A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques
Measurement,
Год журнала:
2024,
Номер
226, С. 114186 - 114186
Опубликована: Янв. 20, 2024
Язык: Английский
Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability
Sustainability,
Год журнала:
2023,
Номер
15(22), С. 15695 - 15695
Опубликована: Ноя. 7, 2023
In
the
realm
of
sustainable
IoT
and
AI
applications
for
well-being
elderly
individuals
living
alone
in
their
homes,
falls
can
have
severe
consequences.
These
consequences
include
post-fall
complications
extended
periods
immobility
on
floor.
Researchers
been
exploring
various
techniques
fall
detection
over
past
decade,
this
study
introduces
an
innovative
Elder
Fall
Detection
system
that
harnesses
technologies.
our
configuration,
we
integrate
RFID
tags
into
smart
carpets
along
with
readers
to
identify
among
population.
To
simulate
events,
conducted
experiments
13
participants.
these
experiments,
embedded
transmit
signals
readers,
effectively
distinguishing
from
events
regular
movements.
When
a
is
detected,
activates
green
signal,
triggers
alarm,
sends
notifications
alert
caregivers
or
family
members.
enhance
precision
detection,
employed
machine
deep
learning
classifiers,
including
Random
Forest
(RF),
XGBoost,
Gated
Recurrent
Units
(GRUs),
Logistic
Regression
(LGR),
K-Nearest
Neighbors
(KNN),
analyze
collected
dataset.
Results
show
algorithm
achieves
43%
accuracy
rate,
GRUs
exhibit
44%
XGBoost
33%
rate.
Remarkably,
KNN
outperforms
others
exceptional
rate
99%.
This
research
aims
propose
efficient
framework
significantly
contributes
enhancing
safety
overall
independently
individuals.
It
aligns
principles
sustainability
applications.
Язык: Английский
An Interdisciplinary Overview on Ambient Assisted Living Systems for Health Monitoring at Home: Trade-Offs and Challenges
Sensors,
Год журнала:
2025,
Номер
25(3), С. 853 - 853
Опубликована: Янв. 30, 2025
The
integration
of
IoT
and
Ambient
Assisted
Living
(AAL)
enables
discreet
real-time
health
monitoring
in
home
environments,
offering
significant
potential
for
personalized
preventative
care.
However,
challenges
persist
balancing
privacy,
cost,
usability,
system
reliability.
This
paper
provides
an
overview
recent
advancements
sensor
technologies
assisted
living,
with
a
focus
on
elderly
individuals
living
independently.
It
categorizes
types
that
enhance
healthcare
delivery
explores
interdisciplinary
framework
encompassing
sensing,
communication,
decision-making
systems.
Through
this
analysis,
highlights
current
applications,
identifies
emerging
challenges,
pinpoints
critical
areas
future
research.
aims
to
inform
ongoing
discourse
advocate
approaches
design
address
existing
trade-offs
optimize
performance.
Язык: Английский
IoT Health Guardian: Seamless Monitoring, Smart Medication, and Fall Detection
Lecture notes in electrical engineering,
Год журнала:
2025,
Номер
unknown, С. 381 - 391
Опубликована: Янв. 1, 2025
Язык: Английский
The Effect of using Dimensionality Reduction Compared with Type of Algorithm on Detecting Patient Fall: Triage Case Study
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Фев. 21, 2024
Abstract
Falling
is
one
of
the
most
critical
outcomes
loss
consciousness
during
triage
in
emergency
department
(ED).
It
an
important
sign
requires
immediate
medical
intervention.
This
paper
presents
a
computer
vision-based
fall
detection
model
ED.
In
this
study,
we
hypothesis
that
proposed
provides
accuracy
equal
to
traditional
system
(TTS)
conducted
by
nursing
team.
Thus,
build
model,
use
MoveNet,
pose
estimation
can
identify
joints
related
falls,
consisting
17
key
points.
To
test
hypothesis,
two
experiments:
deep
learning
(DL)
used
complete
feature
keypoints
which
was
passed
and
built
using
Artificial
Neural
Network
(ANN).
second
dimensionality
reduction
Feature-Reduction
for
Fall
(FRF),
Random
Forest
(RF)
selection
analysis
filter
points
classifier.
We
tested
performance
models
dataset
many
images
real-world
scenarios
classified
into
classes:
Not
fall.
split
80%
training
20%
validation.
The
these
experiments
were
trained
obtain
results
compare
them
with
reference
model.
effectiveness
t-test
performed
evaluate
null
both
experiments.
show
FRF
outperforms
DL
has
same
Accuracy
TTS.
Язык: Английский
Loss of consciousness detection model for smart triage
Journal of Infrastructure Policy and Development,
Год журнала:
2024,
Номер
8(8), С. 2687 - 2687
Опубликована: Авг. 14, 2024
Falling
is
one
of
the
most
critical
outcomes
loss
consciousness
during
triage
in
emergency
department
(ED).
It
an
important
sign
requires
immediate
medical
intervention.
This
paper
presents
a
computer
vision-based
fall
detection
model
ED.
In
this
study,
we
hypothesis
that
proposed
provides
accuracy
equal
to
traditional
system
(TTS)
conducted
by
nursing
team.
Thus,
build
model,
use
MoveNet,
pose
estimation
can
identify
joints
related
falls,
consisting
17
key
points.
To
test
hypothesis,
two
experiments:
deep
learning
(DL)
used
complete
feature
keypoints
which
was
passed
and
built
using
Artificial
Neural
Network
(ANN).
second
dimensionality
reduction
Feature-Reduction
for
Fall
(FRF),
Random
Forest
(RF)
selection
analysis
filter
points
classifier.
We
tested
performance
models
dataset
many
images
real-world
scenarios
classified
into
classes:
Not
fall.
split
80%
training
20%
validation.
The
these
experiments
were
trained
obtain
results
compare
them
with
reference
model.
effectiveness
t-test
performed
evaluate
null
both
experiments.
show
FRF
outperforms
DL
has
same
TTS.
Язык: Английский