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In this study, the challenge to maximise the potential of gait analysis by employing advanced methods was addressed by using self-organising neural networks to quantify the deviation of patients’ gait from normal. Data including three-dimensional joint angles, moments and powers of the two lower limbs and the pelvis were used to train Kohonen artificial neural networks to learn an abstract definition...
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...
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