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.
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.