Discriminative time-frequency kernels for gait analysis for amyotrophic lateral sclerosis

Many stochastic systems show certain trends which in turn govern their underlying non-stationary time varying behavior. In order to facilitate efficient quantification of such signals, their analysis necessitates the use of robust tools for discerning between different classes of data. Research show that, use of time-frequency techniques offer intelligible representations for non-stationary signals, along with facilitating computation of instantaneous parameters. Further, in order to obtain efficient discrimination machine learning (ML) modules are often used alongside suitable representation techniques. In this work, we exploit the concepts of ML-kernel functions directly by incorporating them in the ambiguity time-frequency (TF) space, thereby obtaining a one-step discrimination between different non-stationary patterns. The proposed technique is evaluated for quantification applications for gait signal analysis. An overall classification accuracy of 93.1% is reported for the neurological gait database consisting of signals from 16-control and 13-amyotrophic lateral sclerosis (ALS) subjects. Results indicate that this scheme offers great potential in designing robust tools for time-varying signal analysis.