INTRODUCTION
The goal of much of the research regarding
the olfactory system is to understand how individual odors are identified. Many
researchers have produced mathematical models of the olfactory system. These
models often include simulations of the neurobiological information processing
systems. It is interesting to consider that the mammalian epithelium contains
from approximately 1 million sensory neurons in the mouse, to 10 million
sensory neurons in the human, to 100 million sensory neurons in the pig.
Electronic noses are much simpler than
almost all biological olfactory systems and detect only a small range of odors.
However, for many potential tele-smell applications in the near future, a
predetermined and limited set of odors is likely. Thus, it is likely an
electronic nose will be a key component in an olfactory input to a telepresent
virtual reality system.
Electronic/Artificial Noses
Electronic/artificial noses are being
developed as systems for the automated detection and classification of odors,
vapors, and gases. The two main components of an electronic nose are the
sensing system and the automated pattern recognition system. The sensing system
can be an array of several different sensing elements (e.g., chemical sensors),
where each element measures a different property of the sensed odor, or it can
be a single sensing device (e.g., spectrometer) that produces an array of
measurements for each odor, or it can be a combination. Each odor presented to
the sensor array produces a signature or pattern characteristic of the odor. By
presenting many different odors to the sensor array, a database of signatures
is built up. This database of labeled odor signatures is used to train the
pattern recognition system. The goal of this training process is to configure
the recognition system to produce unique mappings of each odor so that an
automated identification can be implemented.
The quantity and complexity of the data
collected by sensor arrays can make conventional chemical analysis of data in
an automated fashion difficult. [One approach to odor identification is to
build an array of sensors, where each sensor in the array is designed to
respond to a specific odor. With this approach, the number of unique sensors
must be at least as great as the number of odors being monitored. It is both
expensive and difficult to build highly selective chemical sensors.
Artificial neural networks
(ANNs), which have been used to analyze complex data and for pattern
recognition, are showing promising results in chemical vapor recognition. When
an ANN is combined with a sensor array, the number of detectable odors is
generally greater than the number of sensors [5]. Also, less selective sensors
which are generally less expensive can be used with this approach. Once the ANN
is trained for odor recognition, operation consists of propagating the sensor
data through the network. Since this is simply a series of vector-matrix
multiplications, unknown odors can be rapidly identified in the field.
Electronic noses that incorporate ANNs have been demonstrated in the following
applications: monitoring food and beverage odors, controlling the cooking of
food, automated flavor control, analyzing fuel mixtures, discriminating the
smoke from different brands of cigarettes, detecting oil leaks, identifying
different types of alcohol, identifying odors from household chemicals, and
quantifying individual components in gas mixtures. Several ANN configurations
have been used in electronic noses including back propagation-trained,
feed-forward networks; Kohonen's self-organizing maps (SOMs); Learning Vector
Quantizers (LVQs); Hamming networks; Boltzmann machines; and Hopfield networks.
Due
to the limitations of current technology, many ANN based electronic noses have
less than 20 sensing elements and less than 100 neurons. These systems are
designed for specific applications with a limited range of odors. Systems that
mimic more of the functionality of the human olfactory system will require a
much larger set of sensing elements and a larger ANN.
ARTIFICIAL
NEURAL NETWORKS
An Artificial Neural Network (ANN) is an information processing
paradigm that was inspired by the way biological nervous systems, such as the
brain, process information. The key element of this paradigm is the novel
structure of the information processing system. It is composed of a large
number of highly interconnected processing elements (neurons) working in unison
to solve specific problems. ANNs, like people, learn by example. An ANN is
configured for a specific application, such as pattern recognition or data
classification, through a learning process. Learning in biological systems
involves adjustments to the synaptic connections that exist between the
neurons. This is true of ANNs as well.
Another advantage of the
parallel processing nature of the ANN is the speed performance. During
development, ANNs are configured in a training mode. This involves a repetitive
process of presenting data from known diagnoses to the training algorithm. This
training mode often takes many hours. The payback occurs in the field where the
actual odor identification is accomplished by propagating the data through the
system which takes only a fraction of a second. Since the identification time
is similar to the response times of many sensor arrays, this approach permits
real-time odor identification.
