Smart homes boost with VibroSense home appliance tracker

Developed by a team at Cornell University, VibroSense can track 17 types of appliances with lasers that capture subtle vibrations in walls, ceilings and floors, and a deep learning network that models the vibrometer's data to create different signatures for each appliance.

Smart speakers detect signs of cardiac arrest

According to Cornell, this could increase efficiency in typical households where people forget to take wet clothes out of washing machines, retrieve hot food from microwaves and turn off dripping taps.

"Recognising home activities can help computers better understand human behaviours and needs, with the hope of developing a better human-machine interface," said Cheng Zhang, assistant professor of information science and senior author of a paper detailing the research in Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies. The paper is due to be presented at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, which will be held virtually between September 12-17.