Cornell researchers have invented an earphone that can continuously track full facial expressions by observing the contour of the cheeks – and can then translate expressions into emojis or silent speech commands.

With the ear-mounted device, called C-Face, users could express emotions to online collaborators without holding cameras in front of their faces – an especially useful communication tool as much of the world engages in remote work or learning.

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 researchers have developed a single device that can track 17 types of appliances using vibrations.

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.