Doctoral thesis resume
Ph.D. thesis proposes a new bioinstrumental system capable to determine
the subject's fatigue state as a result of human-system interaction (e.g.human
- computer interaction).
the classification process the bioinstrumental system used three psychological
signals acquired without any contact with the subject. These signals were:
hand tremor signal, respirator signal and the subject body movements.
The thesis presents: the system of sensors, acquisition techniques, the
preprocessing methods used to artifact elimination, the extracted features
from the psychological signals, the methodology used to reveal the tired
state, the methods used to analyze the fatigue state, the classification
systems, the obtained results, the analyses of these results and, in the
end, the conclusions that can be used to further improve the system's
on an overview of the HCI fields, in the second chapter the static and
the dynamic characteristic of a new class of resonant sensors are extracted
and presented. This sensor was used to record the hand tremor signal and
the respiratory activity. These extracted characteristics prove the transducer
performances and, more, the sensor capability to acquire both physiological
signals (named above) without any quality loses and components distortions.
This mode of behave is very important mainly because even small components
can convey important information that can be use by the pattern recognition
system. In order to extract the sensor's dynamic characteristic a custom
system was build around of the TMS320F240 DSP system. This system is capable
to generate a reference mechanical movement accordingly with the user
requirements. Moreover, in this system it is incorporated a new developed
RNSIC converter (Rectifier With Near-Sinusoidal Current) able to improve
the waveform of the current drawn from the power source.
3rd chapter a new noncontact Virtual Joystick system is presented starting
with the system's main concepts, the development stages of different parts
of the system and the device analysis. The Virtual Joystick uses three
transducers similars with the transducer presented in the previously chapter.
The Virtual Joystick system is presented starting with: the transducers
command and control units, the external interfacing units (the heart of
this unit is the TMS320F240 DSP), the PC software application (it presents
the hand position from the 3D input space and manages the hand tremor
signal acquisition), the first dedicated fuzzy system used to compensate
sensor characteristic and the noise suppression and, finally, the second
fuzzy system used to modeled the hand position and to determine correctly
the hand position. In the end, the acquired tremor signal is analyzed
in order to estimate the system's ability to obtain a "real"
tremor signal. These results outline once again the Virtual Joystick performances
4 is dedicated to the presentation and the analysis of the biological
process known under the name of "tremor". After a short overview
of the general elements and a description of the tremor characteristics
the existing components of the tremor signal are presented (mechanical
components, components generated by the neuro-muscular feedback, central
components, etc.). In the following part of the chapter the methodology
used to evidence the fatigue state is presented. The problems of subjects'
selection and the standard rules in the fields, the protocol and data
acquiring methodologies, methods of data preprocessing, features extraction,
fatigue state analyses, classes analyses and the classificatory selection
are presented, all these in order to correctly differentiate the fatigue
state. Moreover, this chapter gives an idea of a large diversity of information
that is embedded in the tremor signal and highlights the possibility to
recognize the fatigue state using for this only the tremor signal.
chapter 5 there are presented, properly explained, applied and analyzed
several methods of signal processing in order to investigate the results
obtained in the previously chapter. The size of data set is studied and
the assumptions relevance regarding the tired state is also analyzed.
The central nervous influence on the tremor signal is deeply investigated
using several different methods. All three method used (frequency analyses,
coherence analyses and neural network analyzes) converge to the same result:
the physiological tremor signal has a central nervous system origin.
Chapter 6 is dedicated to the presentation of the contribution regarding
the design, construction and testing of a new system capable to acquire
the respiratory activity without any kind of contact using the same sensor
presented and analyzed in the Chapter 2. The respiratory signal acquired
with this system is contaminated with two types of artifacts: with slow
variation and by fast artifacts (these slow and fast variations are compared
with the respiratory behavioral speed). The slow artifacts are eliminated
based on a custom hardware and software implemented adaptive system. This
method shows superior performances compared with a classical filter and
with software implemented adaptive system. In the next section the fast
artifacts are eliminated based on a Blind Source Separation method. This
method proves to be more robust and accurate in artifact removing, in
the frame of nonstationary artifacts compared with neural networks systems.
Using these methods of artifact removing the new proposed system is capable
to obtain a respiratory signal unaffected by two large classes of artifacts.
seven chapters entitled "A noncontact laser system for body language
acquisition and interpretation of subject movements" present a new
innovative system of human computer interaction. After an introduction
the system working mode and implementation are presented. This system
was implemented in two different versions. In the first implementation
the system's core was a PC and in the second embodiment a TMS 320C6414
DSP was used. This system offers for the first time in the field of HCI
systems the possibility to use a new kind of information regarding the
subject's emotional state, unexploited yet on this domain, namely, the
emotional state of the subject expressed through his/her body language.
the last chapter the conclusions and the contribution of this Ph.D. thesis
are highlighted and the future directions of studies (having the obtained
results as a starting point) are presented.