A New device detects person’s emotions using wireless signals read more at here www.spinonews.com/index.php/item/942-a-new-device-detects-person-s-emotions-using-wireless-signals
As many a relationship book can tell you, understanding someone else’s emotions can be a difficult task. However, what if technology could tell us how someone is really feeling?
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “EQ-Radio,” a device that can detect a person’s emotions using wireless signals.
MIT professor and project lead Dina Katabi, says, “Our work shows that wireless signals can capture information about human behavior that is not always visible to the naked eye.”
Existing emotion-detection methods rely on audiovisual cues or on-body sensors, but there are downsides to both techniques.
EQ-Radio sends wireless signals that reflect off of a person’s body and back to the device. Its beat-extraction algorithms break the reflections into individual heartbeats and analyze the small variations in heartbeat intervals to determine their levels of arousal and positive effect.
These measurements which allow EQ-Radio to detect emotion. The exact correlations vary from person to person, but are consistent enough that EQ-Radio could detect emotions with 70 percent accuracy even when it hadn’t previously measured the target person’s heartbeat.
For the experiments, subjects used videos or music to recall a series of memories that each evoked one the four emotions, as well as a no-emotion baseline. Trained just on those five sets of two-minute videos, EQ-Radio could accurately classify the person’s behavior among the four emotions 87 percent of the time.
One of the CSAIL team’s toughest challenges was to tune out irrelevant data. To do so, the team focused on wireless signals that are based on acceleration rather than distance traveled.
Since the rise and fall of the chest with each breath tend to be much more consistent and, have a lower acceleration than the motion of the heartbeat. Although the focus on emotion-detection meant analyzing the time between heartbeats.
The team says that the algorithm’s ability to captured the heartbeat’s entire waveform means that in the future it could be used for non-invasive health monitoring and diagnostic settings.
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