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Visualisation of Clinical Gait Data Using a Self-Organising Artificial Neural Network
Abstract
The decision-making performance of gait experts varies depending on their background, training and experience. They have to analyse large quantities of complex gait data and this gives rise to an unbalanced use of the available information. These limitations inevitably lead to a biased interpretation. In this study, self-organising artificial neural networks were used to reduce the complexity of joint kinematic and kinetic data which form part of a typical instrumented gait assessment. Three dimensional joint angles, moments and powers during the gait cycle were projected from the multi-dimensional data space onto a topological neural map which thereby identified gait stem-patterns. Patients were positioned on the map in relation to each other and this enabled them to be compared on the basis of their gait patterns. The visualisation of large amounts of complex data in a two-dimensional map labelled with gait patterns is an enabling step towards more objective analysis protocols which will better inform decision making.
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