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Research Papers

Optimal Estimation of Anthropometric Parameters for Quantifying Multisegment Trunk Kinetics

[+] Author and Article Information
Alireza Noamani

Department of Mechanical Engineering,
University of Alberta,
Edmonton T6G 1H9, AB, Canada
e-mail: noamani@ualberta.ca

Albert H. Vette

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

Richard Preuss

School of Physical & Occupational Therapy,
McGill University,
Montreal H3G 1Y5, QC, Canada
e-mail: richard.preuss@mcgill.ca

Milos R. Popovic

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

Hossein Rouhani

Department of Mechanical Engineering,
University of Alberta,
Edmonton T6G 1H9, AB, Canada
e-mail: hrouhani@ualberta.ca

1Corresponding author.

Manuscript received August 8, 2017; final manuscript received May 6, 2018; published online June 21, 2018. Assoc. Editor: Joel D Stitzel.

J Biomech Eng 140(10), 101003 (Jun 21, 2018) (10 pages) Paper No: BIO-17-1348; doi: 10.1115/1.4040247 History: Received August 08, 2017; Revised May 06, 2018

Kinetics assessment of the human head-arms-trunk (HAT) complex via a multisegment model is a useful tool for objective clinical evaluation of several pathological conditions. Inaccuracies in body segment parameters (BSPs) are a major source of uncertainty in the estimation of the joint moments associated with the multisegment HAT. Given the large intersubject variability, there is currently no comprehensive database for the estimation of BSPs for the HAT. We propose a nonlinear, multistep, optimization-based, noninvasive method for estimating individual-specific BSPs and calculating joint moments in a multisegment HAT model. Eleven nondisabled 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 (MVH) images. The intersegmental moments were calculated using both bottom-up and top-down inverse dynamics approaches. Our proposed method adjusted the scaled BSPs and center of pressure (COP) 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 (defined as the difference between the net joint moment calculated via bottom-up and top-down approaches) by 79.3% (median among participants). Our proposed method enables an optimized estimation of individual-specific BSPs and, consequently, a less erroneous assessment of the three-dimensional (3D) kinetics of a multisegment HAT model.

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Figures

Grahic Jump Location
Fig. 1

Targets for the trunk-bending task were placed at participant-specific distances and heights representing an angular motion of 45 deg of the trunk

Grahic Jump Location
Fig. 2

Markers were placed over the participant's spinal column to form a seven-segment trunk model: Head and neck (HD), upper thoracic (UT), midupper thoracic (MUT), midlower thoracic (MLT), lower thoracic (LT), upper lumbar (UL), lower lumbar (LL), and sacral (SC) segments. The segment-fixed frames for the trunk segments were defined by a marker placed centrally on the spinous process of the caudal vertebra and two markers placed 5 cm laterally of the spinous process of the rostral vertebra. The pelvis-fixed frame was defined by the markers placed on anatomical landmarks of the pelvis. In addition, the local frames for the different segments of the HAT model are shown. For the trunk segments (LL to HD), only one frame is illustrated as an example; the frames for the other trunk segments were defined similarly.

Grahic Jump Location
Fig. 3

The two-step optimization algorithm was used to minimize the error between top-down and bottom-up inverse dynamics approaches. The first step optimized the offset error of the COP measured by the force plate. The range of variation of the COP offsets was constrained with lower and upper bounds of [−1 cm, +1 cm] based on previous studies [12,37]. The second step used the scaled anthropometric data from the MVH and the corrected COP from the previous step to find optimized individual-specific COM, JCR, and mass of each segment. Lower and upper bounds were defined for the normalized mass, COM and JCRs of the segments. Inequality constraints were applied to the position vectors from the reflective marker on the spinous process to the corresponding JCR and COM of each segment for obtaining anatomically sensible position vectors (see Eqs. (8) and (9)). Moreover, an equality constraint was applied to the mass since the total mass of the participant should be preserved. At each step, optimization constraints were used as the criterion.

Grahic Jump Location
Fig. 4

The joint moments calculated via the bottom-up (solid lines) and the top-down (dashed lines) approaches. Black lines indicate the moments obtained before optimization and the red lines indicate the moments obtained after optimization. As such, we expect that the distances between the solid and dashed lines decrease after optimization. Normalized joint moments with respect to the body weight (BW) and trunk height (TH) (expressed in BW × TH%), are depicted for a representative participant. Results are presented for moments in the sagittal, coronal, and transverse planes at all joints. The scale is different for moments in the sagittal, coronal, and transverse planes are different.

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