ELECTRONIC NOSE
The prototype electronic nose, shown in Figure 1, identifies
odors from several common household chemicals. It employs an array of nine
tin-oxide gas sensors, a humidity sensor, and two temperature sensors to
examine the environment. Although each sensor is designed for a specific
chemical, each responds to a wide variety of chemical vapors. Collectively,
these sensors respond with unique signatures (patterns) to different chemicals.
During the training process, various chemicals with known mixtures are
presented to the system. In the initial studies, the back propagation algorithm
was used to train the ANN to provide the correct analysis of the presented
chemicals.
The nine tin-oxide sensors are commercially available
Taguchi-type gas sensors obtained from Figaro Co. Ltd. (Sensor 1, TGS 109;
Sensors 2 and 3, TGS 822; Sensor 4, TGS 813; Sensor 5, TGS 821; Sensor 6, TGS
824; Sensor 7, TGS 825; Sensor 8, TGS 842; and Sensor 9, TGS 880). Exposure of
a tin-oxide sensor to a vapor produces a large change in its electrical
resistance [15]. The humidity sensor (Sensor 10: NH-02) and the temperature
sensors (Sensors 11 and 12: 5KD-5) are used to monitor the conditions of the
experiment and are also fed into the ANN.
The prototyped ANN was constructed as a multilayer feed forward
network and was trained with the back propagation of error algorithm by using a
training set from the sensor database [16]. This prototype was initially trained
to identify odors from eight household chemicals: acetone, correction fluid,
contact cement, glass cleaner, isoproponal alcohol, lighter fluid, rubber
cement, and vinegar. Another category, "none," was used denote the
absence of all chemicals except those normally found in the air. This resulted
in nine output categories from the ANN.
During operation, the sensor array
"smells" an odor, the sensor signals are digitized and fed into a
computer, and the ANN (implemented in software) then identifies the chemical.
This identification time is limited only by the response of the chemical
sensors, but the complete process can be completed within seconds. Figure 3
illustrates both the sensor response and the ANN classification of the system
for a variety of test chemicals presented to the prototype. The identified
odors can then be transmitted to an odor regeneration system.
TELE- SMELL DEMONSTRATION
It is composed of an odor identification system
(e.g., electronic nose), transmission channel, and an odor regeneration system.
To date, we have only demonstrated the odor identification part. However, we
are currently preparing for a demonstration of tele-smell. The goal of this
demonstration is to show the concept of tele-smell and olfactory input in a
virtual reality environment. In this demonstration, the electronic nose
described in the previous section will be placed in a physical mock-up of a
house with various household chemicals distributed throughout the house (e.g.,
vinegar, cleaners, etc.). The odor generation system will be placed in a virtual/artificial reality mock-up of a
house. The electronic nose will be moved around the physical house mock-up in a
walk-around demonstration. The identified odors will be transmitted to the odor
generation system located in the virtual house. This system will then
regenerate the detected odor in the virtual environment.
APPLICATIONS
1.
Used in tele-medicine
2.
Can be used in video games.
3.
We will be able to
smell and taste products before buying
them from a website.
4.
Can be used to send a
perfumed e-mail.
5.
This technique can be used
to detect smells in dangerous environments.
CONCLUSION
The electronic nose described in
this paper is adequate for demonstrating the concept of tele-smell and
olfactory input in a virtual reality environment. This electronic nose has
worked well in identifying several common household chemicals.
The
ultimate goal of our research will be to demonstrate tele-smell with odors of
importance in tele-present surgery and tele-present battlefield surgery. This
will involve the construction of an automated odor sensing system (electronic
nose) that can identify odors generated by the human body (e.g., bile, urine,
blood, etc.), integration of the electronic nose with the odor generation
system, and demonstration of tele-smell in tele-surgery. This will require a
sensing system more sophisticated than the common household chemical odor
system described in this paper. Possible sensing technologies for this
application include mass spectrometry. However, inclusion of mass spectrometers
into portable systems will require a large effort in miniaturizing the mass spectrometer
and its associated hardware.
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