Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices. (arXiv:2208.07981v1 [cs.LG])

The focus of this paper is a proof of concept, machine learning (ML) pipeline
that extracts heart rate from pressure sensor data acquired on low-power edge
devices. The ML pipeline consists an upsampler neural network, a signal quality
classifier, and a 1D-convolutional neural network optimized for efficient and
accurate heart rate estimation. The models were designed so the pipeline was
less than 40 kB. Further, a hybrid pipeline consisting of the upsampler and
classifier, followed by a peak detection algorithm …

arxiv devices edge edge devices hr learning lg machine machine learning pipeline rate