Parametric and non-parametric signal analysis for mapping air flow in the ear-canal to tongue movements: a new strategy for hands-free human-machine interfaces

A complete signal processing strategy is presented to detect and recognize tongue movement precisely by monitoring changes in air flow that occur in the ear canal. Tongue movements within the human oral cavity create unique, subtle pressure signals in the ear that can be processed to produce command signals in response to that movement. Once recognized, the movements can be used in human-machine interface applications, such as communicating with a computer and controlling mechanical devices. The processing strategy includes pressure signal acquisition using a microphone inserted into the ear-canal, PSD analysis to design bandpass filters to reject pressure changes due to sources other than tongue movements, start- and end-point detection in the waveforms through cross-correlation, signal estimation, and the design and evaluation of parametric and non-parametric signal classifiers. The non-parametric signal classifiers include non-linear alignment classifiers and matched filters, while the parametric classification involves a multivariate Gaussian classifier using AR model parameters. The complete strategy was tested on 4 tongue actions touching areas of the mouth: left corner; right corner; top center; bottom center. Experiments show that the pressure signals due to tongue movements are distinct and can be detected with over 97% accuracy. The unique strategy makes hands-free control of devices using tongue movements a practical reality.