Can heart rate sequences from wearable devices predict day-long mental states in higher education students: a signal processing and machine learning case study at a UK university
Brain Informatics,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: Dec. 1, 2024
Abstract
The
mental
health
of
students
in
higher
education
has
been
a
growing
concern,
with
increasing
evidence
pointing
to
heightened
risks
developing
condition.
This
research
aims
explore
whether
day-long
heart
rate
sequences,
collected
continuously
through
Apple
Watch
an
open
environment
without
restrictions
on
daily
routines,
can
effectively
indicate
states,
particularly
stress
for
university
students.
While
(HR)
is
commonly
used
monitor
physical
activity
or
responses
isolated
stimuli
controlled
setting,
such
as
stress-inducing
tests,
this
study
addresses
the
gap
by
analyzing
fluctuations
throughout
day,
examining
their
potential
gauge
overall
levels
more
comprehensive
and
real-world
context.
data
was
at
public
UK.
Using
signal
processing,
both
original
sequences
representations,
via
Fourier
transformation
wavelet
analysis,
have
modeled
using
advanced
machine
learning
algorithms.
Having
achieving
statistically
significant
results
over
baseline,
provides
understanding
how
alone
may
be
characterize
states
processing
learning,
system
poised
further
testing
ongoing
collection
continues.
Language: Английский
The Role of Wearable Devices on Students
Advances in educational technologies and instructional design book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 81 - 114
Published: Dec. 24, 2024
Abstract:
This
chapter
of
the
book
explains
significant
role
existing
wearable
devices
on
students.
Students
are
basically
classified
based
what
they
placed
into
such
as
academics,
sports,
or
challenged.
Then
under
upon
their
age
and
class
in
which
studying
per
World
Health
Organization
Standards.
And
later
sensors
used
for
measuring
various
health
like
EEG,
Heart
rate,
etc
psycho
-physiological
parameters
stress,
Sleeplessness,
autism
investigated.
The
made
by
biosensors
attached
to
students
at
different
places
body
that
interested
be
measured.
Theses
monitoring
student's
activities
using
recent
developed
software
algorithms
including
machine
learning
Artificial
Intelligent
algorithms.
Some
case
studies
also
presented.
Language: Английский