A new communication and control concept using tongue movements is introduced to generate, detect, and classify signals that can be used in novel hands-free human-machine interface applications such as communicating with a computer and controlling devices. The signals that are caused by tongue movements are the changes in the airflow pressure that occur in the ear canal. The goal is to demonstrate that the ear pressure signals that are acquired using a microphone that is inserted into the ear canal, due to specific tongue movements, are distinct and that the signals can be detected and classified very accurately. The strategy that is developed for demonstrating the concept includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on four tongue movements of eight subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the four pressure signals can be classified with an accuracy of more than 97% averaged across the eight subjects using the decision fusion classification algorithm. Thus, it is concluded that, through the unique concept that is introduced in this paper, human-computer interfaces that use tongue movements can be designed for hands-free communication and control applications.

A complete signal processing strategy is presented to detect and precisely recognize tongue movement by monitoring changes in airflow 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. The strategy developed for the human machine interface architecture includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on 4 tongue movements of 4 subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the 4 pressure signals can be classified with over 96% classification accuracies across the 4 subjects using the decision fusion classification algorithm

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.