  As we are about to embark upon the highly hyped "Society 5.0", powered by the
Internet of Things (IoT), traditional ways to monitor human heart signals for
tracking cardio-vascular conditions are challenging, particularly in remote
healthcare settings. On the merits of low power consumption, portability, and
non-intrusiveness, there are no suitable IoT solutions that can provide
information comparable to the conventional Electrocardiography (ECG). In this
paper, we propose an IoT device utilizing a spintronic ultra-sensitive sensor
that measures the magnetic fields produced by cardio-vascular electrical
activity, i.e. Magentocardiography (MCG). After that, we treat the
low-frequency noise generated by the sensors, which is also a challenge for
most other sensors dealing with low-frequency bio-magnetic signals. Instead of
relying on generic signal processing techniques such as averaging or filtering,
we employ deep-learning training on bio-magnetic signals. Using an existing
dataset of ECG records, MCG labels are synthetically constructed. A unique deep
learning structure composed of combined Convolutional Neural Network (CNN) with
Gated Recurrent Unit (GRU) is trained using the labeled data moving through a
striding window, which is able to smartly capture and eliminate the noise
features. Simulation results are reported to evaluate the effectiveness of the
proposed method that demonstrates encouraging performance.
