Reshaping the healthcare world by AI-integrated wearable sensors following COVID-19
Chemical Engineering Journal,
Год журнала:
2025,
Номер
505, С. 159478 - 159478
Опубликована: Янв. 11, 2025
Язык: Английский
COVID-19 Early Detection in Doctors and Healthcare Workers (CEDiD) study: a cohort study on the feasibility of wearable devices
BMJ Open,
Год журнала:
2025,
Номер
15(4), С. e089598 - e089598
Опубликована: Апрель 1, 2025
Background
Infectious
agents
such
as
SARS-CoV-2
require
strategies
to
contain
outbreaks,
particularly
in
hospitals
where
the
spread
of
infection
is
most
likely.
Biometric
monitoring
heart
rate,
temperature,
oxygen
saturations
and
sleep
might
provide
important
early
warning
signs
for
SARS-CoV-2.
This
study
aimed
determine
whether
a
smart
medical
device
(E4
wristband)
pulse
oximeter
used
continuously
measure
skin
temperature
saturation
would
predict
onset
infection.
Methods
A
single-centre,
prospective
observational
cohort
30
healthcare
workers
(HCWs)
working
areas
at
high
risk
exposure
were
enrolled.
HCWs
tested
using
RT-qPCR
daily
self-administered
swabs
days.
Each
participant
was
asked
wear
an
E4
wristband
changes
their
throughout
study.
Results
Nine
(30%)
(median
(range)
age
39
(27–57)
years)
positive
COVID-19.
No
significant
differences
found
pre-infection
post-infection
variations
rate
(p=0.31)
or
(p=0.44).
Seven
nine
subjects
reported
symptoms
some
point
during
period:
unusual
fatigue
(40%),
headache
(33%)
runny
nose
(22%)
frequent.
Analysis
trends
observations
demonstrated
fluctuations
biometric
parameters.
Conclusion
These
results
suggest
that
wearable
technology
be
useful
documenting
exposed
HCWs.
Trial
registration
number
NCT04363489
.
Язык: Английский
Artificial Intelligence and Machine Learning in Healthcare
Advances in bioinformatics and biomedical engineering book series,
Год журнала:
2024,
Номер
unknown, С. 12 - 46
Опубликована: Март 4, 2024
Artificial
intelligence
(AI)
systems
are
designed
by
humans
that,
given
a
complex
goal,
act
in
the
physical
or
digital
dimension
perceiving
their
environment
through
data
acquisition,
interpreting
collected
structured
unstructured
data,
reasoning
on
knowledge,
processing
information,
derived
from
this
and
deciding
best
action(s)
to
take
achieve
goal.
It
is
precisely
AI's
ability
carry
out
speedy
analysis
of
datasets
that
one
its
key
strengths.
The
recent
renaissance
AI
largely
has
been
driven
successful
application
deep
learning
—
which
involves
training
an
artificial
neural
network
with
many
layers
(that
is,
‘deep'
network)
huge
datasets.
rise
dissemination
clinical
medicine
will
refine
our
diagnostic
accuracy
rule-out
capabilities.
In
Book
Chapter,
we
focus
applications
could
augment
change
practice,
identify
impact
arising
development
suggest
future
research
directions.
Язык: Английский
Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices
Sensors,
Год журнала:
2024,
Номер
24(16), С. 5219 - 5219
Опубликована: Авг. 12, 2024
Battery
power
is
crucial
for
wearable
devices
as
it
ensures
continuous
operation,
which
critical
real-time
health
monitoring
and
emergency
alerts.
One
solution
long-lasting
energy
harvesting
systems.
Ensuring
a
consistent
supply
from
variable
sources
reliable
device
performance
major
challenge.
Additionally,
integrating
components
without
compromising
the
wearability,
comfort,
esthetic
design
of
healthcare
presents
significant
bottleneck.
Here,
we
show
that
with
meticulous
using
small
highly
efficient
photovoltaic
(PV)
panels,
compact
thermoelectric
(TEG)
modules,
two
ultra-low-power
BQ25504
DC-DC
boost
converters,
battery
life
can
increase
9.31
h
to
over
18
h.
The
parallel
connection
converters
at
points
output
allows
both
individually
achieve
maximum
point
tracking
(MPPT)
during
charging.
We
found
under
specific
conditions
such
facing
sun
more
than
hours,
became
self-powered.
Our
results
demonstrate
long-term
stable
sensor
node
an
efficiency
96%.
Given
high-power
density
solar
cells
outdoors,
combination
PV
TEG
harvest
quickly
sufficiently
sunlight
body
heat.
form
factor
system
environmental
particular
occupations
oil
gas
industry
make
suitable
wearables
worn
on
head,
face,
or
wrist
region,
targeting
outdoor
workers.
Язык: Английский
Advanced Diagnostics With Artificial Intelligence and Machine Learning in the Healthcare Sector
Advances in medical diagnosis, treatment, and care (AMDTC) book series,
Год журнала:
2024,
Номер
unknown, С. 47 - 81
Опубликована: Янв. 5, 2024
Artificial
intelligence
(AI)
systems
are
software
(and
possibly
also
hardware)
designed
by
humans
that,
given
a
complex
goal,
act
in
the
physical
or
digital
dimension
perceiving
their
environment
through
data
acquisition,
interpreting
collected
structured
unstructured
data,
reasoning
on
knowledge,
processing
information,
derived
from
this
and
deciding
best
action(s)
to
take
achieve
goal.
for
health
includes
machine
learning
(ML),
natural
language
(NLP),
speech
recognition
(text-to-speech
speech-to-text),
image
vision,
expert
(a
computer
system
that
emulates
decision-making
ability
of
human
expert),
robotics,
planning,
scheduling,
optimization.
ML
is
core
component
AI
allows
automatically
learn
improve
without
being
explicitly
programmed.
Computer
programs
access
use
it
with
aim
intervention
assistance
adjust
actions
accordingly.
Язык: Английский