Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 31, 2025
Human
Activity
Recognition
(HAR)
using
wearable
sensors
has
prompted
substantial
interest
in
recent
years
due
to
the
availability
and
low
cost
of
Inertial
Measurement
Units
(IMUs).
HAR
IMUs
can
aid
both
ergonomic
evaluation
performed
activities
and,
more
recently,
with
development
exoskeleton
technologies,
assist
selection
precisely
tailored
assisting
strategies.
However,
there
needs
be
research
regarding
identification
diverse
lifting
styles,
which
requires
appropriate
datasets
proper
hyperparameters
for
employed
classification
algorithms.
This
paper
offers
insight
into
effect
sensor
placement,
number
sensors,
time
window,
classifier
complexity,
IMU
data
types
used
styles.
The
analyzed
classifiers
are
feedforward
neural
networks,
1-D
convolutional
recurrent
standard
architectures
series
but
offer
different
capabilities
computational
complexity.
is
utmost
importance
when
inference
expected
occur
an
embedded
platform
such
as
occupational
exoskeleton.
It
shown
that
accurate
style
detection
multiple
sufficiently
long
windows,
able
leverage
temporal
nature
since
differences
subtle
from
a
kinematic
point
view
significantly
impact
possibility
injuries.
Journal of Back and Musculoskeletal Rehabilitation,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 19, 2025
Background
Elderly
people
as
age
increases
often
struggle
with
weight
lifting
in
their
daily
lives
due
to
decreased
muscle
strength
and
endurance.
This
limits
ability
perform
routine
tasks,
which
affects
independence
quality
of
life.
Objective
The
aim
this
study
is
evaluate
predict
the
effectiveness
developed
upper
limb
Exo-skeleton
for
lifting,
using
ergonomic
analysis
a
weighted
K-Nearest
Neighbors
(KNN)
machine
learning
algorithm.
Methods
Experiments
were
conducted
measure
Maximum
Voluntary
Isometric
Contraction
(MVIC)
Mean
Power
Frequency
(MPF)
values
assess
before
after
wearing
device
on
elderly
subjects.
Results
results
%MVIC
value
muscles
when
no
load
assistive
lies
between
2%
6%,
whereas
while
adding
5
kg
hand,
MVIC
25%
40%,
15
load,
slightly
increased
30%
71%.
indicated
that
fatigue
Biceps
Brachii
(BB)
flexor
carpi
radialis
(FCR)
are
during
without
Exo-skeleton,
usage
significantly
reduces
fatigue.
Conclusion
demonstrated
exoskeleton
range
weight,
indicating
biceps
Exo-skeleton.
K
nearest
neighboring
algorithm
predicts
new
nerve
disordered
subject,
whether
suitable
or
not
based
his
Body
Mass
Index
(BMI)
fatigueless.
suggested
proposed
compensates
muscular
potentially
guiding
development
user-friendly
devices
elderly.
highlights
significance
studies
AI
algorithms
enhancing
design
functionality.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 29, 2025
Back
exoskeletons
are
gaining
attention
for
preventing
occupational
back
injuries,
but
they
can
disrupt
movement,
a
burden
that
risks
abandonment.
Enhanced
adaptability
is
proposed
to
mitigate
burdens,
perceptual
benefits
less
known.
This
study
investigates
the
and
biomechanical
impacts
of
SLACK
suit
(non-assistive)
controller
versus
three
controllers
with
varying
adaptability:
Weight-Direction-Angle
adaptive
(WDA-ADPT)
scales
assistance
based
on
weight
boxes
using
chest-mounted
camera
machine
learning
algorithm,
movement
direction,
trunk
flexion
angle,
standard
Direction-Angle
(DA-ADPT)
Angle
(A-ADPT)
controllers.
Fifteen
participants
performed
variable
(2,
8,
14
kg)
box-transfer
task.
WDA-ADPT
achieved
highest
score
(88%)
across
survey
categories
reduced
peak
extensor
(BE)
muscle
amplitudes
by
10.1%.
DA-ADPT
had
slightly
lower
(76%)
BE
reduction
(8.5%).
A-ADPT
induced
hip
restriction,
which
could
explain
lowest
(55%)
despite
providing
largest
reductions
in
activity
(17.3%).
Reduced
scores
DA
were
explained
too
much
or
little
actual
task
demands.
These
findings
underscore
scaling
demands
improves
perception
device's
suitability.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 31, 2025
Human
Activity
Recognition
(HAR)
using
wearable
sensors
has
prompted
substantial
interest
in
recent
years
due
to
the
availability
and
low
cost
of
Inertial
Measurement
Units
(IMUs).
HAR
IMUs
can
aid
both
ergonomic
evaluation
performed
activities
and,
more
recently,
with
development
exoskeleton
technologies,
assist
selection
precisely
tailored
assisting
strategies.
However,
there
needs
be
research
regarding
identification
diverse
lifting
styles,
which
requires
appropriate
datasets
proper
hyperparameters
for
employed
classification
algorithms.
This
paper
offers
insight
into
effect
sensor
placement,
number
sensors,
time
window,
classifier
complexity,
IMU
data
types
used
styles.
The
analyzed
classifiers
are
feedforward
neural
networks,
1-D
convolutional
recurrent
standard
architectures
series
but
offer
different
capabilities
computational
complexity.
is
utmost
importance
when
inference
expected
occur
an
embedded
platform
such
as
occupational
exoskeleton.
It
shown
that
accurate
style
detection
multiple
sufficiently
long
windows,
able
leverage
temporal
nature
since
differences
subtle
from
a
kinematic
point
view
significantly
impact
possibility
injuries.
