Hello, my name is Paula Lago.
And as you may have
guessed from the title,
I will be talking about activity recognition.
As you may know, activity recognition
has been an active research area
for the last decade because of
its multiple applications in sports,
healthcare and many other industries.
However, despite achieving high
accuracy in laboratory settings,
activity recognition in
the wild still hasn't .
achieved the same levels
of accuracy as in the laboratory.
Although there may be
several reasons for this,
one of them is that in laboratory settings
we have access to multiple sensors that
allow us to measure whole body movement,
whereas in real life users
have only one or two sensors.
In most cases of lower quality
than those found in the laboratory.
Therefore,
we are left in a problem setting where
training data has multiple sensors,
but testing data has only one or two sensors.
In this paper,
we explored the research question: can
we use the additional sensor information
for training even if it won't be
available during the testing phase?
Our idea is to learn a shared
representation space to which both
training and testing data will be mapped.
So from high dimensional data
we use clustering to learn these
shared representation space.
Then single sensor data,
our testing data, will be mapped to this
using a mapping function which we learn
using multiple linear regressions.
Finally,
from the shared representation space,
we train a classification model.
During testing, what we would do
is to use single sensor data and
map it to the shared representation
space and then use the classification
model to output a class label.
We have tested this approach
using the opportunity data set
which contains labels for complex
activities such as grooming,
preparing a sandwich,
preparing coffee,
drinking coffee and cleaning up.
We segment the data in 30 seconds
windows and we use leave one subject out
cross validation for our evaluation.
Our results show that when we use one
single sensor (RUA=right upper arm) for training and testing
and use support vector machine classification,
we get 0.34.
F-Score (average).
But when we use our approach to
use all sensors for training,
we get a 0.54 F score,
which represents an improvement
of 20 percentage points.
Our results show improvements
when using most sensors,
except the right wrist sensor.
We use ideas similar to domain
adaptation and transfer learning
with the following differences.
First,
we do not assume any knowledge about the
attributes that describe an activity.
We use high dimensional data to learn this.
Second,
as we have different dimensions
for training and testing data,
the mapping from test data to the
learned features is not as straightforward,
so we use multiple regressions for this.
And third,
our approach can be adapted to be used
with any feature learning algorithm
or classification algorithms.
So in conclusion,
we have proposed a meta algorithm for
complex activity recognition using
additional information for training.
We show that our proposed method
improves the recognition accuracy
and F score compared to a traditional
single sensor training approach.
Thank you for your attention.