Assessment of active back-support exoskeleton on carpentry framing tasks: Muscle activity, range of motion, discomfort, and exertion
International Journal of Industrial Ergonomics,
Год журнала:
2025,
Номер
107, С. 103716 - 103716
Опубликована: Март 6, 2025
Язык: Английский
Design Models and Performance Analysis for a Novel Shape Memory Alloy-Actuated Wearable Hand Exoskeleton for Rehabilitation
IEEE Robotics and Automation Letters,
Год журнала:
2024,
Номер
9(10), С. 8905 - 8912
Опубликована: Сен. 6, 2024
Язык: Английский
A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry
Ruiqin Ma,
Run-Qing Chen,
Buwen Liang
и другие.
Sensors,
Год журнала:
2024,
Номер
24(23), С. 7826 - 7826
Опубликована: Дек. 7, 2024
Transportation
pressure
poses
a
serious
threat
to
the
health
of
live
sheep
and
quality
their
meat.
So,
edible
Hu
was
chosen
as
research
object
for
meat
sheep.
We
constructed
systematic
biosignal
detecting,
processing,
modeling
method.
The
sensing
performed
with
wearable
sensors
(photoelectric
sensor
infrared
temperature
measurement)
physiological
dynamic
continuous
monitoring
transport
environment
Core
waveform
extraction
modern
spectral
estimation
methods
are
used
determine
strip
out
target
signal
from
it
purpose
accurate
acquisition
key
parameters.
Subsequently,
we
built
qualitative
stress
assessment
method
based
on
external
manifestations
reference
Karolinska
drowsiness
scale
establish
stage
classification
rules
data
in
transportation
Finally,
machine
learning
algorithms
such
Gaussian
Naive
Bayes
(GaussianNB),
Passive-Aggressive
Aggregative
Classifier
(PAC),
Nearest
Centroid
(NC),
K-Nearest
Neighbor
Classification
(KNN),
Random
Forest
(RF),
Support
Vector
(SVC),
Gradient
Boosting
Decision
Tree
(GBDT),
eXtreme
(XGB)
were
established
predict
models
Their
results
compared.
show
that
SVC
GBDT
more
effective
overall
model
accuracy
reached
86.44%
91.53%.
XGB
has
best
results.
state
after
optimization
three
parameters
100%,
90.91%,
93.33%,
94.92%.
final
achieved
improve
reliability,
reduce
risk,
solve
problems
inefficient
supervision
control.
Язык: Английский
Performance Optimizing of Pneumatic soft Robotic Hands using wave-shaped contour actuator
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 103456 - 103456
Опубликована: Дек. 1, 2024
Язык: Английский
Wireless Multi-Channel EMG Monitoring System For Rehabilitation Exercise Assistance
Journal of Flexible and Printed Electronics,
Год журнала:
2024,
Номер
3(2), С. 231 - 239
Опубликована: Дек. 1, 2024
Rehabilitation
exercises
are
essential
for
restoring
motor
function
following
fractures,
ligament
injuries,
and
nerve
damage.
However,
performing
these
correctly
without
professional
supervision
can
be
difficult,
improper
execution
may
slow
recovery
or
even
cause
secondary
injuries.
Surface
electromyography
(sEMG),
which
detects
electrical
signals
generated
by
muscle
contractions
through
the
skin,
offers
a
valuable
measure
of
activation.
In
this
study,
we
propose
wireless
sEMG
monitoring
system
to
support
correct
rehabilitation
exercises.
This
monitors
engagement
ensure
appropriate
activation
levels,
aligned
with
therapeutic
goals
each
exercise.
By
developing
device,
aim
enhance
effectiveness
assist
patients
in
recovering
helping
them
perform
prescribed
accurately.
Язык: Английский