, 2017; Revised May 06, 2018

Abstract

Kinetics assessment of the human head-arms-trunk (HAT) complex using a multi-segment model is required for clinical evaluation of several pathological conditions. Inaccuracies in body segment parameters (BSPs) is a major source of uncertainty in the estimation of the joint moments associated with the multi-segment HAT. Given the large inter-subject variability, there is currently no comprehensive database for the estimation of BSPs for the HAT. We propose a nonlinear, multi-step, optimization-based, non-invasive method for estimating individual-specific BSPs and calculating joint moments in a multi-segment HAT model. Eleven non-disabled individuals participated in a trunk-bending experiment, and their body motion was recorded using cameras and a force plate. A seven-segment model of the HAT was reconstructed for each participant. An initial guess of the BSPs was obtained by individual-specific scaling of the BSPs calculated from the Male Visible Human images. The inter-segmental moments were calculated using both bottom-up and top-down inverse dynamics approaches. Our proposed method adjusted the scaled BSPs and centre of pressure offsets to estimate optimal individual-specific BSPs that minimize the difference between the moments obtained by top-down and bottom-up inverse dynamics approaches. Our results indicate that the proposed method reduced the error in the net joint moment estimation by 77.59% (average among participants). Our proposed method enables accurate estimation of individual-specific BSPs and, consequently, accurate assessment of the three-dimensional kinetics of a multi-segment HAT model.

Copyright (c) 2018 by ASME
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