A way of monitoring household appliances by using machine learning to analyse vibrations on a wall or ceiling has been developed by researchers in the US. Their system could be used to create centralized smart home systems without the need for individual sensors in each object. What is more, the technology could help track energy use, identify electrical faults and even remind people to empty the dishwasher.
“Recognizing home activities can help computers better understand human behaviours and needs, with the hope of developing a better human-machine interface,” says team member and information scientist Cheng Zhang of Cornell University.
The system, dubbed VibroSense, comprises two core parts: a laser Doppler vibrometer and a deep learning model, which is a type of machine learning system.
Wall or ceiling
The vibrometer detects tiny vibrations by measuring the distortion of a laser beam reflected from a surface. For single-story houses, VibroSense’s laser is targeted at a central interior wall within the home, whereas in two-story residences, the researchers aim it at a ceiling. The researchers’ prototype device is cable of detecting vibration velocities of up to 1.25 m/s with a resolution of around 0.2 µm/s.
The deep learning model learns how to