Perturbation Detection Prediction Fuzzy Modeling for Posturally Perturbed Standing Subjects
Standing blindfolded subject psychophysical movement detection strategies were categorized by analyzing the changes in his/her biomechanical variable (head acceleration) that correlate with the ability to correctly detect small translational perturbations of the movement platform. The time-series head acceleration data provided a measure of postural stability and a clear indication of postural control response that can be directly correlated with the stimulus. Studying the biomechanical and psychophysical responses together enabled to discriminate correct responses from potential guesses. To compare the biomechanical response to psychophysical response it was necessary to find any abnormality in the biomechanical response (head acceleration) that related to the platform movement. For this purpose, a novel method based on Adaptive Neural Fuzzy Inference Systems (ANFIS) is applied to identify the abnormality present in the head acceleration data that related to external perturbation. Consequently, a fuzzy logic base model was designed to take head acceleration time series data and subject?s psychophysical responses as inputs for predicting external perturbation detection and distinguishing potential guesses from right answers.