Although undetectable by our limited five senses, most electrical devices emit small amounts of unique electromagnetic noise, which is actually transferred through our bodies.
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“[Most electronic devices] have EM signatures, but for passive objects like steel ladders – which are conductive – they pick up EM signals from their immediate environment, like fluorescent lights or power lines”, said Laput.
New technology from Carnegie Mellon University and Disney Research can identify objects based on the electromagnetic (EM) signals that they emit.
The team embedded their sensor inside a traditional smartwatch as a proof-of-concept called EM-Sense. Imagine your wearable automatically starting a timer when you start brushing your teeth, or reading your emails and to-do list when you open the door to your office in the morning.
The electromagnetic noise given off by electromechanical objects is distinctive and can be used to classify them.
Examples of the EM-Sense’s detection capabilities are what really bring the technology to life. When a user makes physical contact with such an object, this EM signal propagates through the user, owing to the conductivity of the human body. The user can then edit the robot’s structure, adding or removing motors, or adjusting their position and orientation.
According to Disney the watch works on anything electrical or electromechanical or even large objects like doors and furniture.
For more information and a video, please visit the project website at http://www.disneyresearch.com/publication/emsense/.
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This touch-recognition technology from Disney, Carnegie Mellon and other researchers has countless possibilities. More than 13,000 students in the university’s seven schools and colleges benefit from a small student-to- faculty ratio and an education characterized by its focus on creating and implementing solutions for real problems, interdisciplinary collaboration and innovation. Research topics include computer graphics, animation, video processing, computer vision, robotics, wireless & mobile computing, human-computer interaction, displays, behavioral economics, and machine learning.
