Our lives are awash with ambient electromagnetic radiation, from the fields generated by power lines to the signals used to send data between Wi-Fi transmitters. Researchers at Microsoft and the University of Washington have found a way to harness this radiation for a computer interface that turns any wall in a building into a touch-sensitive surface.
The technology could allow light switches, thermostats, stereos, televisions, and security systems to be controlled from anywhere in the house, and could lead to new interfaces for games.
“There’s all this electromagnetic radiation in the air,” says Desney Tan, senior researcher at Microsoft (and a TR35 honoree in 2007). Radio antennas pick up some of the signals, Tan explains, but people can do this too. “It turns out that the body is a relatively good antenna,” he says.
The ambient electromagnetic radiation emitted by home appliances, mobile phones, computers, and the electrical wiring within walls is usually considered noise. But the researchers chose to put it at the core of their new interface.
When a person touches a wall with electrical wiring behind it, she becomes an antenna that tunes the background radiation, producing a distinct electrical signal, depending on her body position and proximity to and location on the wall. This unique electrical signal can be collected and interpreted by a device in contact with or close to her body. When a person touches a spot on the wall behind her couch, the gesture can be recognized, and it could be used, for example, to turn down the volume on the stereo.
So far, the researchers have demonstrated only that a body can turn electromagnetic noise into a usable signal for a gesture-based interface. A paper outlining this will be presented next week at the CHI Conference on Human Factors in Computing Systems in Vancouver, BC.
In an experiment, test subjects wore a grounding strap on their wrist—a bracelet that is normally used to prevent the buildup of static electricity in the body. A wire from the strap was connected to an analog-to-digital converter, which fed data from the strap to a laptop worn in a backpack. Machine-learning algorithms then processed the data to identify characteristic changes in the electrical signals corresponding to a person’s proximity to a wall, the position of her hand on the wall, and her location within the house.