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NEWS
New Device Makes Homes ‘Smart’
September 25, 2020

To boost efficiency in typical households – where people forget to take wet clothes out of washing machines, retrieve hot food from microwaves and turn off dripping faucets – Cornell University researchers have developed a single device that can track 17 types of appliances using vibrations.

According to information, the device, called VibroSense, uses lasers to capture subtle vibrations in walls, ceilings and floors, as well as a deep learning network that models the vibrometer’s data to create different signatures for each appliance – bringing researchers closer to a more efficient and integrated smart home.

“Recognizing home activities can help computers better understand human behaviors and needs, with the hope of developing a better human-machine interface,” said Cheng Zhang, assistant professor of information science and senior author of “VibroSense: Recognizing Home Activities by Deep Learning Subtle Vibrations on an Interior Surface of a House from a Single Point Using Laser Doppler Vibrometry.” The paper was published in Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies and was presented recently at the virtual ACM International Joint Conference on Pervasive and Ubiquitous Computing.

“In order to have a smart home at this point, you’d need each device to be smart, which is not realistic; or you’d need to install separate sensors on each device or in each area,” said Prof. Zhang, who directs Cornell’s SciFi Lab. “Our system is the first that can monitor devices across different floors, in different rooms, using one single device.”

In order to detect usage across an entire house, the researchers’ task was twofold: detect tiny vibrations using a laser Doppler vibrometer; and differentiate similar vibrations created by multiple devices by identifying the paths traveled by the vibrations from room to room.

The deep learning network was trained to distinguish different activities, partly by learning path signatures – the distinctive path vibrations followed through the house – as well as their distinct noises.

The device is primarily useful in single-family houses, Prof. Zhang said, because in buildings it could pick up activities in neighboring apartments, presenting a potential privacy risk.

Among other uses, the system could help homes monitor energy usage and potentially help reduce consumption.


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