| Paper: | MLSP-P2.11 | ||
| Session: | Bioinformatics and Biomedical Applications | ||
| Time: | Wednesday, May 19, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | 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 | ||
| Authors: | Ravi Vaidyanathan; Case Western Reserve University / Think-A-Move, Ltd. | ||
| Hyunseok Kook; Southern Illinois University | |||
| Lalit Gupta; Southern Illinois University | |||
| James West; Think-A-Move, LLC | |||
| Abstract: | 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 commands signals in response to that movement. Once recognized, said movements can in turn 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 is tested on 4 tongue actions: touching the tongue to the left and right corners of the mouth, and to the top and bottom center of the mouth. Through extensive experiments, it is shown that the pressure signals due to tongue movements are distinct and can be detected with over 97% accuracy. It is thus concluded that the unique strategy will make hands-free control of devices using tongue movements a practical reality. | ||
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