A Contactless Artificial Intelligence System for Smart Devices Can Identify a Sign of Cardiac Arrest

Researchers at the University of Washington created a tool, which could potentially be developed into an application for smart speakers and smartphones, that uses algorithms and machine learning to identify instances of agonal breathing, a sign of cardiac arrest, with an accuracy of 97% at distances of up to 6 meters away. A contactless support vector machine (SVM), an artificial intelligence system that uses algorithms and machine learning, could be used by smart speakers and similar devices to detect agonal breathing, a symptom of potential cardiac arrest. The machine performs with 97% accuracy from a distance of up to 6 meters away, according to a study in Nature Partner Journals Digital Medicine. "A lot of people have smart speakers in their homes, and these devices have amazing capabilities that we can take advantage of," said sudy co-author Shyam Gollakota, PhD, associate professor at the University of Washington's Paul G. Allen School of Computer Science and Engineering, in a statement. "We envision a contactless system that works by continuously and passively monitoring the bedroom for an agonal breathing event, and alerts anyone nearby to come provide CPR. And then if there's no response, the device can automatically call 911."