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Researchers at Washington University in St. Louis have developed a deep-learning model called WearNet, which uses data from wearable fitness monitors to detect depression and anxiety. The model studied 10 variables collected by the Fitbit activity tracker, such as total daily steps and average heart rate, and was able to detect depression and anxiety better than state-of-the-art machine learning models. The researchers hope that their work will lead to the implementation of this technology in hospitals and companies.
