A Novel Probabilistic Network Model for Estimating Cognitive-Gait Connection Using Multimodal Interface

Abstract

Research in human gait analysis has captivated several computer vision researchers to solve human identification problems. The proposed work provides a novel approach for cognitive state estimation via multi-modal analysis. The advantage of multi-modal system is to provide adequate motion signatures with ensemble of multimodal gait data to ensure data reliability for classification. The relationship between human cognitive states and gait is predicted using both temporal and non-temporal probabilistic models. We estimate prior probability tables for non-temporal probabilistic model known as Simple Bayesian Model after analysing the data acquired from Inertial Measurement Unit (IMU), Electroencephalography (EEG) and multiple Kinect V2.0 sensors. A novel Dynamic Bayesian Network (DBN) is used as probabilistic temporal model for estimating most probable sequence of transitions among human cognitive states. We apply Gaussian Mixture Modelling with Expectation maximisation (GMM-EM) to tune transition and emission probabilities for maximizing the probability of the observed sequence. A promising estimation accuracy of 88.6% is obtained. Also, Principal Component Analysis (PCA) and k-nearest neighbour (kNN) algorithms are applied separately to calculate the input probabilities for DBN model. It is observed that both GMM-EM and k-NN based approaches outperform all the state-of-the-art techniques. Moreover, standard statistical tests are performed on acquired dataset to validate the experimental results.