Deng et al. develop deep learning methods that identify Parkinson’s Disease (PD) patients using public accelerometer and position data with higher accuracy than when using gait/rest and voice-based models. Their study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.
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