NASHVILLE,
Tenn. — Robotics designers are working with psychologists here at
Vanderbilt University to improve human-machine interfaces by
teaching robots to sense human emotions. Such "sensitive" robots
would change the way they interact with humans based on an
evaluation of a person's mood.
"We believe that many of our human-to-human communications are
implicit — that is, the more familiar we are with a person, the
better we are at understanding them. We want to determine whether a
robot can sense a person's mood and change the way it interacts
[with the human] for more natural communications," said Vanderbilt
assistant professor Nilanjan Sarkar.
"We don't want to give a robot emotions; we just want them to be
sensitive to our emotions," added Craig Smith, Vanderbilt
associate professor of psychology and human development.
Sarkar, an engineer, initiated the research project with Smith, a
psychologist, with the insight that there is no universal method of
detecting emotions in humans. This impressed Smith, who had
independently noticed that years of research in psychology had
failed to uncover the Rosetta stone of human emotions. The bottom
line for both researchers was that people express the same emotions
in different ways; thus, any "universal" method for detecting
emotions with robots would be doomed.
"Psychologists have been trying to identify universal patterns of
physiological response since the early 1900s, but without success.
We believe that the lesson to be learned there is that there are no
such universal patterns," said Smith.
Consequently, the team's research project has two parts: sensing
the unique patterns of behavior that mark an individual person's
emotions, and converting that information in real-time into
actuator-style commands to the robot to facilitate communications
between humans and machines.
"We have established the feasibility of the individual-specific
approach that we are taking, and there is a good chance that we can
succeed," said Smith.
Emotional data
The approach taken by the researchers was adopted from voice- and
handwriting-recognition technologies: Information on baseline
features is compiled for each person, and then the features that
indicate each mental state are identified for that person. Armed
with their personalized emotion-recognition system, the researchers
hope to use diverse data steams from users to create a more
intuitive interface.
In their prototype studies, sensors are worn by the person being
monitored by the robot. For example, heart rate monitors would gauge
the user's anxiety level, and the robotic responses would be
adjusted accordingly. With the sensors in place on the subject, the
researchers observe data streams for the subject in various
situations, such as while the subject is playing a videogame.
By subjecting each person to the same anxiety-producing
situations in the game, the researchers obtained electrocardiogram
profiles for specific mental states.
One such experiment gathered information from the same user's
sensors over a six-month period in order to validate the feasibility
of the "personalized" approach.
So far, Sarkar's team has performed preliminary analysis of the
profiles using conventional signal-processing algorithms and
experimental methods like fuzzy logic and wavelet analysis. They
have found patterns in the variations in the interval between
heartbeats that could be "personalized."
Specifically, two frequency bands vary predictably with changes
in stress. Sarkar's team is now conducting similar analyses using
other available biosensors, including skin conductance (which
changes when people sweat under stress) and facial muscles (such as
furrowing the brow or clenching the jaw).
The team is also expanding the programming of its small robot to
allow the robot to make better use of this information when
communicating with people.
'I sense you are anxious'
In a current experiment the small robot explores its environment
with a St. Bernard rescue hound-style human-machine interface. When
the robot "finds" a person, it examines the subject's data streams
to determine that person's mental state, then responds accordingly.
For instance, when finding an "anxious" person, the robot says: "I
sense that you are anxious. Is there anything I can do to help?"
In the future, the research team wants to be able to discriminate
between "bad" anxiety and "good" excitement, since both produce
similar physiological profiles. They also plan to map out other
psychological states, such as boredom and frustration.
For the latter, Smith has already devised an anagram-based system
that can frustrate test subjects by systematically increasing in
difficulty. The team is also analyzing different data streams, such
as electroencephalogram brain wave monitors and more subtle measures
of cardiovascular activity.
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