A noncontact vital sign sensor demonstrating a strong correlation with an electrocardiogram electrode and a CO2 sensor
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Май 6, 2025
Abstract
Background:
Accurate
assessment
of
vital
signs
is
important
for
reducingmortality.
The
aim
this
study
was
to
validate
the
effectiveness
and
safety
noncontactvital
sign
sensors.
Methods:
Interference
tests
were
conducted
with
a
noncontact
sensorand
medical
devices.
Inpatients’
heart
respiratory
rates
monitored
via
sensor,
measurements
sensor
compared
those
reference
equipment.
Results:
Noncontactvital
sensors
devices
did
not
interferewith
each
other.
A
total
21
patients
(10
adults
11
children,including
1
baby)
analysed.
For
all
patients,
correlation
coefficients
HR
RR
0.86
0.96,
respectively.
In
adult
0.75
paediatric
0.82
0.94,
No
effects
on
surrounding
or
equipment
observed.
Conclusion:
are
accurate
safe.
Язык: Английский
A noncontact vital sign sensor demonstrating a strong correlation with an electrocardiogram electrode and a CO2 sensor
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Май 19, 2025
Accurate
assessment
of
vital
signs
is
important
for
reducing
mortality.
The
aim
this
study
was
to
validate
the
effectiveness
and
safety
noncontact
sign
sensors.
Interference
tests
were
conducted
with
a
sensor
medical
devices.
Inpatients'
heart
respiratory
rates
monitored
via
sensor,
measurements
compared
those
reference
equipment.
Noncontact
sensors
devices
did
not
interfere
each
other.
A
total
21
patients
(10
adults
11
children,
including
1
baby)
analysed.
For
all
patients,
correlation
coefficients
HR
RR
0.86
0.96,
respectively.
In
adult
0.75
paediatric
0.82
0.94,
No
effects
on
surrounding
or
equipment
observed.
are
accurate
safe.
Язык: Английский
Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning
Sensors,
Год журнала:
2025,
Номер
25(11), С. 3377 - 3377
Опубликована: Май 27, 2025
Millimeter-wave
(mmWave)
radar
is
increasingly
used
in
smart
environments
for
human
detection
due
to
its
rich
sensing
capabilities
and
sensitivity
subtle
movements.
However,
indoor
multipath
propagation
causes
severe
ghost
target
issues,
reducing
reliability.
To
address
this,
we
propose
a
trajectory-based
suppression
method
that
integrates
multi-target
tracking
with
point
cloud
deep
learning.
Our
approach
consists
of
four
key
steps:
(1)
pre-segmentation,
(2)
inter-frame
trajectory
tracking,
(3)
feature
aggregation,
(4)
broadcasting,
effectively
combining
spatiotemporal
information
point-level
features.
Experiments
on
an
dataset
demonstrate
superior
performance
compared
existing
methods,
achieving
93.5%
accuracy
98.2%
AUROC.
Ablation
studies
the
importance
each
component,
particularly
complementary
benefits
pre-segmentation
processing.
Язык: Английский