Machine learning technique helps wearable devices get better at diagnosing sleep disorders and quality

Getting diagnosed with a sleep disorder or assessing quality of sleep is an often expensive and tricky proposition, involving sleep clinics where patients are hooked up to sensors and wires for monitoring. Wearable devices, such as the Fitbit and Apple Watch, offer less intrusive and more cost-effective sleeping monitoring, but the tradeoff can be inaccurate or imprecise sleep data. Researchers at the Georgia Institute of Technology are working to combine the accuracy of sleep clinics with the convenience of wearable computing by developing machine learning models, or smart algorithms, that provide better sleep measurement data as well as considerably faster, more energy-efficient software. The team is focusing on electrical ambient noise that is emitted by devices but that is often not audible and can interfere with sleep sensors on a wearable gadget. Leave the TV on at night, and the electrical signal – not the infomercial in the background – might mess with your sleep tracker.