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

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

Richard Preuss

School of Physical & Occupational Therapy, McGill University, Montreal, Quebec, H3G 1Y5, Canada

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

Hossein Rouhani

Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada

1Corresponding author.

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


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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