International Physical Medicine & Rehabilitation Journal,
Год журнала:
2023,
Номер
8(2), С. 135 - 140
Опубликована: Июнь 29, 2023
The
development
of
artificially
intelligent
technological
machine
systems
that
can
integrate
large
volumes
data,
and
also
‘learn’
to
recognize
notable
patterns,
are
currently
being
widely
discussed
employed
in
various
health
other
realms.
In
this
regard,
what
promise
do
these
hold
for
ameliorating
the
late
life
chronic
disease
burden
increasing
numbers
adults
globally
may
stem
from
one
or
multiple
longstanding
conditions.
To
explore
issue,
a
broad
exploration
rehabilitation
associated
artificial
intelligence
implications
was
conducted
using
leading
data
bases.
Results
show
there
some
active
advances
both
learning
realms,
but
not
context
desirable
robust
observations
all
cases.
Much
future
work
is
indicated
though
strongly
recommended.
ACS Nano,
Год журнала:
2025,
Номер
19(5), С. 5613 - 5628
Опубликована: Янв. 31, 2025
Sensory
rehabilitation
in
pediatric
patients
with
traumatic
spinal
cord
injury
is
challenging
due
to
the
ongoing
development
of
their
nervous
systems.
However,
these
sensory
problems
often
result
nonuse
impaired
limb,
which
disturbs
limb
and
leads
overuse
contralateral
other
physical
or
psychological
issues
that
may
persist.
Here,
we
introduce
a
soft
nanomembrane
sensor-enabled
wearable
glove
system
wirelessly
delivers
haptic
sensation
from
hand
tactile
feedback
responses
for
impairment
assistance.
The
smart
uses
gold
nanomembranes,
copper-elastomer
composites,
laser-induced
graphene
sensitive
detection
pressure,
temperature,
strain
changes.
nanomaterial
sensors
are
integrated
low-profile
actuators
wireless
flexible
electronics
offer
real-time
feedback.
system's
thin-film
demonstrate
98%
97%
accuracy
detecting
pressure
finger
flexion,
respectively,
along
coverage
real-life
temperature
changes
as
an
effective
tool.
Collectively,
upper-limb
assistance
embodies
latest
materials
technology
incorporate
miniaturized
maximize
its
compatibility
human
users,
offering
promising
solution
patient
rehabilitation.
Actuators,
Год журнала:
2025,
Номер
14(1), С. 16 - 16
Опубликована: Янв. 6, 2025
The
integration
of
neural
networks
into
robotic
exoskeletons
for
physical
rehabilitation
has
become
popular
due
to
their
ability
interpret
complex
physiological
signals.
Surface
electromyography
(sEMG),
(EMG),
electroencephalography
(EEG),
and
other
signals
enable
communication
between
the
human
body
systems.
Utilizing
communicating
with
robots
plays
a
crucial
role
in
robot-assisted
neurorehabilitation.
This
systematic
review
synthesizes
44
peer-reviewed
studies,
exploring
how
can
improve
exoskeleton
individuals
impaired
upper
limbs.
By
categorizing
studies
based
on
joints,
sensor
systems,
control
methodologies,
we
offer
comprehensive
overview
network
applications
this
field.
Our
findings
demonstrate
that
networks,
such
as
Convolutional
Neural
Networks
(CNNs),
Long
Short-Term
Memory
(LSTM),
Radial
Basis
Function
(RBFNNs),
forms
significantly
contribute
patient-specific
by
enabling
adaptive
learning
personalized
therapy.
CNNs
motion
intention
estimation
accuracy,
while
LSTM
capture
temporal
muscle
activity
patterns
real-time
rehabilitation.
RBFNNs
human–robot
interaction
adapting
individual
movement
patterns,
leading
more
efficient
highlights
potential
revolutionize
limb
rehabilitation,
improving
motor
recovery
patient
outcomes
both
clinical
home-based
settings.
It
also
recommends
future
direction
customizing
existing
applications.
International Journal of Environmental Research and Public Health,
Год журнала:
2022,
Номер
19(19), С. 11907 - 11907
Опубликована: Сен. 21, 2022
Artificial
intelligence
(AI)
is
a
discipline
that
studies
whether
and
how
intelligent
computer
systems
can
simulate
the
capacity
behaviour
of
human
thought
be
created
[...]
Healthcare,
Год журнала:
2023,
Номер
11(3), С. 326 - 326
Опубликована: Янв. 21, 2023
The
increase
in
the
number
of
elderly
patients
with
degenerative
diseases
has
brought
additional
medical
and
financial
pressures,
which
are
adding
to
burden
on
society.
development
sports
rehabilitation
robotics
(SRR)
is
becoming
increasingly
sophisticated
at
technical
level
its
application;
however,
few
studies
have
analyzed
how
it
works
effective
aiding
rehabilitation,
fewer
individualized
exercise
programs
been
developed
for
patients.
purpose
this
study
was
analyze
working
methods
effects
different
types
SRR
then
suggest
feasibility
applying
enhance
physical
abilities
diseases.
researcher’s
team
searched
633
English-language
journal
articles,
had
published
over
past
five
years,
they
selected
38
them
a
narrative
literature
review.
Our
summary
found
following:
(1)
current
generally
classified
as
end-effector
robots,
smart
walkers,
intelligent
robotic
rollators,
exoskeleton
robots—exoskeleton
robots
were
be
most
widely
used.
(2)
include
assistant
tools
main
intermediaries—i.e.,
assist
participate;
dominate
sensors
myoelectric-driven
promote
patient
participation.
