AbstractThe objective of this thesis is to develop the computational methodologies for investigating the musculoskeletal system performance during human standing and walking, which can lead to a better understanding of the musculoskeletal functions in standing balance and locomotion, and hence to improve clinical diagnosis, treatments and also rehabilitation interventions.
Firstly, an improved Hill-type muscle model was developed to describe the dynamic response of a musculotendon unit subjected to neural excitation by considering more physiological characteristics of skeletal muscles. The dynamic process from neural input through to muscular force generation was represented using two processes: neural excitation dynamics and muscle activation dynamics. The improved muscle model has been used throughout the thesis for musculoskeletal analysis.
An inverted pendulum model driven by a pair of antagonistic muscles was developed to stimulate human standing in the sagittal plane. A set of dynamic simulations has been used to investigate the effect of muscle intrinsic properties on system stability. It is found that the force-velocity relationship of the muscle contractile element has the most significant impact on the dynamic stability of the musculoskeletal system.
Thereafter, a musculoskeletal model with six ankle flexor and extensor muscles, which was combined with a multi-objective optimization scheme had been constructed to investigate the interplay between energy cost and body stability during standing balance. The simulation results suggest that there is a very strong dependence between energy expenditure and body stability during standing postural control, and energy expenditure appears to be a primary consideration in standing balance.
To simulate human walking biomechanics, a three-dimensional musculoskeletal model with 13 body segments and 98 muscle groups was constructed using OpenSim software. The gait measurement database provided by Grant Challenge Competition was used to support the modelling. A set of bone scaling and inverse kinematics procedures was used to refine the model to fit the subject-specific anthropometric and kinematic dataset. The refined model generated reasonable muscle moment arm and net muscle moment data over a complete walking cycle.
Finally, a novel computational framework has been developed to evaluate the mechanical loadings at each individual skeletal muscle group during human walking by integrating a forward dynamics formulation of the muscle contraction dynamics into an inverse dynamics based static optimization scheme. The experimental validation against the measured force sensor data suggested that the approach proposed here provided more accurate estimation of the muscular loadings than the conventional static optimization method and also the OpenSim software.
|Date of Award
|1 Oct 2013
|Jian Dai (Supervisor) & Lei Ren (Supervisor)