hello everyone today i want to talk
about our work
CARIN which stands for wireless CSI
based driver activity recognition
under the interference of passengers
as we know the CSI-based Wi-Fi sensing
is a very popular choice
in many application areas and it's a very
promising research field. for example, for the
in-room applications
it can be used for fall
detection breath and heart rate tracking
and even interactive gaming. For
in-car
scenarios it can be used for
driver behavior recognition, fatigue
detection
etc but there's a large
limitation for the wi-fi based sensing
work
which is the multi-people scenario when
there's a many people in the same
environment and they perform activity
together.
it may disturb the CSI pattern and the
mismatch between the observed CSI
signal pattern and the learned CSI
pattern stored in classifier.
after that, it will make a
misclassification
and further reduce the
recognition performance for such system
so in this work
we propose CARIN which can recognize
driver activity for the in-car scenario
under the interference of passengers
In CARIN we leverage a
combination-based solution
we profiles all possible
activities by using cartesian product.
for example if you have five activity
types predefined in activity set,
in CARIN, it will profile it
to a total of 25 activity combinations
stand for every possible activity
combinations.
you may think that the combination based
solution is very trivial and brutal
-force
but there are two main challenges two
major challenges
first one is the synchronization issue,
because not all activities start at the
same time and the
time difference between two different
activities can be any
continuous number. That will lead to
infinite number of activity combinations
but for the in-car scenario, this
this problem is not very severe because
the duration and the moving-scale
for in-car activities
is rather small. More importantly, the
second
challenge is the combination explosion
because with the number of passenger
increases we need to profile all
possible activity conditions
so the computation complexity will
grow exponentially
so in CARIN we need to design a
combination reduction algorithm
to solve this problem.
To design this combination reduction
algorithm
we made a key observation in CARIN
which is the CSI signal from two separate
activities
can be added together to simulate
a real-world activity combination
for example if you look at the first
column, represent the driver only activity
and the second column, which represents the
passenger only
activity. if we add the two spectrums
together, we can get a simulated activity
combination
shows in the third
column,
which are very similar to the real-world
activity combination
spectrum showing in the last column.
based on this
based on this observation, we can design
our combination reduction algorithm
first step, we can calculate when we get
an
online observation CSI signal, we can
calculate
the average doppler shift for this
observed signal.
After that, for every possible activity
combinations
we can sum up the frenquency shift for the
single activities,
which is stored in our offline database.
After that, we can compare the
online observed CSI signal (its average
doppler shift)
and the offline simulated
doppler shift by the single activities
and calculate the distance between the
two activity combinations.
After that, we can eliminate those
combinations with the distance
larger than threshold, and in the
classifier we only load those
unfiltered activity combinations and
neglect those filtered out ones,
so that in the classifier, the
computation overhead can be reduced.
In our experiment we use a real
car
toyota car for experiment, and
establish one communication link inside
the car.
For target activities we investigate
eight driver activities together with
seven front passenger activities
and three backseat passenger activities in
total
for the experiment.
in CARIN we mainly
compare CARIN with three baselines:
The first one is WiMU,
which is a state-of-the-art multi-people
in-room
human activity recognition system, but it
can only work for large-scale activities
mainly for in-rooms activties. The second
one the third one
are all proposed for the in-car
scenarios,
but not designed for the multi-people
scenario. So it may
perform poorly when there's a passenger
inside the same car
with the driver. The results are shown
here below in the two figures. The first
one shows the recognition accuracy for
CARIN is about 90 percent
90 percent
90 percent for the front passenger
plus driver
scenario, and the second figure shows that
CARIN
also successfully reduces the computation
overhead by about 72 percent.
To conclude in this talk, we
talked about
CARIN, which is a CSI-based recognition
scheme under the interference of
passengers. CARIN aims to solve the
multi-people issue
for the in-car scenario for Wi-Fi
based sensing
scheme and it achieves an accuracy of 90.9%
while reducing the computation overhead
of 72 percent
that's all my talk thanks all for the
attentions.
and you may ask questions after this.