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Research Papers

The Effects of Prosthesis Inertial Parameters on Inverse Dynamics: A Probabilistic Analysis

[+] Author and Article Information
Brecca M. M. Gaffney

Department of Mechanical and
Materials Engineering,
Human Dynamics Laboratory,
University of Denver,
Denver, CO 80208
e-mail: brecca.gaffney@gmail.com

Cory L. Christiansen

Department of Physical
Medicine and Rehabilitation,
University of Colorado,
Denver, CO 80045;
Denver Geriatric Research Education and
Clinical Center,
VA Eastern Colorado Health Care System,
Denver, CO 80012
e-mail: cory.christiansen@ucdenver.edu

Amanda M. Murray

Department of Physical
Medicine and Rehabilitation,
University of Colorado,
Denver, CO 80045;
Denver Geriatric Research Education and
Clinical Center,
VA Eastern Colorado Health Care System,
Denver, CO 80012
e-mail: Amanda.Murray2@utoledo.edu

Casey A. Myers

Department of Mechanical and
Materials Engineering,
Center for Orthopaedic Biomechanics,
University of Denver,
Denver, CO 80208
e-mail: casey.myers1@gmail.com

Peter J. Laz

Department of Mechanical and
Materials Engineering,
Center for Orthopaedic Biomechanics,
University of Denver,
Denver, CO 80208
e-mail: peter.laz@du.edu

Bradley S. Davidson

Department of Mechanical and
Materials Engineering,
Human Dynamics Laboratory,
University of Denver,
2155 E Wesley Ave. ECS 443,
Denver, CO 80208
e-mail: Bradley.davidson@du.edu

1Corresponding author.

Manuscript received June 7, 2017; final manuscript received September 19, 2017; published online October 31, 2017. Assoc. Editor: Tina Morrison. The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes.

J. Verif. Valid. Uncert 2(3), 031003 (Oct 31, 2017) (8 pages) Paper No: VVUQ-17-1022; doi: 10.1115/1.4038175 History: Received June 07, 2017; Revised September 19, 2017

Joint kinetic measurement is a fundamental tool used to quantify compensatory movement patterns in participants with transtibial amputation (TTA). Joint kinetics are calculated through inverse dynamics (ID) and depend on segment kinematics, external forces, and both segment and prosthetic inertial parameters (PIPS); yet the individual influence of PIPs on ID is unknown. The objective of this investigation was to assess the importance of parameterizing PIPs when calculating ID using a probabilistic analysis. A series of Monte Carlo simulations were performed to assess the influence of uncertainty in PIPs on ID. Multivariate input distributions were generated from experimentally measured PIPs (foot/shank: mass, center of mass (COM), moment of inertia) of ten prostheses and output distributions were hip and knee joint kinetics. Confidence bounds (2.5–97.5%) and sensitivity of outputs to model input parameters were calculated throughout one gait cycle. Results demonstrated that PIPs had a larger influence on joint kinetics during the swing period than the stance period (e.g., maximum hip flexion/extension moment confidence bound size: stance = 5.6 N·m, swing: 11.4 N·m). Joint kinetics were most sensitive to shank mass during both the stance and swing periods. Accurate measurement of prosthesis shank mass is necessary to calculate joint kinetics with ID in participants with TTA with passive prostheses consisting of total contact carbon fiber sockets and dynamic elastic response feet during walking.

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Figures

Grahic Jump Location
Fig. 1

Mean (1 SD) 2.5–97.5% confidence bounds for hip and knee joint moments during the stance and swing period

Grahic Jump Location
Fig. 2

Mean (1 SD) 2.5–97.5% confidence bounds for hip and knee joint moments (flexion/extension (F/E), abduction/adduction (A/A), internal/external rotation (I/E) during the stance and swing period

Grahic Jump Location
Fig. 3

Sensitivity factors calculated between peak hip and knee joint moments and shankPIPs during the stance (top row) and swing (bottom row) periods during one gait cycle. * Indicates highly sensitive (r = 0.6–1.0) and + indicates moderately sensitive (r = 0.4–0.6), both of which are statistically significant (95% confidence interval did not cross zero). The level of significance was set at α = 0.05.

Grahic Jump Location
Fig. 4

Mean (1 SD) error of prosthesis shank mass between experimentally measured shank mass and shank mass estimated using: Intact: intact parameters based on Dempster's regressions (black), Scaled: 50% reduction of intact shank mass (red), and GM: 3.3% of the adjusted body mass

Grahic Jump Location
Fig. 5

(a) Ensemble averages of the hip flexion/extension moment during walking in ten patients with unilateral TTA that was calculated using inverse dynamics from a link-segment model with prosthesis mass that was modeled using: experimental measurements (blue), intact segment parameters based on Dempster's regressions (black) [34], mass reduced to 50% of intact shank mass (red) (2.35% body mass [8,11,17,18]), and modeled with the GM developed by Ferris et al. (green) (3.3% of the ABM [27]). (b) the error between peak flexion/extension moments calculated with shank mass experimentally measured and by modeling it using intact segment parameters (black), 50% scaled reduction (red), or the general model (3.3% ABM) (green). The gray circles indicate individual participant values, and the ratios indicate the number of participants whose peak moment error was larger than that of the peak 2.5% confidence bound determined from the probabilistic analysis. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

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