Binarized Neural Networks (BNNs) have the potential to revolutionize the way that deep learning is carried out in edge computing platforms.
Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring.
We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.
Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e. g., up to a few hours).
In this paper, we propose a novel and robust approach for respiration tracking which compensates for the negative effects of variations in the ambient temperature and motion artifacts and can accurately extract breathing rates in highly dynamic thermal scenes.