Journal of Information Technology in Construction,
Год журнала:
2024,
Номер
29, С. 1083 - 1102
Опубликована: Дек. 26, 2024
Exoskeletons
are
increasingly
recognized
as
ergonomic
solutions
for
work-related
musculoskeletal
disorders
in
the
construction
industry.
However,
users
of
active
back-support
exoskeletons
susceptible
to
various
physical
and
psychological
risks,
which
could
be
exoskeleton-type
or
task-dependent.
A
test
bed
is
needed
enable
deployment
assessment
risks
associated
with
exoskeleton
use
tasks.
This
study
presents
a
human-in-the-loop
digital
twin
framework
assessing
using
work.
Through
literature
review,
system
architecture
was
developed.
Semi-structured
interviews
were
conducted
identify
tasks
that
most
suitable
exoskeletons.
Based
on
identified
tasks,
laboratory
experiment
quantify
commercially
available
carpentry
framing
The
efficacy
demonstrated
an
example
classification
exertion
levels
due
1D-convolutional
neural
network.
results
show
performance
model
improved
significantly
synthetic
data.
dashboard
provides
visualization
risk
outcomes
aid
decision-making.
highlights
potential
twins
assessment,
allowing
stakeholders
proactively
address
optimize
sets
precedent
future
research
monitor
construction.
Such
efforts
enhance
sustainability
workplaces.
Journal of Information Technology in Construction,
Год журнала:
2025,
Номер
30, С. 1 - 21
Опубликована: Янв. 8, 2025
Exoskeletons
are
gaining
attention
as
a
potential
solution
for
addressing
back
injury
in
the
construction
industry.
However,
using
active
back-support
exoskeletons
can
trigger
unintended
consequences
which
could
increase
mental
workload
of
workers.
Prolonged
impact
workers’
wellbeing
and
productivity.
Predicting
during
exoskeleton
use
inform
strategies
to
mitigate
triggers.
This
study
investigates
two
machine-learning
frameworks
predicting
an
work.
Laboratory
experiments
were
conducted
wherein
electroencephalography
(EEG)
data
was
collected
from
participants
wearing
perform
flooring
tasks.
The
EEG
underwent
preprocessing,
including
band
filtering,
notch
independent
component
analysis,
remove
artifacts
ensure
quality.
A
regression-based
Long
Short-Term
Memory
(LSTM)
network
hybrid
model
convolutional
neural
LSTM
trained
forecast
future
time
steps
processed
data.
performance
networks
evaluated
root
mean
square
error
r-squared.
An
average
0.162
r-squared
0.939
indicate
that
has
better
predictive
power
across
all
channels.
Results
comparison
between
actual
predicted
also
show
about
75%
variance
is
captured
workload.
enhances
understanding
results
highlight
effectiveness
various
methods
identifying
key
features,
offering
guidance
algorithm
selection
applications.
Additionally,
identifies
most
suitable
brain
channels
assessing
use,
aiding
development
devices
optimize
cost-effectiveness,
explanatory
power,
minimal
provides
valuable
insights
stakeholders
understand
while
discovering
opportunities
mitigation.
Smart and Sustainable Built Environment,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 7, 2025
Purpose
Exoskeletons
have
the
potential
to
alleviate
musculoskeletal
disorders
(MSDs),
increase
productivity
and
ultimately
reduce
construction
project
costs,
but
concerns
about
their
ethical,
social
psychological
risks
for
industry
are
unknown.
This
paper
investigates
these
implications
exoskeleton
acceptance.
Design/methodology/approach
Participants
performed
masonry
tasks
without
an
with
active
passive
exoskeleton.
Using
descriptive
inferential
statistics,
associated
exoskeletons,
as
well
trust
levels,
were
assessed.
Objective
data
procured
determine
stress
levels
while
subjective
included
ethical
of
exoskeletons.
Findings
The
findings
show
that
lack
informed
consent
procuring
sensitive
health
is
important
consideration
when
using
Regarding
risks,
unequal
access
sharing
costs
major
concerns.
Furthermore,
revealed
statistical
differences
between
exoskeletons
in
terms
certain
risks.
participants
believed
more
than
results
also
a
strong
positive
relationship
levels.
indicated
both
induce
relatively
moderate
enhance
productivity,
compared
no
condition.
Originality/value
study
one
few
empirical
investigations
on
which
can
facilitate
adoption
mitigating
MSDs
industry.
Journal of Engineering Design and Technology,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 27, 2024
Purpose
This
study
aims
to
investigate
the
impact
of
active
back-support
exoskeletons
on
muscle
activity
and
range
motion
during
carpentry
tasks,
focusing
addressing
back
disorders
in
construction
sector.
The
purpose
is
understand
how
can
serve
as
ergonomic
solutions
industry.
Design/methodology/approach
Sixteen
participants
engaged
simulated
framing
tasks
under
“no-exoskeleton”
“active-exoskeleton”.
measured
such
measuring,
assembly,
moving,
lifting,
installing
nailing.
experimental
design
was
chosen
assess
effectiveness
exoskeleton
different
scenarios.
Findings
results
indicate
that
reduced
back’s
by
3%–26%
various
suggesting
its
movement
limitations.
Additionally,
led
most
muscles,
with
task-specific
variations.
There
an
increase
1–35%,
measuring
assembly
revealing
nuanced
effects.
Research
limitations/implications
findings
may
be
task-specific,
however,
absence
a
consistent
correlation
between
suggests
potential
complexities
warrant
further
investigation.
Originality/value
research
contributes
understanding
construction,
emphasizing
designs
are
crucial
for
unique
work
requirements.
provides
valuable
data
diverse
effects
tasks.
