Fitness monitoring devices like Fit-bit or even Apple watch claim to be able to measure the number of hours that you sleep. Some apps even claim to be able record the amount of time you spent in REM sleep or tossing and turning. These devices are limited to what the accelerometers and motion detectors produce as a recognizable electronic signature and their interpretations of them.
Most of these devices fall short of being able tell the difference between walking and riding a grass mower. This is a good way to get steps in if that is all you care about. Maybe artificial intelligence can offer us a better analysis of exercise and sleep patterns?
More than 50 million Americans suffer from sleep disorders, and diseases including Parkinson’s and Alzheimer’s can also disrupt sleep. Diagnosing and monitoring these conditions usually requires attaching electrodes and a variety of other sensors to patients, which can further disrupt their sleep.
To make it easier to diagnose and study sleep problems, researchers at MIT and Massachusetts General Hospital have devised a new way to monitor sleep stages without sensors attached to the body. Their device uses an advanced artificial intelligence algorithm to analyze the radio signals around the person and translate those measurements into sleep stages: light, deep, or rapid eye movement (REM).
“Imagine if your Wi-Fi router knows when you are dreaming, and can monitor whether you are having enough deep sleep, which is necessary for memory consolidation,” says Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, who led the study.
Our vision is developing health sensors that will disappear into the background and capture physiological signals and important health metrics, without asking the user to change her behavior in any way.
Katabi worked on the study with Matt Bianchi, chief of the Division of Sleep Medicine at MGH, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science and a member of the Institute for Data, Systems, and Society at MIT. Mingmin Zhao, an MIT graduate student, is the paper’s first author, and Shichao Yue, another MIT graduate student, is also a co-author.