Muscle force estimation in clinical gait analysis
, PhD thesis, University of Salford.
Neuro-musculoskeletal impairments are a substantial burden on our health care system as a consequence of disease, injury or aging. A better understanding of how such impairments influence the skeletal system through muscle force production is needed. Clinical gait analysis lacks in a sufficient estimation of individual muscle forces. To date, joint moments and EMG measurements are used to deduce on the characteristics of muscle forces, however, known limitations restrain a satisfying analysis of muscle force production. Recent developed musculoskeletal models make it possible to estimate individual muscle forces using experimental kinematic and kinetic data as input, however, are not yet implemented into a clinical gait analysis due to a wide range of different methods and models and a lack of standardised protocols which could be easily applied by clinicians in a routine processing.
This PhD thesis assessed the state of the art of mathematical modelling which enables the estimation of muscle force production during walking. This led into devising a standardised protocol which could be used to incorporate muscle force estimation into routine clinical practice. Especially the input of clinical science knowledge led to an improvement of the protocol. Static optimisation and computed muscle control, two mathematical models to estimate muscle forces, have been found to be the most suitable models for clinical purposes. OpenSim, a free available simulation tool, has been chosen as its musculoskeletal models have been already frequently used and tested. Furthermore, OpenSim provides a straight forward pipeline called SimTrack including both mathematical models. Minor and major adjustments were needed to adapt the standard pipeline for the purposes of a clinical gait analysis to be able to create a standardised protocol for gait analyses.
The developed protocol was tested on ten healthy participants walking at five different walking speeds and captured by a standard motion capture system. Muscle forces were estimated and compared to surface EMG measurements regarding activation and shape as well as their dependence on walking speed. The results showed a general agreement between static optimisation, computed muscle control and the EMG excitations. Compared to the literature, these results show a good consistency between the modelling methods and surface EMG. However, some differences were shown between mathematical models and between models and EMG, especially fast walking speeds.
Additionally, high estimated activation peaks and uncertainties within the estimation process point out that more research needs to be undertaken to understand the mechanisms of mathematical models and the influence of different modelling parameters better (e.g. characteristics of muscle-tendon units, uncertainties of dynamic inconsistency). In conclusion, muscle force estimation with mathematical models is not yet robust enough to be able to include the protocol into a clinical gait analysis routine. It is, however, on a good way, especially slow walking speeds showed reasonable good results. Understanding the limitations and influencing factors of these models, however, may make this possible. Further steps may be the inclusion of patients to see the influence of health conditions.
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