A physiology-based inverse dynamic analysis of human gait using sequential convex programming: a comparative study

This paper presents an enhanced version of the previously proposed physiological inverse approach (PIA) to calculate musculotendon (MT) forces and evaluates the proposed methodology in a comparative study. PIA combines an inverse dynamic analysis with an optimisation approach that imposes muscle phy...

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Bibliographic Details
Published in:Computer methods in biomechanics and biomedical engineering Vol. 15; no. 10; pp. 1093 - 1102
Main Authors: De Groote, F., Demeulenaere, B., Swevers, J., De Schutter, J., Jonkers, I.
Format: Journal Article
Language:English
Published: England Taylor & Francis Group 01.10.2012
Subjects:
Algorithms
Computer Simulation
dynamic simulations
gait
Gait - physiology
Humans
Joints - physiology
Leg - physiology
Models, Biological
motion analysis
Muscle Contraction - physiology
muscle control
Muscle, Skeletal - physiology
optimisation
Reproducibility of Results
Sensitivity and Specificity
ISSN:1025-5842, 1476-8259, 1476-8259
Online Access:Get full text
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Summary:This paper presents an enhanced version of the previously proposed physiological inverse approach (PIA) to calculate musculotendon (MT) forces and evaluates the proposed methodology in a comparative study. PIA combines an inverse dynamic analysis with an optimisation approach that imposes muscle physiology and optimises performance over the entire motion. To solve the resulting large-scale, nonlinear optimisation problem, we neglected muscle fibre contraction speed and an approximate quadratic optimisation problem (PIA-QP) was formulated. Conversely, the enhanced version of PIA proposed in this paper takes into account muscle fibre contraction speed. The optimisation problem is solved using a sequential convex programing procedure (PIA-SCP). The comparative study includes PIA-SCP, PIA-QP and two commonly used approaches from the literature: static optimisation (SO) and computed muscle control (CMC). SO and CMC make simplifying assumptions to limit the computational time. Both methods minimise an instantaneous performance criterion. Furthermore, SO does not impose muscle physiology. All methods are applied to a gait cycle of six control subjects. The relative root mean square error averaged over all subjects, , between the joint torques simulated from the optimised activations and the joint torques obtained from the inverse dynamic analysis was about twice as large for SO (  = 86) as compared with CMC (  = 39) and PIA-SCP (  = 50). was at least twice as large for PIA-QP (  = 197) than for all other methods. As compared with CMC, muscle activation patterns predicted by PIA-SCP better agree with experimental electromyography (EMG). This study shows that imposing muscle physiology as well as globally optimising performance is important to accurately calculate MT forces underlying gait.
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ISSN:1025-5842
1476-8259
1476-8259
DOI:10.1080/10255842.2011.571679