IEEE Solid-States Circuits Magazine - Fall 2020 - 46

challenges, such as individual and
regular calibrations. Every human
body is unique; therefore, each
wearable may require individual
adjustments. In addition to this, the
drift in the sensing material through
time requires regular correction.
Long-term signal acquisition is also
susceptible to signal instability and
drift. Such problems may have a significant impact on the accuracy of a
device. Having on-sensor deep learning algorithms can enable a trained
data set that will work as a feedback
system for the robust operation
and accuracy of devices, which will
reduce false negatives and positives.
Integrated deep learning algorithms
on devices may correct human-tohuman variations and drifts in readings due to the device components
and environment, and they may
detect important features in respiratory signals to assist physicians
with providing the highest quality analysis.

Conclusions
Next-generation blood gas monitors
are essential for the future of medical care. These new tools will be vital
for treating any diseases and conditions that O2 affects or in which O2 is
affected, including new respiratory
diseases, such as COVID-19, and longstanding health-care problems, such
as sleep apnea and COPD. Medical
devices and treatment plans that can
be personalized will improve quality of care for a significant number
of patients. This kind of care is not
possible without remote and continuous monitoring of blood gas content,
especially for respiratory diseases.
In this article, we covered emerging blood gas monitoring devices
and techniques to create the sensors
and IC structures to transform bulky
benchtop instruments to miniaturized, next-generation biomedical
devices. We also discussed some of
the challenges that engineers face
in developing medical wearables/
implantables and solutions to enable
these revolutionary devices to become
the new standard in medical care.

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IEEE Solid-States Circuits Magazine - Fall 2020

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