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Deep Neural Network Architecture Search for Wearable Heart Rate Estimations
Daniel Ray, Tim Collins, Prasad Ponnapalli
1106 - 1107
10.3233/SHTI210366
Research Article
Studies in Health Technology and Informatics
Volume 281: Public Health and Informatics
Abstract

Extracting accurate heart rate estimations from wrist-worn photoplethysmography (PPG) devices is challenging due to the signal containing artifacts from several sources. Deep Learning approaches have shown very promising results outperforming classical methods with improvements of 21% and 31% on two state-of-the-art datasets. This paper provides an analysis of several data-driven methods for creating deep neural network architectures with hopes of further improvements.

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