(3)
Better
recovery
perceived
when
using
than
achieved
through
traditional
single-movement
methods,
especially
strength,
balance,
endurance,
coordination.
However,
there
no
significant
improvement
their
speed
or
agility
after
SRR.
Robotics,
Год журнала:
2024,
Номер
13(3), С. 50 - 50
Опубликована: Март 15, 2024
Stroke,
the
third
leading
cause
of
global
disability,
poses
significant
challenges
to
healthcare
systems
worldwide.
Addressing
restoration
impaired
hand
functions
is
crucial,
especially
amid
workforce
shortages.
While
robotic-assisted
therapy
shows
promise,
cost
and
community
concerns
hinder
adoption
exoskeletons.
However,
recent
advancements
in
soft
robotics
digital
fabrication,
particularly
3D
printing,
have
sparked
renewed
interest
this
area.
This
review
article
offers
a
thorough
exploration
current
landscape
exoskeletons,
emphasizing
alternative
designs.
It
surveys
previous
reviews
field
examines
relevant
aspects
anatomy
pertinent
wearable
rehabilitation
devices.
Furthermore,
investigates
design
requirements
for
exoskeletons
provides
detailed
various
exoskeleton
gloves,
categorized
based
on
their
principles.
The
discussion
encompasses
simulation-supported
methods,
affordability
considerations,
future
research
directions.
aims
benefit
researchers,
clinicians,
stakeholders
by
disseminating
latest
advances
technology,
ultimately
enhancing
stroke
outcomes
patient
care.
IEEE Transactions on Medical Robotics and Bionics,
Год журнала:
2023,
Номер
5(1), С. 120 - 132
Опубликована: Янв. 18, 2023
With
the
advancement
in
computing
and
robotics,
it
is
necessary
to
develop
fluent
intuitive
methods
for
interacting
with
digital
systems,
augmented/virtual
reality
(AR/VR)
interfaces,
physical
robotic
systems.
Hand
motion
recognition
widely
used
enable
these
interactions.
configuration
classification
metacarpophalangeal
(MCP)
joint
angle
detection
important
a
comprehensive
reconstruction
of
hand
motion.
Surface
electromyography
(sEMG)
other
technologies
have
been
motions.
Forearm
ultrasound
images
provide
musculoskeletal
visualization
that
can
be
understand
Recent
work
has
shown
classified
using
machine
learning
estimate
discrete
configurations.
Estimating
both
MCP
angles
based
on
forearm
not
addressed
literature.
In
this
paper,
we
propose
convolutional
neural
network
(CNN)
deep
pipeline
predicting
angles.
The
results
were
compared
by
different
algorithms.
Support
vector
classifier
kernels,
multi-layer
perceptron,
proposed
CNN
classify
into
11
configurations
activities
daily
living.
acquired
from
6
subjects
instructed
move
their
hands
according
predefined
Motion
capture
data
was
get
finger
corresponding
movements
at
speeds
(0.5
Hz,
1
&
2
Hz)
index,
middle,
ring,
pinky
fingers.
Average
accuracy
82.7
±
9.7%
over
80%
SVC
kernels
observed
subset
dataset.
An
average
RMSE
$7.35^{\circ
}\pm
1.3$
°
obtained
between
predicted
true
A
low
latency
(6.25
-
9.1
estimating
aimed
real-time
control
human-machine
interfaces.
Machines,
Год журнала:
2024,
Номер
12(5), С. 315 - 315
Опубликована: Май 3, 2024
Various
conditions,
including
traffic
accidents,
sports
injuries,
and
neurological
disorders,
can
impair
human
wrist
movements,
underscoring
the
importance
of
effective
rehabilitation
methods.
Robotic
devices
play
a
crucial
role
in
this
regard,
particularly
rehabilitation,
given
complexity
joint,
which
encompasses
three
degrees
freedom:
flexion/extension,
pronation/supination,
radial/ulnar
deviation.
This
paper
provides
comprehensive
review
devices,
employing
methodological
approach
based
on
primary
articles
sourced
from
PubMed,
ScienceDirect,
Scopus,
IEEE,
using
keywords
“wrist
robot”
2007
onwards.
The
findings
highlight
diverse
array
systematically
organized
tabular
format
for
enhanced
comprehension.
Serving
as
valuable
resource
researchers,
enables
comparative
analyses
robotic
across
various
attributes,
offering
insights
into
future
advancements.
Particularly
noteworthy
is
integration
serious
games
with
simplified
signaling
promising
avenue
enhancing
outcomes.
These
lay
groundwork
development
new
or
to
make
improvements
existing
prototypes
incorporating
forward-looking
improve
Frontiers in Computational Neuroscience,
Год журнала:
2025,
Номер
19
Опубликована: Май 2, 2025
Human-machine
interaction
and
computational
neuroscience
have
brought
unprecedented
application
prospects
to
the
field
of
medical
rehabilitation,
especially
for
elderly
population,
where
decline
recovery
hand
function
become
a
significant
concern.
Responding
special
needs
under
context
normalized
epidemic
prevention
control
aging
trend
this
research
proposes
method
based
on
3D
deep
learning
model
process
laser
sensor
point
cloud
data,
aiming
achieve
non-contact
gesture
surface
feature
analysis
in
intelligent
rehabilitation
human-machine
functions.
By
integrating
key
technologies
such
as
collection
clouds,
local
extraction,
abstraction
enhancement
dimensional
information,
has
constructed
an
accurate
system.
In
terms
experimental
results,
validated
superior
performance
proposed
recognizing
with
average
accuracy
88.72%.
The
findings
are
importance
promoting
development
technology
functions
enhancing
safe
comfortable
methods
patients.
