5 research outputs found
Heterogeneous recognition of bioacoustic signals for human-machine interfaces
Human-machine interfaces (HMI) provide a communication pathway between
man and machine. Not only do they augment existing pathways, they can substitute
or even bypass these pathways where functional motor loss prevents the
use of standard interfaces. This is especially important for individuals who rely
on assistive technology in their everyday life. By utilising bioacoustic activity,
it can lead to an assistive HMI concept which is unobtrusive, minimally disruptive
and cosmetically appealing to the user. However, due to the complexity of
the signals it remains relatively underexplored in the HMI field.
This thesis investigates extracting and decoding volition from bioacoustic activity
with the aim of generating real-time commands. The developed framework
is a systemisation of various processing blocks enabling the mapping of continuous
signals into M discrete classes. Class independent extraction efficiently
detects and segments the continuous signals while class-specific extraction exemplifies
each pattern set using a novel template creation process stable to
permutations of the data set. These templates are utilised by a generalised
single channel discrimination model, whereby each signal is template aligned
prior to classification. The real-time decoding subsystem uses a multichannel
heterogeneous ensemble architecture which fuses the output from a diverse set
of these individual discrimination models. This enhances the classification performance
by elevating both the sensitivity and specificity, with the increased
specificity due to a natural rejection capacity based on a non-parametric majority
vote. Such a strategy is useful when analysing signals which have diverse
characteristics, false positives are prevalent and have strong consequences, and
when there is limited training data available. The framework has been developed
with generality in mind with wide applicability to a broad spectrum of
biosignals.
The processing system has been demonstrated on real-time decoding of tongue-movement
ear pressure signals using both single and dual channel setups. This
has included in-depth evaluation of these methods in both offline and online
scenarios. During online evaluation, a stimulus based test methodology was
devised, while representative interference was used to contaminate the decoding
process in a relevant and real fashion. The results of this research
provide a strong case for the utility of such techniques in real world applications
of human-machine communication using impulsive bioacoustic signals
and biosignals in general