Imposing neural networks and PSO optimization in the quest for optimal ankle-foot orthosis dynamic modelling

Abstract

Individuals with abnormal walking patterns due to various conditions face significant challenges in daily activities, especially walking. Ankle-foot orthosis (AFO) devices are crucial in providing essential support to their lower limbs. Accurately modeling the dynamic behavior of AFO systems, particularly in predicting ground reaction forces, is a complex yet vital task to ensure their effectiveness. This research develops dynamic models for AFO systems using advanced modeling techniques, employing both parametric and non-parametric approaches. Parametric methods, such as particle swarm optimization (PSO), and non-parametric methods, like multi-layer perceptron (MLP) neural networks, are utilized through system identification methods. According to the findings, the MLP neural network continuously generates objective results and performs exceptionally well in correctly detecting the AFO system, attaining a noticeably lower mean squared prediction error of 0.000011. This research highlights the potential of advanced modeling techniques, particularly MLP neural networks, in enhancing AFO system modeling accuracy. Although parametric techniques like PSO are useful, the MLP approach performs better, offering insightful information about modelling AFO systems and indicating that non-parametric techniques like MLP neural networks have potential to further AFO creation and control.