  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 was developed. The pipelines
were deployed on ESP32 edge device and benchmarked against signal processing to
determine the energy usage, and inference times. The results indicate that the
proposed ML and hybrid pipeline reduces energy and time per inference by 82%
and 28% compared to traditional algorithms. The main trade-off for ML pipeline
was accuracy, with a mean absolute error (MAE) of 3.28, compared to 2.39 and
1.17 for the hybrid and signal processing pipelines. The ML models thus show
promise for deployment in energy and computationally constrained devices.
Further, the lower sampling rate and computational requirements for the ML
pipeline could enable custom hardware solutions to reduce the cost and energy
needs of wearable devices.
