Vaidyanathan, R., Kotta, S., Gupta, L. and West, J.
A decision fusion pattern classification architecture for human-robotic interface
In Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006.
Institute of Electrical and Electronics Engineers., .
Full text not available from this repository.
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.
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