title: A naïve Gaussian Bayes classifier for detection of mental activity in gait signature.
abstract: 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.
authors: Center for Biomedical Engineering, Indian Institute of Technology, Hauzkhas 110 016, New Delhi, India. joshideepak2004@yahoo.co.in
authors: Deepak
authors: D
authors: Joshi
authors: A
authors: A
authors: Mishra
authors: Sneh
authors: S
authors: Anand
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D001698:Biomedical Engineering; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D005684:Gait / Q000502:physiology*; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008954:Models, Biological; D008960:Models, Psychological; D015233:Models, Statistical; D025341:Principal Component Analysis; D013647:Task Performance and Analysis; D055815:Young Adult