VibroSense tracks home appliance usage via deep learning and lasers

Advances in technology have made many household appliances more energy efficient, and even given outdated old ones some energy-saving smarts, but addressing the power usage of each individual device across the home is still a tall order. Researchers at Cornell University have been working on more of a one-size-fits-all solution, developing a vibration-sensing device that can keep tabs on appliance usage through machine learning and lasers. The team points to smart homes of the future as its inspiration for developing the VibroSense device, imagining scenarios where the house itself knows when a washing machine has completed its cycle, when a microwave has finished heating food or a faucet is dripping. While replacing each appliance with smart versions or attaching specific sensors to them could be one way to tackle this, the Cornell team sees a more efficient way forward. "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," says Cheng Zhang, assistant professor of information science and senior author of the study.