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Research Papers

The Effects of Personalized Versus Generic Scaling of Body Segment Masses on Joint Torques During Stationary Wheelchair Racing

[+] Author and Article Information
Amy R. Lewis

Movement Science,
Australian Institute of Sport,
Canberra 2617, Australia;
School of Mechanical Engineering,
Faculty of Engineering,
Computer and Mathematical Sciences,
The University of Adelaide,
Adelaide 5005, Australia
e-mail: amy.lewis@adelaide.edu.au

William S. P. Robertson

School of Mechanical Engineering,
Faculty of Engineering,
Computer and Mathematical Sciences,
The University of Adelaide,
Adelaide 5005, Australia
e-mail: william.robertson@adelaide.edu.au

Elissa J. Phillips

Movement Science,
Australian Institute of Sport,
Canberra 2617, Australia
e-mail: Elissa.phillips@ausport.gov.au

Paul N. Grimshaw

School of Mechanical Engineering,
Faculty of Engineering,
Computer and Mathematical Sciences,
The University of Adelaide,
Adelaide 5005, Australia
e-mail: Paul.grimshaw@adelaide.edu.au

Marc Portus

Movement Science,
Australian Institute of Sport,
Canberra 2617, Australia
e-mail: Marc.portus@ausport.gov.au

1Corresponding author.

Manuscript received December 16, 2017; final manuscript received May 26, 2019; published online July 11, 2019. Assoc. Editor: Steven D. Abramowitch.

J Biomech Eng 141(10), 101001 (Jul 11, 2019) (9 pages) Paper No: BIO-17-1593; doi: 10.1115/1.4043869 History: Received December 16, 2017; Revised May 26, 2019

The anthropometries of elite wheelchair racing athletes differ from the generic, able-bodied anthropometries commonly used in computational biomechanical simulations. The impact of using able-bodied parameters on the accuracy of simulations involving wheelchair racing is currently unknown. In this study, athlete-specific mass segment inertial parameters of the head and neck, torso, upper arm, forearm, hand, thigh, shank, and feet for five elite wheelchair athletes were calculated using dual-energy X-ray absorptiometry (DXA) scans. These were compared against commonly used anthropometrics parameters of data presented in the literature. A computational biomechanical simulation of wheelchair propulsion using the upper extremity dynamic model in opensim assessed the sensitivity of athlete-specific mass parameters using Kruskal–Wallis analysis and Spearman correlations. Substantial between-athlete body mass distribution variances (thigh mass between 7.8% and 22.4% total body mass) and between-limb asymmetries (<62.4% segment mass; 3.1 kg) were observed. Compared to nonathletic able-bodied anthropometric data, wheelchair racing athletes demonstrated greater mass in the upper extremities (up to 3.8% total body mass) and less in the lower extremities (up to 9.8% total body mass). Computational simulations were sensitive to individual body mass distribution, with joint torques increasing by up to 31.5% when the scaling of segment masses (measured or generic) differed by up to 2.3% total body mass. These data suggest that nonathletic, able-bodied mass segment inertial parameters are inappropriate for analyzing elite wheelchair racing motion.

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Figures

Grahic Jump Location
Fig. 1

Example of delineated segments on the (a) high attenuation image and (b) low attenuation image

Grahic Jump Location
Fig. 2

Visual representation of the upper extremity dynamic model and marker placement used in this research

Grahic Jump Location
Fig. 3

Body mass distribution of all athletes. Thigh and shank segments of A1 were excluded from analysis due to the presence of overlapping limb segments, with segment masses normalized against measured total body mass.

Grahic Jump Location
Fig. 4

Appendicular mass asymmetry indices. Positive values indicate right side bias, with larger magnitudes demonstrating a greater presence of asymmetry.

Grahic Jump Location
Fig. 5

Segment masses as presented as a percentage of total body mass. Segment masses are presented as the mean left and right limb measurements for (a) population based averages and (b) distribution across all athletes.

Grahic Jump Location
Fig. 6

Absolute (a) and variation (b) in joint reaction moments calculated using inverse dynamics, between athlete-specific and total body mass-matched scaled models. Relative differences of the wrist flexion and deviation and forearm rotation were not presented based on their small absolute magnitudes.

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