A naïve Gaussian Bayes classifier for detection of mental activity in gait signature

A probabilistic modelling is presented to detect mental activity from gait signature recorded from healthy subjects. The proposed scheme is based on principal component analysis with reduced feature dimension followed by a naïve Gaussian Bayes classifier. The leave-one-out cross-validation shows the detection accuracy of 94% with specificity and sensitivity of 96% and 98.3%, respectively. The research has a potential application in the prevention of elderly risk falls, lie detection and rehabilitation among Parkinson's patients.