Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms.

Abstract excerpt

Wearable devices have been increasingly used in research to provide continuous physical activity monitoring, but how to effectively extract features remains challenging for researchers. To analyze the generated actigraphy data in large-scale population studies, we developed computationally efficient methods to derive sleep and activity features through a Hidden Markov Model-based sleep/wake identification...

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