Evaluating the Efficiency of Multilayer Perceptron Neural Network Architecture in Classifying Cognitive Impairments Related to Human Bipedal Spatial Navigation

In this study, We evaluated the efficiency of Multilayer perceptron for classification tasks related to cognitive impairments assessed in a virtual reality environment and on spatial data, “The VR Magic carpet” In our earlier work, we applied machine learning (ML) techniques for assessing and categorizing participants with cognitive impairments. The issue stems from the likelihood of not identifying the most relevant elements that will provide high accuracy in this navigation disorder detection. We used method multilayer perceptron (MLP) architectures to benefit from using layers for feature extraction on velocity time series and solve our classification problem. This navigation disorder identification model was prompt to develop a better understanding of targeting users with navigation disorders. The experimental results of the model in this study provide an enhancement because it can distinguish with more accuracy between healthy individuals and patients.