Energy Technology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 19, 2024
The
health
monitoring
of
electricity
transmission
systems
has
been
increasingly
attracting
the
scientific
community's
attention,
especially
those
power
towers
built
in
remote
and
deserted
areas.
smart
grid
provides
a
realistic
solution
to
problem,
but
there
is
problem
that
energy
supplies
are
still
constrained
by
batteries.
This
article
proposes
novel
vibration
harvester
address
supply
challenges
auxiliary
equipment
mounted
on
or
lines.
proposed
not
only
harvests
when
working
also
attenuates
amplitude
line.
structure
device
comprises
three
modules:
module
for
capturing
vibration,
motion
conversion,
management
module.
analytical
response
under
sinusoidal
random
excitation
investigated,
performance
harvesting
effect
tested.
prototype
achieves
maximum
183.96
mW
tested
using
servo
hydraulic
mechanical
testing
sensing
system,
wireless
data
experiment
proves
generation
ability
prototype.
experimental
results
show
acceleration
line
decreases
working.
Soft Science,
Journal Year:
2025,
Volume and Issue:
5(1)
Published: Feb. 14, 2025
Self-powered
sensing
technology
plays
a
key
role
in
autonomous
and
portable
systems,
with
applications
health
monitoring
robotics.
These
sensors,
which
do
not
rely
on
external
power
sources,
offer
stable,
continuous
data
acquisition
for
real-time
complex
interactions.
For
instance,
triboelectric
nanogenerators
have
enabled
self-powered
wearable
sensors
to
monitor
vital
signs
such
as
heart
beat
rate
respiration
by
converting
body
movement
into
electrical
energy,
eliminating
the
need
batteries.
Despite
their
advantages,
challenges
remain
large-scale
manufacturing,
miniaturization,
multifunctional
integration.
Overcoming
these
may
require
innovative
advances
novel
materials,
intelligent
algorithms,
integration
strategies.
This
perspective
summarizes
recent
existing
technologies
robotics
applications,
provides
an
outlook
future
development.
Advanced Science,
Journal Year:
2025,
Volume and Issue:
12(20)
Published: April 25, 2025
Triboelectric
nanogenerators
(TENGs)
are
emerging
as
transformative
technologies
for
sustainable
energy
harvesting
and
precision
sensing,
offering
eco-friendly
power
generation
from
mechanical
motion.
They
harness
while
enabling
self-sustaining
sensing
self-powered
devices.
However,
challenges
such
material
optimization,
fabrication
techniques,
design
strategies,
output
stability
must
be
addressed
to
fully
realize
their
practical
potential.
Artificial
intelligence
(AI),
with
its
capabilities
in
advanced
data
analysis,
pattern
recognition,
adaptive
responses,
is
revolutionizing
fields
like
healthcare,
industrial
automation,
smart
infrastructure.
When
integrated
TENGs,
AI
can
overcome
current
limitations
by
enhancing
output,
stability,
adaptability.
This
review
explores
the
synergistic
potential
of
AI-driven
TENG
systems,
optimizing
materials
embedding
machine
learning
deep
algorithms
intelligent
real-time
sensing.
These
advancements
enable
improved
harvesting,
predictive
maintenance,
dynamic
performance
making
TENGs
more
across
industries.
The
also
identifies
key
future
research
directions,
including
development
low-power
algorithms,
materials,
hybrid
robust
security
protocols
AI-enhanced
solutions.
With
the
increasing
development
of
metaverse
and
human-computer
interaction
(HMI)
technologies,
artificial
intelligence
(AI)
applications
in
virtual
reality
(VR)
environments
are
receiving
significant
attention.
This
study
presents
a
self-sensing
facial
recognition
mask
(FRM)
utilizing
triboelectric
nanogenerators
(TENG)
machine
learning
algorithms
to
enhance
user
immersion
interaction.
Various
TENG
negative
electrode
materials
evaluated
improve
sensor
performance,
efficacy
single
is
confirmed.
For
accurate
movement
emotion
detection,
different
assessed,
leading
selection
an
advanced
data
processing
method
with
two-layer
long
short-term
memory
model,
which
achieves
99.87%
accuracy.
The
practical
FRM
system
reality,
including
psychotherapy
HMI
scenarios,
validated
through
mathematical
models.
Additionally,
digital
twin-based
monitoring
platform
developed
using
5G,
database,
visualization
technologies
oversee
status.
Overall,
these
innovative
approaches
overcome
limitations
existing
face
environmental
interference
high
cost,
compared
other
technologies.
Small,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 6, 2025
Abstract
Reliable
human
motion
monitoring
is
crucial
across
various
fields
such
as
sports,
healthcare,
and
metaverse.
This
study
introduces
an
AI‐assisted
wearable
hip
joint
energy
harvester
(HJEH)
designed
to
convert
mechanical
from
movements
into
electric
power
for
devices
while
simultaneously
motion.
The
HJEH
utilizes
electromagnetic
generator
(EMG)
in
conjunction
with
a
freestanding
triboelectric
nanogenerator
(FS‐TENG)
achieve
harvesting
sensing.
EMG
specifically
recovers
the
negative
generate
electricity,
flywheel
acceleration
gears
employ
enhance
output
power.
Concurrently,
FS‐TENG
generates
signals
driven
by
motions,
which
are
processed
using
deep
learning
algorithms
accurate
detection.
performance
of
thoroughly
evaluated
through
bench
tests,
treadmill
outdoor
experiments.
achieves
peak
357
mW
maximum
gravitational
density
1.67
W
kg
−1
during
running
at
speed
8
km
h
.
demonstrates
remarkable
accuracy
99.95%
identifying
12
different
types
motion,
validating
efficacy
integrated
system.
In
particular,
incorporating
digital
twin
technology
realizes
safety
elderly.
